Search is not available for this dataset
modelId
stringlengths 5
138
| author
stringlengths 2
42
| last_modified
unknowndate 2020-02-15 11:33:14
2025-05-05 12:28:14
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int64 0
223M
| likes
int64 0
11.7k
| library_name
stringclasses 447
values | tags
sequencelengths 1
4.05k
| pipeline_tag
stringclasses 54
values | createdAt
unknowndate 2022-03-02 23:29:04
2025-05-05 12:27:48
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joboffer/e2a19629-db7f-4809-8967-5c056ddd2422 | joboffer | "2025-05-02T14:51:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"base_model:adapter:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"license:llama3",
"4-bit",
"bitsandbytes",
"region:us"
] | null | "2025-05-02T14:42:17Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
sophieschneider216/lora | sophieschneider216 | "2025-05-02T14:41:17Z" | 0 | 0 | null | [
"region:us"
] | null | "2023-06-27T13:21:13Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
yuiyb/cloud-emulator | yuiyb | "2025-05-02T14:39:05Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T14:30:09Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
ivangrapher/87a55a9c-f880-442f-848e-7b3c99d6866d | ivangrapher | "2025-05-02T14:38:14Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama",
"axolotl",
"generated_from_trainer",
"base_model:unsloth/SmolLM2-1.7B-Instruct",
"base_model:adapter:unsloth/SmolLM2-1.7B-Instruct",
"license:apache-2.0",
"8-bit",
"bitsandbytes",
"region:us"
] | null | "2025-05-02T14:03:42Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
InfoTokenizers/tokenizers | InfoTokenizers | "2025-05-02T14:35:59Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-04-02T15:58:35Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
infogeo/cc85da80-b0ae-428f-adfc-1abcc6806c53 | infogeo | "2025-05-02T14:35:37Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T14:35:37Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
SWY666/Best13_3280_c | SWY666 | "2025-05-02T14:21:53Z" | 0 | 0 | null | [
"safetensors",
"qwen2",
"region:us"
] | null | "2025-05-02T07:01:10Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
no0ne-97/misoginia-bert-spanish-wwm-cased-task2-V1 | no0ne-97 | "2025-05-02T14:09:07Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"bert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-05-02T14:08:45Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
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AnmolPokhriyal/qlora-saul-nyayasathi | AnmolPokhriyal | "2025-05-02T14:03:43Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"generated_from_trainer",
"base_model:Equall/Saul-7B-Instruct-v1",
"base_model:adapter:Equall/Saul-7B-Instruct-v1",
"license:mit",
"region:us"
] | null | "2025-05-02T14:03:39Z" | <!DOCTYPE html>
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Noto Color Emoji;
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width: 6rem;
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color: rgba(107, 114, 128, 1);
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aleegis/818ff924-9934-4692-97b8-587fe8e6e43d | aleegis | "2025-05-02T14:03:11Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"starcoder2",
"axolotl",
"generated_from_trainer",
"base_model:bigcode/starcoder2-3b",
"base_model:adapter:bigcode/starcoder2-3b",
"license:bigcode-openrail-m",
"region:us"
] | null | "2025-05-02T13:22:49Z" | <!DOCTYPE html>
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/>
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Noto Color Emoji;
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img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
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color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
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background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
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alperenenes/vlmr1_grpo_less_rewards | alperenenes | "2025-05-02T13:50:38Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:adapter:Qwen/Qwen2.5-VL-3B-Instruct",
"region:us"
] | null | "2025-05-02T13:40:27Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
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width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
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h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
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p, a {
color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
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testrightnow/6d175cce-c29e-4faf-82d8-cefcae8a4192 | testrightnow | "2025-05-02T13:43:54Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T13:43:39Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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deaca/llm_chatbot | deaca | "2025-05-02T13:04:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T13:04:46Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
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<meta
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/>
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/>
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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AdoCleanCode/soft_real_imagenet_v6 | AdoCleanCode | "2025-05-02T12:48:26Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:openai-community/gpt2",
"base_model:finetune:openai-community/gpt2",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T10:47:22Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
<meta property="fb:app_id" content="1321688464574422" />
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property="og:title"
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<meta property="og:type" content="website" />
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margin: 0;
}
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background-color: white;
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text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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chchen/Llama3-OpenBioLLM-8B-PsyCourse-info-fold1 | chchen | "2025-05-02T12:33:45Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"base_model:aaditya/Llama3-OpenBioLLM-8B",
"base_model:adapter:aaditya/Llama3-OpenBioLLM-8B",
"license:llama3",
"region:us"
] | null | "2025-05-02T11:37:23Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
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background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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alt=""
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<h1>429</h1>
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</div>
</main>
</body>
</html> |
ttdamowang/qwen3-1.7b_medical | ttdamowang | "2025-05-02T12:25:44Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"qwen3",
"text-generation",
"unsloth",
"trl",
"sft",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T11:38:36Z" | ---
library_name: transformers
tags:
- unsloth
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
zelk12/MT2-gemma-3-12B-Q6_K-GGUF | zelk12 | "2025-05-02T12:16:23Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"llama-cpp",
"gguf-my-repo",
"image-text-to-text",
"base_model:zelk12/MT2-gemma-3-12B",
"base_model:quantized:zelk12/MT2-gemma-3-12B",
"license:gemma",
"endpoints_compatible",
"region:us",
"conversational"
] | image-text-to-text | "2025-05-02T12:15:40Z" | ---
base_model: zelk12/MT2-gemma-3-12B
library_name: transformers
license: gemma
pipeline_tag: image-text-to-text
tags:
- mergekit
- merge
- llama-cpp
- gguf-my-repo
---
# zelk12/MT2-gemma-3-12B-Q6_K-GGUF
This model was converted to GGUF format from [`zelk12/MT2-gemma-3-12B`](https://huggingface.co/zelk12/MT2-gemma-3-12B) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/zelk12/MT2-gemma-3-12B) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo zelk12/MT2-gemma-3-12B-Q6_K-GGUF --hf-file mt2-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo zelk12/MT2-gemma-3-12B-Q6_K-GGUF --hf-file mt2-gemma-3-12b-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo zelk12/MT2-gemma-3-12B-Q6_K-GGUF --hf-file mt2-gemma-3-12b-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo zelk12/MT2-gemma-3-12B-Q6_K-GGUF --hf-file mt2-gemma-3-12b-q6_k.gguf -c 2048
```
|
faifaistone/hcw-ci | faifaistone | "2025-05-02T12:08:16Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-05-02T11:20:26Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: HCW
---
# Hcw Ci
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `HCW` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "HCW",
"lora_weights": "https://huggingface.co/faifaistone/hcw-ci/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('faifaistone/hcw-ci', weight_name='lora.safetensors')
image = pipeline('HCW').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 4000
- Learning rate: 0.0004
- LoRA rank: 64
## Contribute your own examples
You can use the [community tab](https://huggingface.co/faifaistone/hcw-ci/discussions) to add images that show off what you’ve made with this LoRA.
|
mergekit-community/mergekit-dare_ties-bqkjlyo | mergekit-community | "2025-05-02T11:32:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:IlyaGusev/saiga_gemma3_12b",
"base_model:finetune:IlyaGusev/saiga_gemma3_12b",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2025-05-02T11:28:00Z" | ---
base_model:
- IlyaGusev/saiga_gemma3_12b
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [IlyaGusev/saiga_gemma3_12b](https://huggingface.co/IlyaGusev/saiga_gemma3_12b) as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: IlyaGusev/saiga_gemma3_12b
#no parameters necessary for base model
- model: IlyaGusev/saiga_gemma3_12b
parameters:
density: 0.5
weight: 0.5
merge_method: dare_ties
base_model: IlyaGusev/saiga_gemma3_12b
parameters:
normalize: true
dtype: bfloat16
```
|
Ahresh-53/GAMGAM | Ahresh-53 | "2025-05-02T11:32:29Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-05-02T11:32:29Z" | ---
license: apache-2.0
---
|
luckycanucky/lora_model_hnd-F16-GGUF | luckycanucky | "2025-05-02T11:01:10Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"llama-cpp",
"gguf-my-lora",
"en",
"base_model:luckycanucky/lora_model_hnd",
"base_model:quantized:luckycanucky/lora_model_hnd",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T11:01:02Z" | ---
base_model: luckycanucky/lora_model_hnd
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- llama-cpp
- gguf-my-lora
license: apache-2.0
language:
- en
---
# luckycanucky/lora_model_hnd-F16-GGUF
This LoRA adapter was converted to GGUF format from [`luckycanucky/lora_model_hnd`](https://huggingface.co/luckycanucky/lora_model_hnd) via the ggml.ai's [GGUF-my-lora](https://huggingface.co/spaces/ggml-org/gguf-my-lora) space.
Refer to the [original adapter repository](https://huggingface.co/luckycanucky/lora_model_hnd) for more details.
## Use with llama.cpp
```bash
# with cli
llama-cli -m base_model.gguf --lora lora_model_hnd-f16.gguf (...other args)
# with server
llama-server -m base_model.gguf --lora lora_model_hnd-f16.gguf (...other args)
```
To know more about LoRA usage with llama.cpp server, refer to the [llama.cpp server documentation](https://github.com/ggerganov/llama.cpp/blob/master/examples/server/README.md).
|
mergekit-community/mergekit-dare_ties-tpraytl | mergekit-community | "2025-05-02T10:43:08Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gemma3",
"image-text-to-text",
"mergekit",
"merge",
"conversational",
"arxiv:2311.03099",
"base_model:soob3123/amoral-gemma3-12B-v2",
"base_model:finetune:soob3123/amoral-gemma3-12B-v2",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | image-text-to-text | "2025-05-02T10:38:46Z" | ---
base_model:
- soob3123/amoral-gemma3-12B-v2
library_name: transformers
tags:
- mergekit
- merge
---
# merge
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
## Merge Details
### Merge Method
This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [soob3123/amoral-gemma3-12B-v2](https://huggingface.co/soob3123/amoral-gemma3-12B-v2) as a base.
### Models Merged
The following models were included in the merge:
### Configuration
The following YAML configuration was used to produce this model:
```yaml
models:
- model: soob3123/amoral-gemma3-12B-v2
#no parameters necessary for base model
- model: soob3123/amoral-gemma3-12B-v2
parameters:
density: 0.5
weight: 0.5
merge_method: dare_ties
base_model: soob3123/amoral-gemma3-12B-v2
parameters:
normalize: true
dtype: bfloat16
```
|
bawin/lora-r64 | bawin | "2025-05-02T10:25:27Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B",
"base_model:finetune:unsloth/Qwen2.5-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T10:25:17Z" | ---
base_model: unsloth/Qwen2.5-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bawin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
SeungWonSeo/baseline | SeungWonSeo | "2025-05-02T08:51:56Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"code",
"codeqwen",
"chat",
"qwen",
"qwen-coder",
"conversational",
"en",
"arxiv:2409.12186",
"arxiv:2309.00071",
"arxiv:2407.10671",
"base_model:Qwen/Qwen2.5-Coder-7B",
"base_model:finetune:Qwen/Qwen2.5-Coder-7B",
"license:apache-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T08:21:31Z" | ---
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen2.5-Coder-7B-Instruct/blob/main/LICENSE
language:
- en
base_model:
- Qwen/Qwen2.5-Coder-7B
pipeline_tag: text-generation
library_name: transformers
tags:
- code
- codeqwen
- chat
- qwen
- qwen-coder
---
# Qwen2.5-Coder-7B-Instruct
<a href="https://chat.qwenlm.ai/" target="_blank" style="margin: 2px;">
<img alt="Chat" src="https://img.shields.io/badge/%F0%9F%92%9C%EF%B8%8F%20Qwen%20Chat%20-536af5" style="display: inline-block; vertical-align: middle;"/>
</a>
## Introduction
Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). As of now, Qwen2.5-Coder has covered six mainstream model sizes, 0.5, 1.5, 3, 7, 14, 32 billion parameters, to meet the needs of different developers. Qwen2.5-Coder brings the following improvements upon CodeQwen1.5:
- Significantly improvements in **code generation**, **code reasoning** and **code fixing**. Base on the strong Qwen2.5, we scale up the training tokens into 5.5 trillion including source code, text-code grounding, Synthetic data, etc. Qwen2.5-Coder-32B has become the current state-of-the-art open-source codeLLM, with its coding abilities matching those of GPT-4o.
- A more comprehensive foundation for real-world applications such as **Code Agents**. Not only enhancing coding capabilities but also maintaining its strengths in mathematics and general competencies.
- **Long-context Support** up to 128K tokens.
**This repo contains the instruction-tuned 7B Qwen2.5-Coder model**, which has the following features:
- Type: Causal Language Models
- Training Stage: Pretraining & Post-training
- Architecture: transformers with RoPE, SwiGLU, RMSNorm, and Attention QKV bias
- Number of Parameters: 7.61B
- Number of Paramaters (Non-Embedding): 6.53B
- Number of Layers: 28
- Number of Attention Heads (GQA): 28 for Q and 4 for KV
- Context Length: Full 131,072 tokens
- Please refer to [this section](#processing-long-texts) for detailed instructions on how to deploy Qwen2.5 for handling long texts.
For more details, please refer to our [blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/), [GitHub](https://github.com/QwenLM/Qwen2.5-Coder), [Documentation](https://qwen.readthedocs.io/en/latest/), [Arxiv](https://arxiv.org/abs/2409.12186).
## Requirements
The code of Qwen2.5-Coder has been in the latest Hugging face `transformers` and we advise you to use the latest version of `transformers`.
With `transformers<4.37.0`, you will encounter the following error:
```
KeyError: 'qwen2'
```
## Quickstart
Here provides a code snippet with `apply_chat_template` to show you how to load the tokenizer and model and how to generate contents.
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "Qwen/Qwen2.5-Coder-7B-Instruct"
model = AutoModelForCausalLM.from_pretrained(
model_name,
torch_dtype="auto",
device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
prompt = "write a quick sort algorithm."
messages = [
{"role": "system", "content": "You are Qwen, created by Alibaba Cloud. You are a helpful assistant."},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
generated_ids = model.generate(
**model_inputs,
max_new_tokens=512
)
generated_ids = [
output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]
response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
```
### Processing Long Texts
The current `config.json` is set for context length up to 32,768 tokens.
To handle extensive inputs exceeding 32,768 tokens, we utilize [YaRN](https://arxiv.org/abs/2309.00071), a technique for enhancing model length extrapolation, ensuring optimal performance on lengthy texts.
For supported frameworks, you could add the following to `config.json` to enable YaRN:
```json
{
...,
"rope_scaling": {
"factor": 4.0,
"original_max_position_embeddings": 32768,
"type": "yarn"
}
}
```
For deployment, we recommend using vLLM.
Please refer to our [Documentation](https://qwen.readthedocs.io/en/latest/deployment/vllm.html) for usage if you are not familar with vLLM.
Presently, vLLM only supports static YARN, which means the scaling factor remains constant regardless of input length, **potentially impacting performance on shorter texts**.
We advise adding the `rope_scaling` configuration only when processing long contexts is required.
## Evaluation & Performance
Detailed evaluation results are reported in this [📑 blog](https://qwenlm.github.io/blog/qwen2.5-coder-family/).
For requirements on GPU memory and the respective throughput, see results [here](https://qwen.readthedocs.io/en/latest/benchmark/speed_benchmark.html).
## Citation
If you find our work helpful, feel free to give us a cite.
```
@article{hui2024qwen2,
title={Qwen2. 5-Coder Technical Report},
author={Hui, Binyuan and Yang, Jian and Cui, Zeyu and Yang, Jiaxi and Liu, Dayiheng and Zhang, Lei and Liu, Tianyu and Zhang, Jiajun and Yu, Bowen and Dang, Kai and others},
journal={arXiv preprint arXiv:2409.12186},
year={2024}
}
@article{qwen2,
title={Qwen2 Technical Report},
author={An Yang and Baosong Yang and Binyuan Hui and Bo Zheng and Bowen Yu and Chang Zhou and Chengpeng Li and Chengyuan Li and Dayiheng Liu and Fei Huang and Guanting Dong and Haoran Wei and Huan Lin and Jialong Tang and Jialin Wang and Jian Yang and Jianhong Tu and Jianwei Zhang and Jianxin Ma and Jin Xu and Jingren Zhou and Jinze Bai and Jinzheng He and Junyang Lin and Kai Dang and Keming Lu and Keqin Chen and Kexin Yang and Mei Li and Mingfeng Xue and Na Ni and Pei Zhang and Peng Wang and Ru Peng and Rui Men and Ruize Gao and Runji Lin and Shijie Wang and Shuai Bai and Sinan Tan and Tianhang Zhu and Tianhao Li and Tianyu Liu and Wenbin Ge and Xiaodong Deng and Xiaohuan Zhou and Xingzhang Ren and Xinyu Zhang and Xipin Wei and Xuancheng Ren and Yang Fan and Yang Yao and Yichang Zhang and Yu Wan and Yunfei Chu and Yuqiong Liu and Zeyu Cui and Zhenru Zhang and Zhihao Fan},
journal={arXiv preprint arXiv:2407.10671},
year={2024}
}
```
|
hanaearg/emo-llama-3-8b-eng-10epochs | hanaearg | "2025-05-02T08:25:41Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/llama-3-8b-bnb-4bit",
"base_model:finetune:unsloth/llama-3-8b-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T08:25:30Z" | ---
base_model: unsloth/llama-3-8b-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** hanaearg
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3-8b-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
VLAI-AIVN/LLama-3.2-3B-Instruct_GRPO | VLAI-AIVN | "2025-05-02T08:10:00Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"arxiv:1910.09700",
"base_model:unsloth/Llama-3.2-3B-Instruct",
"base_model:adapter:unsloth/Llama-3.2-3B-Instruct",
"region:us"
] | null | "2025-05-02T08:03:53Z" | ---
base_model: unsloth/Llama-3.2-3B-Instruct
library_name: peft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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[More Information Needed]
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#### Speeds, Sizes, Times [optional]
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### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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[More Information Needed]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
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[More Information Needed]
## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
[More Information Needed]
### Framework versions
- PEFT 0.15.2 |
bawin/lora-r32 | bawin | "2025-05-02T07:41:02Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"qwen2",
"trl",
"en",
"base_model:unsloth/Qwen2.5-7B",
"base_model:finetune:unsloth/Qwen2.5-7B",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T07:40:54Z" | ---
base_model: unsloth/Qwen2.5-7B
tags:
- text-generation-inference
- transformers
- unsloth
- qwen2
- trl
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** bawin
- **License:** apache-2.0
- **Finetuned from model :** unsloth/Qwen2.5-7B
This qwen2 model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
John6666/uwazumimix-v40-sdxl | John6666 | "2025-05-02T07:22:18Z" | 0 | 0 | diffusers | [
"diffusers",
"safetensors",
"text-to-image",
"stable-diffusion",
"stable-diffusion-xl",
"realistic",
"photorealistic",
"asian",
"Japanese",
"pony",
"en",
"license:other",
"autotrain_compatible",
"endpoints_compatible",
"diffusers:StableDiffusionXLPipeline",
"region:us"
] | text-to-image | "2025-05-02T07:16:34Z" | ---
license: other
license_name: faipl-1.0-sd
license_link: https://freedevproject.org/faipl-1.0-sd/
language:
- en
library_name: diffusers
pipeline_tag: text-to-image
tags:
- text-to-image
- stable-diffusion
- stable-diffusion-xl
- realistic
- photorealistic
- asian
- Japanese
- pony
---
Original model is [here](https://civitai.com/models/832311/uwazumimix?modelVersionId=1732316).
This model created by [UWAZUMI](https://civitai.com/user/UWAZUMI).
|
quacufaizza/zxcvxcv | quacufaizza | "2025-05-02T07:05:46Z" | 0 | 0 | null | [
"license:bigscience-openrail-m",
"region:us"
] | null | "2025-05-02T07:05:46Z" | ---
license: bigscience-openrail-m
---
|
BABYSHARK09/Nq | BABYSHARK09 | "2025-05-02T06:41:38Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T06:29:44Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
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<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
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<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
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[More Information Needed]
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[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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[More Information Needed]
## Model Card Contact
[More Information Needed] |
aadhistii/XLM-Roberta-SDGs-Oplib-Elsevier | aadhistii | "2025-05-02T06:37:58Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"text-classification",
"generated_from_trainer",
"base_model:FacebookAI/xlm-roberta-base",
"base_model:finetune:FacebookAI/xlm-roberta-base",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-05-01T17:47:39Z" | ---
library_name: transformers
license: mit
base_model: FacebookAI/xlm-roberta-base
tags:
- generated_from_trainer
metrics:
- accuracy
model-index:
- name: XLM-Roberta-SDGs-Oplib-Elsevier
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# XLM-Roberta-SDGs-Oplib-Elsevier
This model is a fine-tuned version of [FacebookAI/xlm-roberta-base](https://huggingface.co/FacebookAI/xlm-roberta-base) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1431
- Accuracy: 0.4592
- F1 Micro: 0.8404
- F1 Macro: 0.8122
- Precision Micro: 0.8570
- Precision Macro: 0.8426
- Recall Micro: 0.8245
- Recall Macro: 0.7867
- Roc Auc: 0.8979
- Hamming Loss: 0.0539
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2.1825414528884046e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.04274765185755578
- num_epochs: 20
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Micro | F1 Macro | Precision Micro | Precision Macro | Recall Micro | Recall Macro | Roc Auc | Hamming Loss |
|:-------------:|:-----:|:----:|:---------------:|:--------:|:--------:|:--------:|:---------------:|:---------------:|:------------:|:------------:|:-------:|:------------:|
| 0.4297 | 1.0 | 715 | 0.2359 | 0.2485 | 0.7298 | 0.5206 | 0.7343 | 0.5963 | 0.7252 | 0.5068 | 0.8353 | 0.0925 |
| 0.2361 | 2.0 | 1430 | 0.1700 | 0.3765 | 0.8096 | 0.7204 | 0.7998 | 0.7397 | 0.8197 | 0.7095 | 0.8885 | 0.0664 |
| 0.1507 | 3.0 | 2145 | 0.1534 | 0.4112 | 0.8219 | 0.7506 | 0.8178 | 0.7864 | 0.8260 | 0.7439 | 0.8939 | 0.0617 |
| 0.1294 | 4.0 | 2860 | 0.1422 | 0.4315 | 0.8369 | 0.7933 | 0.8268 | 0.8032 | 0.8471 | 0.7898 | 0.9051 | 0.0569 |
| 0.1016 | 5.0 | 3575 | 0.1359 | 0.4539 | 0.8425 | 0.8066 | 0.8432 | 0.8171 | 0.8417 | 0.8016 | 0.9046 | 0.0542 |
| 0.0881 | 6.0 | 4290 | 0.1362 | 0.4459 | 0.84 | 0.8025 | 0.8309 | 0.8215 | 0.8493 | 0.7902 | 0.9067 | 0.0557 |
| 0.0725 | 7.0 | 5005 | 0.1431 | 0.4592 | 0.8404 | 0.8122 | 0.8570 | 0.8426 | 0.8245 | 0.7867 | 0.8979 | 0.0539 |
### Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu126
- Datasets 3.5.1
- Tokenizers 0.21.1
|
RajeevanL/tamil-xlm-roberta-custom | RajeevanL | "2025-05-02T06:12:39Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"xlm-roberta",
"fill-mask",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | fill-mask | "2025-05-02T06:10:52Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
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## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
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## Model Card Contact
[More Information Needed] |
noneUsername/shisa-v2-unphi4-14b-W8A8 | noneUsername | "2025-05-02T05:29:34Z" | 0 | 0 | null | [
"safetensors",
"llama",
"base_model:shisa-ai/shisa-v2-unphi4-14b",
"base_model:quantized:shisa-ai/shisa-v2-unphi4-14b",
"8-bit",
"compressed-tensors",
"region:us"
] | null | "2025-05-02T05:15:45Z" | ---
base_model:
- shisa-ai/shisa-v2-unphi4-14b
---
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi4-14b,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.916|± |0.0176|
| | |strict-match | 5|exact_match|↑ |0.916|± |0.0176|
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi4-14b,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.928|± |0.0116|
| | |strict-match | 5|exact_match|↑ |0.928|± |0.0116|
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi4-14b,add_bos_token=true,max_model_len=3048,dtype=bfloat16), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: 1
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.7708|± |0.0137|
| - humanities | 2|none | |acc |↑ |0.8205|± |0.0261|
| - other | 2|none | |acc |↑ |0.7590|± |0.0293|
| - social sciences| 2|none | |acc |↑ |0.8167|± |0.0280|
| - stem | 2|none | |acc |↑ |0.7158|± |0.0255|
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi-14b-W8A8-INT8,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.908|± |0.0183|
| | |strict-match | 5|exact_match|↑ |0.908|± |0.0183|
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi-14b-W8A8-INT8,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.916|± |0.0124|
| | |strict-match | 5|exact_match|↑ |0.916|± |0.0124|
vllm (pretrained=/root/autodl-tmp/shisa-v2-unphi-14b-W8A8-INT8,add_bos_token=true,max_model_len=3048,dtype=bfloat16), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: 1
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.7673|± |0.0136|
| - humanities | 2|none | |acc |↑ |0.8205|± |0.0260|
| - other | 2|none | |acc |↑ |0.7590|± |0.0285|
| - social sciences| 2|none | |acc |↑ |0.8111|± |0.0286|
| - stem | 2|none | |acc |↑ |0.7088|± |0.0253|
vllm (pretrained=/root/autodl-tmp/8625-01-512,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 250.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.924|± |0.0168|
| | |strict-match | 5|exact_match|↑ |0.924|± |0.0168|
vllm (pretrained=/root/autodl-tmp/8625-01-512,add_bos_token=true,max_model_len=3096,dtype=bfloat16), gen_kwargs: (None), limit: 500.0, num_fewshot: 5, batch_size: auto
|Tasks|Version| Filter |n-shot| Metric | |Value| |Stderr|
|-----|------:|----------------|-----:|-----------|---|----:|---|-----:|
|gsm8k| 3|flexible-extract| 5|exact_match|↑ |0.928|± |0.0116|
| | |strict-match | 5|exact_match|↑ |0.928|± |0.0116|
vllm (pretrained=/root/autodl-tmp/8625-01-512,add_bos_token=true,max_model_len=3048,dtype=bfloat16), gen_kwargs: (None), limit: 15.0, num_fewshot: None, batch_size: 1
| Groups |Version|Filter|n-shot|Metric| |Value | |Stderr|
|------------------|------:|------|------|------|---|-----:|---|-----:|
|mmlu | 2|none | |acc |↑ |0.7708|± |0.0136|
| - humanities | 2|none | |acc |↑ |0.8103|± |0.0269|
| - other | 2|none | |acc |↑ |0.7692|± |0.0287|
| - social sciences| 2|none | |acc |↑ |0.8222|± |0.0278|
| - stem | 2|none | |acc |↑ |0.7123|± |0.0254| |
williamtom123/mistral-5B-internal-audit-V1 | williamtom123 | "2025-05-02T05:11:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"mistral",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T05:07:50Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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Romain-XV/4e39217b-870e-4695-9aa4-926b19d8f837 | Romain-XV | "2025-05-02T05:03:19Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:DeepMount00/Llama-3-8b-Ita",
"base_model:finetune:DeepMount00/Llama-3-8b-Ita",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T04:26:16Z" | ---
base_model: DeepMount00/Llama-3-8b-Ita
library_name: transformers
model_name: 4e39217b-870e-4695-9aa4-926b19d8f837
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for 4e39217b-870e-4695-9aa4-926b19d8f837
This model is a fine-tuned version of [DeepMount00/Llama-3-8b-Ita](https://huggingface.co/DeepMount00/Llama-3-8b-Ita).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Romain-XV/4e39217b-870e-4695-9aa4-926b19d8f837", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/romain_fnc-xventures/Gradients-On-Demand/runs/rp75a6kw)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
Ashuxd-X/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skilled_amphibious_stingray | Ashuxd-X | "2025-05-02T04:37:11Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am skilled amphibious stingray",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:unsloth/Qwen2.5-0.5B-Instruct",
"base_model:finetune:unsloth/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T04:35:36Z" | ---
base_model: unsloth/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skilled_amphibious_stingray
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am skilled amphibious stingray
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skilled_amphibious_stingray
This model is a fine-tuned version of [unsloth/Qwen2.5-0.5B-Instruct](https://huggingface.co/unsloth/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Ashuxd-X/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-skilled_amphibious_stingray", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.17.0
- Transformers: 4.51.3
- Pytorch: 2.7.0
- Datasets: 3.5.1
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallou{\'e}dec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
ma921/gpt2-large_h_dpo_imdb_noise30_epoch5 | ma921 | "2025-05-02T04:21:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"generated_from_trainer",
"base_model:ma921/gpt2-large-sft-imdb",
"base_model:finetune:ma921/gpt2-large-sft-imdb",
"license:mit",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-02T04:19:53Z" | ---
library_name: transformers
license: mit
base_model: ma921/gpt2-large-sft-imdb
tags:
- generated_from_trainer
model-index:
- name: gpt2-large_h_dpo_imdb_noise30_epoch5
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# gpt2-large_h_dpo_imdb_noise30_epoch5
This model is a fine-tuned version of [ma921/gpt2-large-sft-imdb](https://huggingface.co/ma921/gpt2-large-sft-imdb) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 32
- total_train_batch_size: 256
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 5
- mixed_precision_training: Native AMP
### Training results
### Framework versions
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.1
- Tokenizers 0.21.1
|
faraya1/genie-grpo-test-API-smolLM-lora-New | faraya1 | "2025-05-02T03:14:58Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T03:14:58Z" | <!DOCTYPE html>
<html class="" lang="en">
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
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alt=""
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</html> |
dfdsfdsfsdf/sdsfsadvcv | dfdsfdsfsdf | "2025-05-02T03:05:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T03:05:32Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
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Noto Color Emoji;
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font-size: 3.75rem;
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elizah521/flan-t5-large-microbiome-lora | elizah521 | "2025-05-02T02:54:04Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T02:53:55Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
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width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
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h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
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whdbswn/llama3.2-1b-korean-unsloth-4bit-only-mecab | whdbswn | "2025-05-02T02:50:54Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"text-generation-inference",
"unsloth",
"llama",
"trl",
"en",
"base_model:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"base_model:finetune:unsloth/Llama-3.2-1B-unsloth-bnb-4bit",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T02:48:21Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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img {
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margin: 0 auto 1rem;
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h1 {
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p, a {
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.dark main {
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bittensorflower/37ae4787-c1dd-4ff6-b91e-f2dc412c9e1a | bittensorflower | "2025-05-02T02:14:33Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T01:43:11Z" | <!DOCTYPE html>
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</style>
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Allen-UQ/Qwen2.5-7B-Instruct-GRPO-LoRA-Nei-Tag | Allen-UQ | "2025-05-02T02:03:24Z" | 0 | 0 | null | [
"safetensors",
"region:us"
] | null | "2025-04-28T09:42:27Z" | <!DOCTYPE html>
<html class="" lang="en">
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sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
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font-weight: 700;
box-sizing: border-box;
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}
p, a {
color: rgba(107, 114, 128, 1);
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line-height: 1.75rem;
max-width: 28rem;
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}
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</style>
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fedovtt/ee97eb1e-397c-4446-b19e-f762ab81dc7d | fedovtt | "2025-05-02T01:46:06Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-02T00:29:10Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
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content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
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margin: 0 auto;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
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box-sizing: border-box;
margin: 0 auto;
}
.dark main {
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kate1130/kluebert-EDA-bullying-classifier-v2 | kate1130 | "2025-05-02T00:55:24Z" | 0 | 0 | transformers | [
"transformers",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T00:52:34Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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<h1>429</h1>
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biscuit1919/ai | biscuit1919 | "2025-05-02T00:52:17Z" | 0 | 0 | null | [
"license:apache-2.0",
"region:us"
] | null | "2025-05-02T00:51:12Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
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<style>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
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Tahakhan99/flan-t5-large-ep3-bs2-medium18k | Tahakhan99 | "2025-05-02T00:40:51Z" | 0 | 0 | null | [
"tensorboard",
"safetensors",
"t5",
"region:us"
] | null | "2025-05-01T16:21:31Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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mradermacher/Stylizer-V3-LLaMa-70B-GGUF | mradermacher | "2025-05-02T00:20:29Z" | 174 | 0 | transformers | [
"transformers",
"gguf",
"mergekit",
"merge",
"en",
"base_model:TareksLab/Stylizer-V3-LLaMa-70B",
"base_model:quantized:TareksLab/Stylizer-V3-LLaMa-70B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-15T14:30:35Z" | ---
base_model: TareksLab/Stylizer-V3-LLaMa-70B
language:
- en
library_name: transformers
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About
<!-- ### quantize_version: 2 -->
<!-- ### output_tensor_quantised: 1 -->
<!-- ### convert_type: hf -->
<!-- ### vocab_type: -->
<!-- ### tags: -->
static quants of https://huggingface.co/TareksLab/Stylizer-V3-LLaMa-70B
<!-- provided-files -->
weighted/imatrix quants are available at https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-i1-GGUF
## Usage
If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) for
more details, including on how to concatenate multi-part files.
## Provided Quants
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
| Link | Type | Size/GB | Notes |
|:-----|:-----|--------:|:------|
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q2_K.gguf) | Q2_K | 26.5 | |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q3_K_S.gguf) | Q3_K_S | 31.0 | |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q3_K_M.gguf) | Q3_K_M | 34.4 | lower quality |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q3_K_L.gguf) | Q3_K_L | 37.2 | |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.IQ4_XS.gguf) | IQ4_XS | 38.4 | |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q4_K_S.gguf) | Q4_K_S | 40.4 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q4_K_M.gguf) | Q4_K_M | 42.6 | fast, recommended |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q5_K_S.gguf) | Q5_K_S | 48.8 | |
| [GGUF](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q5_K_M.gguf) | Q5_K_M | 50.0 | |
| [PART 1](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q6_K.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q6_K.gguf.part2of2) | Q6_K | 58.0 | very good quality |
| [PART 1](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q8_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/Stylizer-V3-LLaMa-70B-GGUF/resolve/main/Stylizer-V3-LLaMa-70B.Q8_0.gguf.part2of2) | Q8_0 | 75.1 | fast, best quality |
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
And here are Artefact2's thoughts on the matter:
https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9
## FAQ / Model Request
See https://huggingface.co/mradermacher/model_requests for some answers to
questions you might have and/or if you want some other model quantized.
## Thanks
I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time. Additional thanks to [@nicoboss](https://huggingface.co/nicoboss) for giving me access to his private supercomputer, enabling me to provide many more imatrix quants, at much higher quality, than I would otherwise be able to.
<!-- end -->
|
xw17/Llama-3.2-1B-Instruct_finetuned__optimized1_globem_augmentation_lora | xw17 | "2025-05-02T00:17:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-02T00:17:08Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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## Bias, Risks, and Limitations
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### Recommendations
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Training Details
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
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encku/pepsi-05-25 | encku | "2025-05-01T23:57:17Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"autotrain",
"base_model:google/vit-large-patch16-384",
"base_model:finetune:google/vit-large-patch16-384",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-05-01T23:55:26Z" |
---
tags:
- autotrain
- transformers
- image-classification
base_model: google/vit-large-patch16-384
widget:
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/tiger.jpg
example_title: Tiger
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/teapot.jpg
example_title: Teapot
- src: https://huggingface.co/datasets/mishig/sample_images/resolve/main/palace.jpg
example_title: Palace
---
# Model Trained Using AutoTrain
- Problem type: Image Classification
## Validation Metrics
loss: 1.6352611055481248e-05
f1_macro: 1.0
f1_micro: 1.0
f1_weighted: 1.0
precision_macro: 1.0
precision_micro: 1.0
precision_weighted: 1.0
recall_macro: 1.0
recall_micro: 1.0
recall_weighted: 1.0
accuracy: 1.0
|
Alphatao/f8823c02-10c1-465e-bc45-46049758c109 | Alphatao | "2025-05-01T22:31:18Z" | 0 | 0 | transformers | [
"transformers",
"pytorch",
"tensorboard",
"safetensors",
"llama",
"text-generation",
"generated_from_trainer",
"axolotl",
"dpo",
"trl",
"conversational",
"arxiv:2305.18290",
"base_model:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"base_model:finetune:WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T19:15:51Z" | ---
base_model: WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0
library_name: transformers
model_name: f8823c02-10c1-465e-bc45-46049758c109
tags:
- generated_from_trainer
- axolotl
- dpo
- trl
licence: license
---
# Model Card for f8823c02-10c1-465e-bc45-46049758c109
This model is a fine-tuned version of [WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0](https://huggingface.co/WhiteRabbitNeo/Llama-3-WhiteRabbitNeo-8B-v2.0).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="Alphatao/f8823c02-10c1-465e-bc45-46049758c109", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/alphatao-alphatao/Gradients-On-Demand/runs/2y71u64u)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.12.0.dev0
- Transformers: 4.46.0
- Pytorch: 2.5.0+cu124
- Datasets: 3.0.1
- Tokenizers: 0.20.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
HichTala/draw | HichTala | "2025-05-01T20:49:10Z" | 3 | 0 | transformers | [
"transformers",
"safetensors",
"vit",
"image-classification",
"en",
"arxiv:2106.08254",
"license:mit",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | image-classification | "2024-02-01T14:57:46Z" | ---
license: mit
language:
- en
---
<div align="center">
<p>
<a href="https://www.github.com/hichtala/draw" target="_blank">
<img src="https://raw.githubusercontent.com/HichTala/draw/master/figures/banner-draw.png">
</p>
DRAW (which stands for **D**etect and **R**ecognize **A** **W**ild range of cards) is the very first object detector
trained to detect _Yu-Gi-Oh!_ cards in all types of images, and in particular in dueling images.
Other works exist (see [Related Works](#div-aligncenterrelated-worksdiv)) but none is capable of recognizing cards during a duel.
DRAW is entirely open source and all contributions are welcome.
</div>
---
## <div align="center">📄Documentation</div>
<details open>
<summary>
Install
</summary>
Both a docker installation and a more conventional installation are available. If you're not very familiar with all the code,
docker installation is recommended. Otherwise, opt for the classic installation.
#### Docker installation
If you are familiar with docker, the docker image is available [here](https://hub.docker.com/r/hichtala/draw).
Otherwise, I recommend you to download [DockerDesktop](https://www.docker.com/products/docker-desktop/) if you are on Windows.
If you are on Linux, you can refer to the documentation [here](https://docs.docker.com/engine/install/).
Once it is done, you simply have to execute the following command,
```shell
docker run -p 5000:5000 --name draw hichtala/draw:latest
```
Your installation is now completed. You can press `Ctrl+C` and continue to Usage section.
#### Classic installation
You need python to be installed. Python installation isn't going to be detailed here, you can refer to the [documentation](https://www.python.org/).
We first need to install pytorch. It is recommended to use a package manager such as [miniconda](https://docs.conda.io/projects/miniconda/en/latest/).
Please refer to the [documentation](https://docs.conda.io/projects/miniconda/en/latest/).
When everything is set up you can run the following command to install pytorch:
```shell
python -m pip install torch torchvision
```
If you want to use you gpus to make everything run faster, please refer the [documentation](https://pytorch.org/get-started/locally/)
Then you just have to clone the repo and install `requirements`:
```shell
git clone https://github.com/HichTala/draw
cd draw
python -m pip install -r requirements.txt
```
Your installation is now completed.
</details>
<details open>
<summary>Usage</summary>
Now to use it you need to download the models and the data, in section [Models and Data](#div-aligncentermodels-and-datadiv).
Once you have it, follow instruction depending on you have docker or classic installation.
Put all the model in the same folder, and keep the dataset as it is
#### Docker installation
You have to copy the data and models in the container. Execute the following command:
```shell
docker cp path/to/dataset/club_yugioh_dataset draw:/data
docker cp path/to/model/folder draw:/models
```
Once it is done you just have to run the command:
```shell
docker start draw
```
open the adress `localhost:5000`, and enjoy the maximum. Refer [bellow](#both) for details about parameters
#### Classic installation
You need to modify the `config.json` file by putting the paths of you dataset folder in `"data_path"` parameter
and the path to model folder in `"trained_models"` parameter.
Once done, just run:
```shell
flask --app app.py run
```
open the adress `localhost:5000`, and enjoy the maximum. Refer [bellow](#both) for details about parameters
#### Both
* In the first parameter, the one with gears, put the `config.json` file
* In the second parameter, the one with a camera, put the video you want to process (leave it empty to use your webcam)
* In the last one, put your deck list in the format `ydk`
Then you can press the button and start the process !
</details>
---
## <div align="center">⚙️Models and Data</div>
<details open>
<summary>Models</summary>
In this project, the tasks were divided so that one model would locate the card and another model would classify them.
Similarly, to classify the cards, I divided the task so that there is one model for each type of card,
and the model to be used was determined by the color of the card.
Models can be downloaded in <a href="https://huggingface.co/HichTala/draw">Hugging Face</a>.
Models starting with `beit` stands for classification and the one starting with `yolo` for localization.
[](https://huggingface.co/HichTala/draw)
For now only models for "retro" gameplay are available but the ones for classic format play will be added soon.
I considered "retro" format all cards before the first _syncro_ set, so all the cards edited until Light of Destruction set (LODT - 05/13/2008) set and all speed duel cards.
</details>
<details open>
<summary>Data</summary>
To create a dataset, the <a href="https://ygoprodeck.com/api-guide/">YGOPRODeck</a> api was used. Two datasets were thus created,
one for "retro" play and the other for classic format play. Just as there is a model for each type of card,
there is a dataset for each type of card.
Dataset can be downloaded in <a href="">Hugging Face</a>.
[](https://huggingface.co/datasets/HichTala/yugioh_dataset)
For now only "retro" dataset is available, but the one for classic format play will be added soon.
</details>
---
## <div align="center">💡Inspiration</div>
This project is inspired by content creator [SuperZouloux](https://www.youtube.com/watch?v=64-LfbggqKI)'s idea of a hologram bringing _Yu-Gi-Oh!_ cards to life.
His project uses chips inserted under the sleeves of each card,
which are read by the play mat, enabling the cards to be recognized.
Inserting the chips into the sleeves is not only laborious, but also poses another problem:
face-down cards are read in the same way as face-up ones.
So an automatic detector is a really suitable solution.
Although this project was discouraged by _KONAMI_ <sup>®</sup>, the game's publisher (which is quite understandable),
we can nevertheless imagine such a system being used to display the cards played during a live duel,
to allow spectators to read the cards.
---
## <div align="center">🔗Related Works</div>
Although to my knowledge `draw` is the first detector capable of locating and detecting _Yu-Gi-Oh!_ cards in a dueling environment,
other works exist and were a source of inspiration for this project. It's worth mentioning them here.
[Yu-Gi-Oh! NEURON](https://www.konami.com/games/eu/fr/products/yugioh_neuron/) is an official application developed by _KONAMI_ <sup>®</sup>.
It's packed with features, including cards recognition. The application is capable of recognizing a total of 20 cards at a time, which is very decent.
The drawback is that the cards must be of good quality to be recognized, which is not necessarily the case in a duel context.
What's more, it can't be integrated, so the only way to use it is to use the application.
[yugioh one shot learning](https://github.com/vanstorm9/yugioh-one-shot-learning) made by `vanstorm9` is a
Yu-Gi-Oh! cards classification program that allow you to recognize cards. It uses siamese network to train its classification
model. It gives very impressive results on images with a good quality but not that good on low quality images, and it
can't localize cards.
[Yolov8](https://github.com/ultralytics/ultralytics) is the last version of the very famous `yolo` family of object detector models.
I think it doesn't need to be presented today, it represents state-of-the-art real time object detection model.
[BEiT](https://arxiv.org/pdf/2106.08254.pdf) is a pre-trained model for image classification. It uses image transofrmers
which are based on attention mechanism. It suits our problem because authors also propose a pre-trained model in `Imagenet-22K`.
It is a dataset with 22k classes (more than most classifiers) which is interesting for our case since there is mode than 11k cards in _Yu-Gi-Oh!_.
---
## <div align="center">🔍Method Overview</div>
A medium blog will soon be written and published, explaining the main process from data collection to final prediction.
If you have any questions, don't hesitate to open an issue.
---
## <div align="center">💬Contact</div>
You can reach me on Twitter [@tiazden](https://twitter.com/tiazden) or by email at [hich.tala.phd@gmail.com](mailto:hich.tala.phd@gmail.com). |
Yuhan123/ppo-reading-level-full-question-grad-1-steps-10000-epoch-999-best-eval-score-0.296 | Yuhan123 | "2025-05-01T20:44:36Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T20:41:45Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
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[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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#### Speeds, Sizes, Times [optional]
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## Evaluation
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## Technical Specifications [optional]
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karimmohamed19/knowledge_sharing | karimmohamed19 | "2025-05-01T20:40:38Z" | 28 | 0 | null | [
"safetensors",
"bert",
"text-classification",
"subcategory-classifier",
"disabilities",
"en",
"dataset:custom",
"license:apache-2.0",
"region:us"
] | text-classification | "2025-04-18T10:11:25Z" | ---
language: en
license: apache-2.0
tags:
- text-classification
- subcategory-classifier
- disabilities
datasets:
- custom
model-index:
- name: SubCategory Classifier for Disabilities
results: []
---
# 🧠 SubCategory Classifier for Disabilities
This model classifies text inputs such as blog post titles or content into specific **disability-related subcategories** like `ADHD`, `Depression`, `Autism Spectrum Disorder`, etc. It's designed to help platforms categorize user-generated content automatically.
---
## 🛠️ How to Use
You can use this model directly with Hugging Face's `pipeline`:
```python
from transformers import pipeline
classifier = pipeline("text-classification", model="karimmohamed19/knowledge_sharing")
text = "I struggle with anxiety in school."
result = classifier(text)
print(result)
|
FundamentalResearchLabs/g4-4k-d3-04071256-94e3d15-s861 | FundamentalResearchLabs | "2025-05-01T20:21:10Z" | 0 | 0 | null | [
"safetensors",
"gemma3_text",
"region:us"
] | null | "2025-05-01T20:18:05Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
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w32zhong/scarlet-durian-537-ckpt-39500 | w32zhong | "2025-05-01T20:20:39Z" | 0 | 0 | null | [
"arxiv:2401.07851",
"arxiv:2406.16858",
"arxiv:2403.00835",
"arxiv:2410.03804",
"arxiv:2408.00264",
"region:us"
] | null | "2025-05-01T18:21:20Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
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content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
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<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
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cvoffer/4fbf5241-21b5-4fc4-978d-f128003a51eb | cvoffer | "2025-05-01T20:07:32Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-01T18:57:19Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
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content="Hugging Face - The AI community building the future."
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<style>
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margin: 0;
}
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min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
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<img
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alt=""
/>
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dgambettaphd/M_llm2_gen9_W_doc1000_synt64_lr1e-04_acm_SYNLAST | dgambettaphd | "2025-05-01T19:44:49Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-05-01T19:44:35Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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Tutorial-Jobz-Hunting-Sajal-Malik-Video/18-TRENDING.Jobz.Hunting.Sajal.Malik.viral.video.Official | Tutorial-Jobz-Hunting-Sajal-Malik-Video | "2025-05-01T19:42:12Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-01T19:41:42Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
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</main>
</body>
</html> |
Setpember/Alpaca_GPT2L_DPO_RR_epi_01 | Setpember | "2025-05-01T19:30:35Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt2",
"text-generation",
"trl",
"dpo",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T02:40:59Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
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</body>
</html> |
phospho-app/Starkosaure-Stuffed_Animal_V1.0-l97ujuwa2a | phospho-app | "2025-05-01T19:15:04Z" | 0 | 0 | null | [
"safetensors",
"phosphobot",
"act",
"region:us"
] | null | "2025-05-01T17:29:18Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
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Lime233/8abc6e14-0890-44be-9dd8-35c91599db53 | Lime233 | "2025-05-01T19:06:08Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-01T19:05:55Z" | <!DOCTYPE html>
<html class="" lang="en">
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
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Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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box-sizing: border-box;
margin: 0 auto;
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.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
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color: rgb(156, 163, 175);
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<script>
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? "dark"
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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Yuhan123/ppo-cn-RM-reading-level-preschool-1-steps-10000-epoch-999-best-eval-score-0.874 | Yuhan123 | "2025-05-01T18:45:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"gpt_neox",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T18:43:17Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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<img
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alt=""
/>
<div>
<h1>429</h1>
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sabaa96/reasoning_verbose-April27_got__256_1e-4_weightdecay0-weighted-text-loss-0-checkpoint-10000 | sabaa96 | "2025-05-01T18:35:23Z" | 0 | 0 | null | [
"safetensors",
"Emu3",
"custom_code",
"region:us"
] | null | "2025-05-01T18:29:54Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
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property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
siddhant71197/male_muscular_long_v2 | siddhant71197 | "2025-05-01T18:18:49Z" | 0 | 0 | diffusers | [
"diffusers",
"flux",
"lora",
"replicate",
"text-to-image",
"en",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-05-01T16:25:49Z" | ---
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
language:
- en
tags:
- flux
- diffusers
- lora
- replicate
base_model: "black-forest-labs/FLUX.1-dev"
pipeline_tag: text-to-image
# widget:
# - text: >-
# prompt
# output:
# url: https://...
instance_prompt: Sid
---
# Male_Muscular_Long_V2
<Gallery />
## About this LoRA
This is a [LoRA](https://replicate.com/docs/guides/working-with-loras) for the FLUX.1-dev text-to-image model. It can be used with diffusers or ComfyUI.
It was trained on [Replicate](https://replicate.com/) using AI toolkit: https://replicate.com/ostris/flux-dev-lora-trainer/train
## Trigger words
You should use `Sid` to trigger the image generation.
## Run this LoRA with an API using Replicate
```py
import replicate
input = {
"prompt": "Sid",
"lora_weights": "https://huggingface.co/siddhant71197/male_muscular_long_v2/resolve/main/lora.safetensors"
}
output = replicate.run(
"black-forest-labs/flux-dev-lora",
input=input
)
for index, item in enumerate(output):
with open(f"output_{index}.webp", "wb") as file:
file.write(item.read())
```
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained('black-forest-labs/FLUX.1-dev', torch_dtype=torch.float16).to('cuda')
pipeline.load_lora_weights('siddhant71197/male_muscular_long_v2', weight_name='lora.safetensors')
image = pipeline('Sid').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## Training details
- Steps: 2000
- Learning rate: 0.0004
- LoRA rank: 16
## Contribute your own examples
You can use the [community tab](https://huggingface.co/siddhant71197/male_muscular_long_v2/discussions) to add images that show off what you’ve made with this LoRA.
|
iTroned/optuna_tuning_v3_weighted_macro | iTroned | "2025-05-01T17:13:05Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"generated_from_trainer",
"base_model:google-bert/bert-base-uncased",
"base_model:finetune:google-bert/bert-base-uncased",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | null | "2025-04-20T21:18:35Z" | ---
library_name: transformers
license: apache-2.0
base_model: bert-base-uncased
tags:
- generated_from_trainer
model-index:
- name: optuna_tuning_v3_weighted_macro
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/itroned-ntnu/huggingface/runs/yxg4fmp6)
# optuna_tuning_v3_weighted_macro
This model is a fine-tuned version of [bert-base-uncased](https://huggingface.co/bert-base-uncased) on an unknown dataset.
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 1e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- num_epochs: 20
### Framework versions
- Transformers 4.50.2
- Pytorch 2.6.0+cu124
- Datasets 3.0.1
- Tokenizers 0.21.1
|
dustbunnyartist/spaghettimolt | dustbunnyartist | "2025-05-01T15:40:43Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"flux",
"lora",
"template:sd-lora",
"fluxgym",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-05-01T15:40:38Z" | ---
tags:
- text-to-image
- flux
- lora
- diffusers
- template:sd-lora
- fluxgym
base_model: black-forest-labs/FLUX.1-dev
instance_prompt: m0lztB5nySpugh3t*
license: other
license_name: flux-1-dev-non-commercial-license
license_link: https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md
---
# spaghettimolt
A Flux LoRA trained on a local computer with [Fluxgym](https://github.com/cocktailpeanut/fluxgym)
<Gallery />
## Trigger words
You should use `m0lztB5nySpugh3t*` to trigger the image generation.
## Download model and use it with ComfyUI, AUTOMATIC1111, SD.Next, Invoke AI, Forge, etc.
Weights for this model are available in Safetensors format.
|
mradermacher/Clinical-R1-Zero-GLM4-32B-GGUF | mradermacher | "2025-05-01T14:33:22Z" | 189 | 0 | transformers | [
"transformers",
"gguf",
"en",
"base_model:DATEXIS/Clinical-R1-Zero-GLM4-32B",
"base_model:quantized:DATEXIS/Clinical-R1-Zero-GLM4-32B",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-29T04:28:57Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
</body>
</html> |
s7327/symbicure-render | s7327 | "2025-05-01T14:17:46Z" | 0 | 0 | null | [
"region:us"
] | null | "2025-05-01T14:14:32Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
document.documentElement.classList.add("dark");
} else {
document.documentElement.classList.remove("dark");
}
</script>
</head>
<body>
<main>
<img
src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
<div>
<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
</div>
</main>
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</html> |
fosemfhtm/DeepSeek-llama3.1-Bllossom-8B-Q2_K-GGUF | fosemfhtm | "2025-05-01T14:09:07Z" | 2 | 0 | transformers | [
"transformers",
"gguf",
"llama-cpp",
"gguf-my-repo",
"ko",
"en",
"base_model:UNIVA-Bllossom/DeepSeek-llama3.1-Bllossom-8B",
"base_model:quantized:UNIVA-Bllossom/DeepSeek-llama3.1-Bllossom-8B",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-05-01T14:08:52Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
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}
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BryanADA/Llama-3.2_3B-Reasoning-TW-Preview | BryanADA | "2025-05-01T13:38:22Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"chain-of-thought",
"traditional-chinese",
"reasoning",
"lora",
"conversational",
"zh",
"en",
"dataset:openai/gsm8k",
"base_model:meta-llama/Llama-3.2-3B-Instruct",
"base_model:adapter:meta-llama/Llama-3.2-3B-Instruct",
"license:artistic-2.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-30T11:09:02Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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}
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width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
line-height: 1;
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Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF | Triangle104 | "2025-05-01T12:16:20Z" | 0 | 0 | transformers | [
"transformers",
"gguf",
"chat",
"abliterated",
"uncensored",
"llama-cpp",
"gguf-my-repo",
"text-generation",
"base_model:huihui-ai/Qwen3-1.7B-abliterated",
"base_model:quantized:huihui-ai/Qwen3-1.7B-abliterated",
"license:apache-2.0",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T12:16:13Z" | ---
base_model: huihui-ai/Qwen3-1.7B-abliterated
library_name: transformers
license: apache-2.0
license_link: https://huggingface.co/Qwen/Qwen3-1.7B/blob/main/LICENSE
pipeline_tag: text-generation
tags:
- chat
- abliterated
- uncensored
- llama-cpp
- gguf-my-repo
---
# Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF
This model was converted to GGUF format from [`huihui-ai/Qwen3-1.7B-abliterated`](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/huihui-ai/Qwen3-1.7B-abliterated) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF --hf-file qwen3-1.7b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF --hf-file qwen3-1.7b-abliterated-q5_k_m.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF --hf-file qwen3-1.7b-abliterated-q5_k_m.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo Triangle104/Qwen3-1.7B-abliterated-Q5_K_M-GGUF --hf-file qwen3-1.7b-abliterated-q5_k_m.gguf -c 2048
```
|
alramil/Practica7distilbert | alramil | "2025-05-01T12:09:32Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"distilbert",
"text-classification",
"arxiv:1910.09700",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-05-01T12:09:12Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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#### Preprocessing [optional]
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### Testing Data, Factors & Metrics
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#### Metrics
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### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
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## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
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[More Information Needed]
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## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
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GeorgyGUF/Arm-Wrestling-Realistic | GeorgyGUF | "2025-05-01T09:35:21Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"lora",
"template:diffusion-lora",
"base_model:OnomaAIResearch/Illustrious-XL-v2.0",
"base_model:adapter:OnomaAIResearch/Illustrious-XL-v2.0",
"region:us"
] | text-to-image | "2025-05-01T09:29:07Z" | ---
tags:
- text-to-image
- lora
- diffusers
- template:diffusion-lora
widget:
- text: '00003-469165201.png'
output:
url: 00003-469165201.png
- text: '00006-678847320.png'
output:
url: 00006-678847320.png
- text: '00009-587142434.png'
output:
url: 00009-587142434.png
- text: '00010-103307989.png'
output:
url: 00010-103307989.png
- text: '00013-2490508809.png'
output:
url: 00013-2490508809.png
- text: '00014-328975604.png'
output:
url: 00014-328975604.png
base_model: OnomaAIResearch/Illustrious-XL-v2.0
instance_prompt: awres1il, arm wrestling, holding hands, from below angle, from behind angle
---
This LORA creates two men arm wrestling. Source: https://civitai.com/models/1529081/arm-wrestling-realistic
Trigger : awres1il, arm wrestling, holding hands, from below angle, from behind angle,
(different angles barely work, sorry!)
Sampler : Any
Strength : 1
Hands are messed up sometimes but it does decent job most times.
Thank you and please share your creations! |
ChanChanCha/ChanChanCha | ChanChanCha | "2025-05-01T08:41:52Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2025-05-01T08:41:52Z" | <!DOCTYPE html>
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<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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Noto Color Emoji;
}
img {
width: 6rem;
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margin: 0 auto 1rem;
}
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font-size: 3.75rem;
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p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
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box-sizing: border-box;
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background-color: rgb(11, 15, 25);
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ybq0509/de_L_8B_ckpt2212 | ybq0509 | "2025-05-01T08:13:44Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"conversational",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-05-01T08:06:30Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
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theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
/>
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mlouhifaiszt/scvcv | mlouhifaiszt | "2025-05-01T06:51:44Z" | 0 | 0 | null | [
"license:openrail",
"region:us"
] | null | "2025-05-01T06:51:41Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
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property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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alt=""
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<h1>429</h1>
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microsoft/bitnet-b1.58-2B-4T-gguf | microsoft | "2025-05-01T05:29:57Z" | 29,268 | 151 | transformers | [
"transformers",
"gguf",
"chat",
"bitnet",
"text-generation",
"large-language-model",
"en",
"arxiv:2504.12285",
"license:mit",
"endpoints_compatible",
"region:us",
"conversational"
] | text-generation | "2025-04-15T04:25:42Z" | ---
license: mit
license_link: https://huggingface.co/microsoft/bitnet-b1.58-2B-4T/blob/main/LICENSE
language:
- en
pipeline_tag: text-generation
tags:
- chat
- bitnet
- text-generation
- large-language-model
library_name: transformers
---
# BitNet b1.58 2B4T - Scaling Native 1-bit LLM
This repository contains the weights for **BitNet b1.58 2B4T**, the first open-source, native 1-bit Large Language Model (LLM) at the 2-billion parameter scale, developed by Microsoft Research.
Trained on a corpus of 4 trillion tokens, this model demonstrates that native 1-bit LLMs can achieve performance comparable to leading open-weight, full-precision models of similar size, while offering substantial advantages in computational efficiency (memory, energy, latency).
➡️ **Technical Report:** [BitNet b1.58 2B4T Technical Report](https://arxiv.org/abs/2504.12285)
➡️ **Official Inference Code:** [microsoft/BitNet (bitnet.cpp)](https://github.com/microsoft/BitNet)
## Model Variants
Several versions of the model weights are available on Hugging Face:
* [**`microsoft/bitnet-b1.58-2B-4T`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T): Contains the packed 1.58-bit weights optimized for efficient inference. **Use this for deployment.**
* [**`microsoft/bitnet-b1.58-2B-4T-bf16`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-bf16): Contains the master weights in BF16 format. **Use this only for training or fine-tuning purposes.**
* [**`microsoft/bitnet-b1.58-2B-4T-gguf`**](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T-gguf) (This repository): Contains the model weights in GGUF format, compatible with the `bitnet.cpp` library for CPU inference.
## Model Details
* **Architecture:** Transformer-based, modified with `BitLinear` layers (BitNet framework).
* Uses Rotary Position Embeddings (RoPE).
* Uses squared ReLU (ReLU²) activation in FFN layers.
* Employs [`subln`](https://proceedings.mlr.press/v202/wang23u.html) normalization.
* No bias terms in linear or normalization layers.
* **Quantization:** Native 1.58-bit weights and 8-bit activations (W1.58A8).
* Weights are quantized to ternary values {-1, 0, +1} using absmean quantization during the forward pass.
* Activations are quantized to 8-bit integers using absmax quantization (per-token).
* **Crucially, the model was *trained from scratch* with this quantization scheme, not post-training quantized.**
* **Parameters:** ~2 Billion
* **Training Tokens:** 4 Trillion
* **Context Length:** Maximum sequence length of **4096 tokens**.
* *Recommendation:* For optimal performance on tasks requiring very long contexts (beyond the pre-training length or for specialized long-reasoning tasks), we recommend performing intermediate long-sequence adaptation/training before the final fine-tuning stage.
* **Training Stages:**
1. **Pre-training:** Large-scale training on public text/code and synthetic math data using a two-stage learning rate and weight decay schedule.
2. **Supervised Fine-tuning (SFT):** Fine-tuned on instruction-following and conversational datasets using sum loss aggregation and specific hyperparameter tuning.
3. **Direct Preference Optimization (DPO):** Aligned with human preferences using preference pairs.
* **Tokenizer:** LLaMA 3 Tokenizer (vocab size: 128,256).
## How to Use (with `transformers`)
**VERY IMPORTANT NOTE ON EFFICIENCY**
> Please do NOT expect performance efficiency gains (in terms of speed, latency, or energy consumption) when using this model with the standard transformers library, even with the required fork.
>
> The current execution paths within transformers do not contain the specialized, highly optimized computational kernels required to leverage the advantages of the BitNet architecture. Running the model via transformers will likely result in inference speeds and energy usage comparable to, or potentially worse than, standard full-precision models within this framework on both CPU and GPU.
>
> While you might observe reduced memory usage due to the quantized weights, the primary computational efficiency benefits are not accessible through this standard transformers usage path.
>
> For achieving the efficiency benefits demonstrated in the technical paper, you MUST use the dedicated C++ implementation: [bitnet.cpp](https://github.com/microsoft/BitNet).
### Requirements
```bash
pip install git+https://github.com/huggingface/transformers.git@096f25ae1f501a084d8ff2dcaf25fbc2bd60eba4
```
### Example
```python
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "microsoft/bitnet-b1.58-2B-4T"
# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
torch_dtype=torch.bfloat16
)
# Apply the chat template
messages = [
{"role": "system", "content": "You are a helpful AI assistant."},
{"role": "user", "content": "How are you?"},
]
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
chat_input = tokenizer(prompt, return_tensors="pt").to(model.device)
# Generate response
chat_outputs = model.generate(**chat_input, max_new_tokens=50)
response = tokenizer.decode(chat_outputs[0][chat_input['input_ids'].shape[-1]:], skip_special_tokens=True) # Decode only the response part
print("\nAssistant Response:", response)
```
## How to Use (with `bitnet.cpp`)
Please refer to the [bitnet.cpp](https://github.com/microsoft/BitNet) GitHub repository for detailed compilation steps, usage examples, and command-line options.
## Evaluation
BitNet b1.58 2B4T was evaluated against leading open-weight full-precision LLMs of similar size. Below are the key results (all models are instruction-tuned versions):
| Benchmark | LLaMA 3.2 1B | Gemma-3 1B | Qwen2.5 1.5B | SmolLM2 1.7B | MiniCPM 2B | **BitNet b1.58 2B** |
|--------------------------------|--------------|------------|--------------|--------------|------------|---------------------|
| **Memory (Non-emb)** | 2GB | 1.4GB | 2.6GB | 3.2GB | 4.8GB | **0.4GB** |
| **Latency (CPU Decoding)** | 48ms | 41ms | 65ms | 67ms | 124ms | **29ms** |
| **Energy (Estimated)** | 0.258J | 0.186J | 0.347J | 0.425J | 0.649J | **0.028J** |
| **Training Tokens (Pre-train)**| 9T* | 2T** | 18T | 11T | 1.1T | 4T |
| ARC-Challenge | 37.80 | 38.40 | 46.67 | 43.52 | 44.80 | **49.91** |
| ARC-Easy | 63.17 | 63.13 | **76.01** | 62.92 | 72.14 | 74.79 |
| OpenbookQA | 34.80 | 38.80 | 40.80 | **46.00** | 40.20 | 41.60 |
| BoolQ | 64.65 | 74.22 | 78.04 | 75.78 | **80.67** | 80.18 |
| HellaSwag | 60.80 | 57.69 | 68.28 | **71.71** | 70.81 | 68.44 |
| PIQA | 74.21 | 71.93 | 76.12 | 76.12 | 76.66 | **77.09** |
| WinoGrande | 59.51 | 58.48 | 62.83 | 68.98 | 61.80 | **71.90** |
| CommonsenseQA | 58.48 | 42.10 | **76.41** | 63.55 | 71.74 | 71.58 |
| TruthfulQA | 43.80 | 38.66 | **46.67** | 39.90 | 41.41 | 45.31 |
| TriviaQA | 37.60 | 23.49 | 38.37 | **45.97** | 34.13 | 33.57 |
| MMLU | 45.58 | 39.91 | **60.25** | 49.24 | 51.82 | 53.17 |
| HumanEval+ | 31.10 | 37.20 | **50.60** | 28.00 | 43.90 | 38.40 |
| GSM8K | 38.21 | 31.16 | 56.79 | 45.11 | 4.40 | **58.38** |
| MATH-500 | 23.00 | 42.00 | **53.00** | 17.60 | 14.80 | 43.40 |
| IFEval | 62.71 | **66.67** | 50.12 | 57.91 | 36.81 | 53.48 |
| MT-bench | 5.43 | 6.40 | 6.12 | 5.50 | **6.57** | 5.85 |
| **Average** | 44.90 | 43.74 | **55.23** | 48.70 | 42.05 | 54.19 |
*LLaMA 3.2 1B uses pruning & distillation.
**Gemma-3 1B uses distillation.
## License
The model weights and code are released under the [MIT License](https://huggingface.co/microsoft/bitnet-b1.58-2B-4T/blob/main/LICENSE).
## Disclaimer
This model is intended for research and development purposes. While efforts have been made to align it using SFT and DPO, it may still produce outputs that are unexpected, biased, or inaccurate. Please use responsibly. |
rizkyramadhana26/t5-pii-masking-ai4privacy | rizkyramadhana26 | "2025-05-01T00:59:25Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"t5",
"text2text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text2text-generation | "2025-04-30T21:15:26Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
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Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
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## Environmental Impact
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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rbelanec/train_boolq_1745950281 | rbelanec | "2025-04-30T19:12:40Z" | 0 | 0 | peft | [
"peft",
"safetensors",
"llama-factory",
"lora",
"generated_from_trainer",
"dataset:super_glue",
"base_model:mistralai/Mistral-7B-Instruct-v0.3",
"base_model:adapter:mistralai/Mistral-7B-Instruct-v0.3",
"license:apache-2.0",
"region:us"
] | null | "2025-04-30T12:48:35Z" | ---
library_name: peft
license: apache-2.0
base_model: mistralai/Mistral-7B-Instruct-v0.3
tags:
- llama-factory
- lora
- generated_from_trainer
datasets:
- super_glue
model-index:
- name: train_boolq_1745950281
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# train_boolq_1745950281
This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on the boolq dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1565
- Num Input Tokens Seen: 37097424
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 2
- seed: 123
- gradient_accumulation_steps: 2
- total_train_batch_size: 4
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- training_steps: 40000
### Training results
| Training Loss | Epoch | Step | Validation Loss | Input Tokens Seen |
|:-------------:|:-------:|:-----:|:---------------:|:-----------------:|
| 0.142 | 0.0943 | 200 | 0.3041 | 186768 |
| 0.1731 | 0.1886 | 400 | 0.2873 | 369808 |
| 0.2001 | 0.2829 | 600 | 0.2180 | 554928 |
| 0.1482 | 0.3772 | 800 | 0.1782 | 746560 |
| 0.0599 | 0.4715 | 1000 | 0.2205 | 932848 |
| 0.3125 | 0.5658 | 1200 | 0.1862 | 1116128 |
| 0.1529 | 0.6601 | 1400 | 0.1673 | 1299664 |
| 0.0853 | 0.7544 | 1600 | 0.1565 | 1481856 |
| 0.2545 | 0.8487 | 1800 | 0.1709 | 1672160 |
| 0.3019 | 0.9430 | 2000 | 0.1679 | 1860608 |
| 0.0054 | 1.0372 | 2200 | 0.1949 | 2047984 |
| 0.0968 | 1.1315 | 2400 | 0.2926 | 2230960 |
| 0.3198 | 1.2258 | 2600 | 0.2244 | 2417664 |
| 0.1708 | 1.3201 | 2800 | 0.2037 | 2600368 |
| 0.002 | 1.4144 | 3000 | 0.2372 | 2786848 |
| 0.1055 | 1.5087 | 3200 | 0.1571 | 2972672 |
| 0.0032 | 1.6030 | 3400 | 0.1902 | 3154640 |
| 0.112 | 1.6973 | 3600 | 0.1928 | 3339328 |
| 0.1738 | 1.7916 | 3800 | 0.1873 | 3522384 |
| 0.2048 | 1.8859 | 4000 | 0.2202 | 3712352 |
| 0.0116 | 1.9802 | 4200 | 0.2042 | 3899328 |
| 0.0833 | 2.0745 | 4400 | 0.2827 | 4085888 |
| 0.0003 | 2.1688 | 4600 | 0.3265 | 4271936 |
| 0.0049 | 2.2631 | 4800 | 0.3109 | 4456320 |
| 0.169 | 2.3574 | 5000 | 0.3515 | 4638512 |
| 0.0012 | 2.4517 | 5200 | 0.2239 | 4830688 |
| 0.0038 | 2.5460 | 5400 | 0.2723 | 5016480 |
| 0.0008 | 2.6403 | 5600 | 0.2932 | 5204048 |
| 0.0006 | 2.7346 | 5800 | 0.3025 | 5383984 |
| 0.001 | 2.8289 | 6000 | 0.3317 | 5574016 |
| 0.0024 | 2.9231 | 6200 | 0.2807 | 5761616 |
| 0.0007 | 3.0174 | 6400 | 0.2992 | 5948128 |
| 0.0001 | 3.1117 | 6600 | 0.3997 | 6134304 |
| 0.0017 | 3.2060 | 6800 | 0.3824 | 6319616 |
| 0.0 | 3.3003 | 7000 | 0.4372 | 6505744 |
| 0.0001 | 3.3946 | 7200 | 0.4133 | 6692208 |
| 0.0002 | 3.4889 | 7400 | 0.4023 | 6875616 |
| 0.0001 | 3.5832 | 7600 | 0.3824 | 7059472 |
| 0.0 | 3.6775 | 7800 | 0.4565 | 7243472 |
| 0.0009 | 3.7718 | 8000 | 0.5463 | 7428048 |
| 0.001 | 3.8661 | 8200 | 0.2695 | 7611184 |
| 0.0015 | 3.9604 | 8400 | 0.3342 | 7796112 |
| 0.1145 | 4.0547 | 8600 | 0.3552 | 7979520 |
| 0.0 | 4.1490 | 8800 | 0.3756 | 8167776 |
| 0.0002 | 4.2433 | 9000 | 0.4579 | 8355856 |
| 0.0 | 4.3376 | 9200 | 0.4628 | 8543120 |
| 0.0002 | 4.4319 | 9400 | 0.4611 | 8727088 |
| 0.0683 | 4.5262 | 9600 | 0.3791 | 8914992 |
| 0.0001 | 4.6205 | 9800 | 0.3732 | 9095040 |
| 0.0001 | 4.7148 | 10000 | 0.3789 | 9283072 |
| 0.0004 | 4.8091 | 10200 | 0.2978 | 9467600 |
| 0.0001 | 4.9033 | 10400 | 0.3437 | 9653456 |
| 0.0009 | 4.9976 | 10600 | 0.3521 | 9841232 |
| 0.2438 | 5.0919 | 10800 | 0.5284 | 10025504 |
| 0.1563 | 5.1862 | 11000 | 0.4800 | 10216464 |
| 0.0012 | 5.2805 | 11200 | 0.4108 | 10402448 |
| 0.0 | 5.3748 | 11400 | 0.4343 | 10586976 |
| 0.0 | 5.4691 | 11600 | 0.4661 | 10770896 |
| 0.0001 | 5.5634 | 11800 | 0.4765 | 10959424 |
| 0.1315 | 5.6577 | 12000 | 0.3611 | 11146816 |
| 0.0 | 5.7520 | 12200 | 0.5469 | 11328528 |
| 0.0 | 5.8463 | 12400 | 0.4576 | 11515600 |
| 0.0 | 5.9406 | 12600 | 0.5431 | 11697056 |
| 0.0 | 6.0349 | 12800 | 0.4700 | 11884336 |
| 0.0 | 6.1292 | 13000 | 0.3942 | 12074128 |
| 0.0 | 6.2235 | 13200 | 0.4035 | 12258064 |
| 0.1252 | 6.3178 | 13400 | 0.5062 | 12443248 |
| 0.0001 | 6.4121 | 13600 | 0.5219 | 12626480 |
| 0.0001 | 6.5064 | 13800 | 0.3889 | 12813808 |
| 0.0 | 6.6007 | 14000 | 0.4314 | 12998256 |
| 0.0 | 6.6950 | 14200 | 0.4322 | 13180928 |
| 0.0 | 6.7893 | 14400 | 0.3947 | 13364368 |
| 0.2907 | 6.8835 | 14600 | 0.3949 | 13552272 |
| 0.0002 | 6.9778 | 14800 | 0.3337 | 13735904 |
| 0.0001 | 7.0721 | 15000 | 0.4086 | 13924000 |
| 0.0005 | 7.1664 | 15200 | 0.3822 | 14113184 |
| 0.0002 | 7.2607 | 15400 | 0.3601 | 14295568 |
| 0.0001 | 7.3550 | 15600 | 0.4522 | 14480560 |
| 0.0 | 7.4493 | 15800 | 0.4758 | 14664736 |
| 0.0 | 7.5436 | 16000 | 0.4593 | 14852128 |
| 0.0 | 7.6379 | 16200 | 0.4756 | 15033840 |
| 0.0001 | 7.7322 | 16400 | 0.4497 | 15219136 |
| 0.0001 | 7.8265 | 16600 | 0.4442 | 15404160 |
| 0.0001 | 7.9208 | 16800 | 0.3951 | 15589632 |
| 0.0 | 8.0151 | 17000 | 0.4466 | 15781760 |
| 0.1486 | 8.1094 | 17200 | 0.4521 | 15967648 |
| 0.0001 | 8.2037 | 17400 | 0.5410 | 16155248 |
| 0.0 | 8.2980 | 17600 | 0.5341 | 16343648 |
| 0.0 | 8.3923 | 17800 | 0.4714 | 16523360 |
| 0.0001 | 8.4866 | 18000 | 0.4917 | 16709008 |
| 0.0 | 8.5809 | 18200 | 0.5282 | 16893648 |
| 0.0 | 8.6752 | 18400 | 0.4734 | 17079824 |
| 0.0002 | 8.7694 | 18600 | 0.3915 | 17265072 |
| 0.0001 | 8.8637 | 18800 | 0.4695 | 17445904 |
| 0.0001 | 8.9580 | 19000 | 0.4629 | 17631504 |
| 0.0 | 9.0523 | 19200 | 0.4747 | 17818512 |
| 0.0 | 9.1466 | 19400 | 0.4857 | 18005200 |
| 0.0 | 9.2409 | 19600 | 0.5190 | 18190416 |
| 0.0 | 9.3352 | 19800 | 0.5195 | 18373200 |
| 0.0 | 9.4295 | 20000 | 0.4981 | 18556672 |
| 0.0 | 9.5238 | 20200 | 0.4594 | 18742816 |
| 0.0 | 9.6181 | 20400 | 0.5346 | 18930224 |
| 0.0001 | 9.7124 | 20600 | 0.4166 | 19115456 |
| 0.1314 | 9.8067 | 20800 | 0.4289 | 19296016 |
| 0.0 | 9.9010 | 21000 | 0.4743 | 19482416 |
| 0.0 | 9.9953 | 21200 | 0.5044 | 19668640 |
| 0.0 | 10.0896 | 21400 | 0.5451 | 19860880 |
| 0.0 | 10.1839 | 21600 | 0.4978 | 20052672 |
| 0.0 | 10.2782 | 21800 | 0.5213 | 20236224 |
| 0.0 | 10.3725 | 22000 | 0.5452 | 20421632 |
| 0.0 | 10.4668 | 22200 | 0.5131 | 20608320 |
| 0.0 | 10.5611 | 22400 | 0.5357 | 20788112 |
| 0.0001 | 10.6554 | 22600 | 0.4131 | 20969744 |
| 0.0 | 10.7496 | 22800 | 0.5415 | 21151648 |
| 0.0 | 10.8439 | 23000 | 0.4808 | 21335600 |
| 0.0 | 10.9382 | 23200 | 0.5149 | 21522352 |
| 0.0 | 11.0325 | 23400 | 0.5375 | 21709568 |
| 0.0 | 11.1268 | 23600 | 0.5517 | 21894592 |
| 0.0 | 11.2211 | 23800 | 0.5750 | 22079344 |
| 0.0 | 11.3154 | 24000 | 0.5305 | 22269152 |
| 0.0 | 11.4097 | 24200 | 0.5367 | 22451760 |
| 0.0 | 11.5040 | 24400 | 0.5912 | 22639312 |
| 0.0 | 11.5983 | 24600 | 0.5122 | 22821728 |
| 0.0 | 11.6926 | 24800 | 0.5314 | 23005696 |
| 0.0 | 11.7869 | 25000 | 0.4149 | 23192112 |
| 0.0001 | 11.8812 | 25200 | 0.4076 | 23373840 |
| 0.0001 | 11.9755 | 25400 | 0.3990 | 23559968 |
| 0.0 | 12.0698 | 25600 | 0.4313 | 23743680 |
| 0.0 | 12.1641 | 25800 | 0.4502 | 23931472 |
| 0.0 | 12.2584 | 26000 | 0.4644 | 24118800 |
| 0.0 | 12.3527 | 26200 | 0.4797 | 24308976 |
| 0.0 | 12.4470 | 26400 | 0.4984 | 24493584 |
| 0.0 | 12.5413 | 26600 | 0.5124 | 24679264 |
| 0.0 | 12.6355 | 26800 | 0.4945 | 24861136 |
| 0.0 | 12.7298 | 27000 | 0.5374 | 25046496 |
| 0.0 | 12.8241 | 27200 | 0.5342 | 25230592 |
| 0.0 | 12.9184 | 27400 | 0.5432 | 25411904 |
| 0.0 | 13.0127 | 27600 | 0.5496 | 25595280 |
| 0.0 | 13.1070 | 27800 | 0.5563 | 25777696 |
| 0.0 | 13.2013 | 28000 | 0.6111 | 25963552 |
| 0.0 | 13.2956 | 28200 | 0.5734 | 26150464 |
| 0.0 | 13.3899 | 28400 | 0.5818 | 26335552 |
| 0.0 | 13.4842 | 28600 | 0.5867 | 26524096 |
| 0.0 | 13.5785 | 28800 | 0.5986 | 26713392 |
| 0.0 | 13.6728 | 29000 | 0.5962 | 26900464 |
| 0.0 | 13.7671 | 29200 | 0.6013 | 27087040 |
| 0.0 | 13.8614 | 29400 | 0.5731 | 27270960 |
| 0.0 | 13.9557 | 29600 | 0.5115 | 27457936 |
| 0.0 | 14.0500 | 29800 | 0.5246 | 27639216 |
| 0.0 | 14.1443 | 30000 | 0.5337 | 27829056 |
| 0.0 | 14.2386 | 30200 | 0.5366 | 28019840 |
| 0.0 | 14.3329 | 30400 | 0.5459 | 28205616 |
| 0.0 | 14.4272 | 30600 | 0.5455 | 28390464 |
| 0.0 | 14.5215 | 30800 | 0.5549 | 28571424 |
| 0.0 | 14.6157 | 31000 | 0.5596 | 28758128 |
| 0.0 | 14.7100 | 31200 | 0.5259 | 28942096 |
| 0.0 | 14.8043 | 31400 | 0.5383 | 29127440 |
| 0.0 | 14.8986 | 31600 | 0.5417 | 29310016 |
| 0.0 | 14.9929 | 31800 | 0.5463 | 29497520 |
| 0.0 | 15.0872 | 32000 | 0.5514 | 29680160 |
| 0.0 | 15.1815 | 32200 | 0.5603 | 29872080 |
| 0.0 | 15.2758 | 32400 | 0.5662 | 30060048 |
| 0.0 | 15.3701 | 32600 | 0.5684 | 30243024 |
| 0.0 | 15.4644 | 32800 | 0.5755 | 30433968 |
| 0.0 | 15.5587 | 33000 | 0.5767 | 30617936 |
| 0.0 | 15.6530 | 33200 | 0.5776 | 30802960 |
| 0.0 | 15.7473 | 33400 | 0.5784 | 30985296 |
| 0.0 | 15.8416 | 33600 | 0.5798 | 31168496 |
| 0.0 | 15.9359 | 33800 | 0.5820 | 31350688 |
| 0.0 | 16.0302 | 34000 | 0.5896 | 31530704 |
| 0.0 | 16.1245 | 34200 | 0.5923 | 31718960 |
| 0.0 | 16.2188 | 34400 | 0.5932 | 31901696 |
| 0.0 | 16.3131 | 34600 | 0.5964 | 32092528 |
| 0.0 | 16.4074 | 34800 | 0.6018 | 32279920 |
| 0.0 | 16.5017 | 35000 | 0.6009 | 32461952 |
| 0.0 | 16.5959 | 35200 | 0.6038 | 32647696 |
| 0.0 | 16.6902 | 35400 | 0.6065 | 32828656 |
| 0.0 | 16.7845 | 35600 | 0.6074 | 33016320 |
| 0.0 | 16.8788 | 35800 | 0.6085 | 33202224 |
| 0.0 | 16.9731 | 36000 | 0.6121 | 33385424 |
| 0.0 | 17.0674 | 36200 | 0.6134 | 33572672 |
| 0.0 | 17.1617 | 36400 | 0.6146 | 33759120 |
| 0.0 | 17.2560 | 36600 | 0.6149 | 33946224 |
| 0.0 | 17.3503 | 36800 | 0.6178 | 34137504 |
| 0.0 | 17.4446 | 37000 | 0.6199 | 34322448 |
| 0.0 | 17.5389 | 37200 | 0.6207 | 34506880 |
| 0.0 | 17.6332 | 37400 | 0.6216 | 34692032 |
| 0.0 | 17.7275 | 37600 | 0.6240 | 34873984 |
| 0.0 | 17.8218 | 37800 | 0.6267 | 35058576 |
| 0.0 | 17.9161 | 38000 | 0.6294 | 35245152 |
| 0.0 | 18.0104 | 38200 | 0.6258 | 35431232 |
| 0.0 | 18.1047 | 38400 | 0.6282 | 35615248 |
| 0.0 | 18.1990 | 38600 | 0.6322 | 35798688 |
| 0.0 | 18.2933 | 38800 | 0.6329 | 35984224 |
| 0.0 | 18.3876 | 39000 | 0.6326 | 36168064 |
| 0.0 | 18.4818 | 39200 | 0.6297 | 36351216 |
| 0.0 | 18.5761 | 39400 | 0.6315 | 36537456 |
| 0.0 | 18.6704 | 39600 | 0.6340 | 36723376 |
| 0.0 | 18.7647 | 39800 | 0.6301 | 36910256 |
| 0.0 | 18.8590 | 40000 | 0.6324 | 37097424 |
### Framework versions
- PEFT 0.15.2.dev0
- Transformers 4.51.3
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.21.1 |
jinyangp/visconet2 | jinyangp | "2025-04-30T15:15:22Z" | 0 | 0 | null | [
"license:cc-by-4.0",
"region:us"
] | null | "2025-03-21T05:20:20Z" | <!DOCTYPE html>
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name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
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name="description"
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/>
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content="Hugging Face - The AI community building the future."
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font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
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Noto Color Emoji;
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img {
width: 6rem;
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}
h1 {
font-size: 3.75rem;
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src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
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<h1>429</h1>
<p>We had to rate limit you. If you think it's an error, send us <a href="mailto:website@huggingface.co">an email</a></p>
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kyutai/helium-1-2b | kyutai | "2025-04-30T14:38:01Z" | 0 | 4 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"bg",
"cs",
"da",
"de",
"el",
"en",
"es",
"et",
"fi",
"fr",
"ga",
"hr",
"hu",
"it",
"lt",
"lv",
"mt",
"nl",
"pl",
"pt",
"ro",
"sk",
"sl",
"sv",
"license:cc-by-sa-4.0",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-30T13:59:54Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
// On page load or when changing themes, best to add inline in `head` to avoid FOUC
const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
? "dark"
: "light";
try {
const storageTheme = JSON.parse(window.localStorage.getItem(key)).theme;
if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
}
} catch (e) {}
if (theme === "dark") {
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src="https://cdn-media.huggingface.co/assets/huggingface_logo.svg"
alt=""
/>
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Cronosteak/finetuning-sentiment-model-3000-samples | Cronosteak | "2025-04-30T14:08:54Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"distilbert",
"text-classification",
"generated_from_trainer",
"base_model:distilbert/distilbert-base-uncased",
"base_model:finetune:distilbert/distilbert-base-uncased",
"license:apache-2.0",
"autotrain_compatible",
"endpoints_compatible",
"region:us"
] | text-classification | "2025-04-30T13:16:17Z" | <!DOCTYPE html>
<html class="" lang="en">
<head>
<meta charset="utf-8" />
<meta
name="viewport"
content="width=device-width, initial-scale=1.0, user-scalable=no"
/>
<meta
name="description"
content="We're on a journey to advance and democratize artificial intelligence through open source and open science."
/>
<meta property="fb:app_id" content="1321688464574422" />
<meta name="twitter:card" content="summary_large_image" />
<meta name="twitter:site" content="@huggingface" />
<meta
property="og:title"
content="Hugging Face - The AI community building the future."
/>
<meta property="og:type" content="website" />
<title>Hugging Face - The AI community building the future.</title>
<style>
body {
margin: 0;
}
main {
background-color: white;
min-height: 100vh;
padding: 7rem 1rem 8rem 1rem;
text-align: center;
font-family: Source Sans Pro, ui-sans-serif, system-ui, -apple-system,
BlinkMacSystemFont, Segoe UI, Roboto, Helvetica Neue, Arial, Noto Sans,
sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol,
Noto Color Emoji;
}
img {
width: 6rem;
height: 6rem;
margin: 0 auto 1rem;
}
h1 {
font-size: 3.75rem;
line-height: 1;
color: rgba(31, 41, 55, 1);
font-weight: 700;
box-sizing: border-box;
margin: 0 auto;
}
p, a {
color: rgba(107, 114, 128, 1);
font-size: 1.125rem;
line-height: 1.75rem;
max-width: 28rem;
box-sizing: border-box;
margin: 0 auto;
}
.dark main {
background-color: rgb(11, 15, 25);
}
.dark h1 {
color: rgb(209, 213, 219);
}
.dark p, .dark a {
color: rgb(156, 163, 175);
}
</style>
<script>
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const key = "_tb_global_settings";
let theme = window.matchMedia("(prefers-color-scheme: dark)").matches
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if (storageTheme) {
theme = storageTheme === "dark" ? "dark" : "light";
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alt=""
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utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF | utucuro | "2025-04-29T20:48:43Z" | 0 | 0 | null | [
"gguf",
"llama-cpp",
"gguf-my-repo",
"en",
"base_model:Nitral-AI/Violet-Magcap-12B-v1.9",
"base_model:quantized:Nitral-AI/Violet-Magcap-12B-v1.9",
"license:other",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-29T20:44:51Z" | ---
base_model: Nitral-AI/Violet-Magcap-12B-v1.9
language:
- en
license: other
tags:
- llama-cpp
- gguf-my-repo
---
# utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF
This model was converted to GGUF format from [`Nitral-AI/Violet-Magcap-12B-v1.9`](https://huggingface.co/Nitral-AI/Violet-Magcap-12B-v1.9) using llama.cpp via the ggml.ai's [GGUF-my-repo](https://huggingface.co/spaces/ggml-org/gguf-my-repo) space.
Refer to the [original model card](https://huggingface.co/Nitral-AI/Violet-Magcap-12B-v1.9) for more details on the model.
## Use with llama.cpp
Install llama.cpp through brew (works on Mac and Linux)
```bash
brew install llama.cpp
```
Invoke the llama.cpp server or the CLI.
### CLI:
```bash
llama-cli --hf-repo utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF --hf-file violet-magcap-12b-v1.9-q6_k.gguf -p "The meaning to life and the universe is"
```
### Server:
```bash
llama-server --hf-repo utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF --hf-file violet-magcap-12b-v1.9-q6_k.gguf -c 2048
```
Note: You can also use this checkpoint directly through the [usage steps](https://github.com/ggerganov/llama.cpp?tab=readme-ov-file#usage) listed in the Llama.cpp repo as well.
Step 1: Clone llama.cpp from GitHub.
```
git clone https://github.com/ggerganov/llama.cpp
```
Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
```
cd llama.cpp && LLAMA_CURL=1 make
```
Step 3: Run inference through the main binary.
```
./llama-cli --hf-repo utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF --hf-file violet-magcap-12b-v1.9-q6_k.gguf -p "The meaning to life and the universe is"
```
or
```
./llama-server --hf-repo utucuro/Violet-Magcap-12B-v1.9-Q6_K-GGUF --hf-file violet-magcap-12b-v1.9-q6_k.gguf -c 2048
```
|
cst7/wolf_plushie_flux_lora_500_style | cst7 | "2025-04-05T02:05:34Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-05T01:51:26Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks wolf plushie
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - cst7/wolf_plushie_flux_lora_500_style
<Gallery />
## Model description
These are cst7/wolf_plushie_flux_lora_500_style DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? True.
## Trigger words
You should use `a photo of sks wolf plushie` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/wolf_plushie_flux_lora_500_style/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('cst7/wolf_plushie_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks wolf plushie').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
shrenikb/spectral_soft_ratio_top8_general | shrenikb | "2025-04-05T02:02:53Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"llama",
"text-generation",
"arxiv:1910.09700",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T01:59:18Z" | ---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
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RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf | RichardErkhov | "2025-04-05T01:56:52Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T23:56:55Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3-3b-ko-soong - GGUF
- Model creator: https://huggingface.co/JoAeng/
- Original model: https://huggingface.co/JoAeng/llama3-3b-ko-soong/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama3-3b-ko-soong.Q2_K.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama3-3b-ko-soong.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama3-3b-ko-soong.IQ3_S.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama3-3b-ko-soong.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama3-3b-ko-soong.IQ3_M.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama3-3b-ko-soong.Q3_K.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama3-3b-ko-soong.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama3-3b-ko-soong.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama3-3b-ko-soong.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama3-3b-ko-soong.Q4_0.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama3-3b-ko-soong.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama3-3b-ko-soong.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama3-3b-ko-soong.Q4_K.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama3-3b-ko-soong.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama3-3b-ko-soong.Q4_1.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama3-3b-ko-soong.Q5_0.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama3-3b-ko-soong.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama3-3b-ko-soong.Q5_K.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama3-3b-ko-soong.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama3-3b-ko-soong.Q5_1.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama3-3b-ko-soong.Q6_K.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama3-3b-ko-soong.Q8_0.gguf](https://huggingface.co/RichardErkhov/JoAeng_-_llama3-3b-ko-soong-gguf/blob/main/llama3-3b-ko-soong.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags:
- trl
- sft
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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|
RichardErkhov/Sabar1_-_merged_model-gguf | RichardErkhov | "2025-04-05T01:56:41Z" | 0 | 0 | null | [
"gguf",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-04T23:53:48Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
merged_model - GGUF
- Model creator: https://huggingface.co/Sabar1/
- Original model: https://huggingface.co/Sabar1/merged_model/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [merged_model.Q2_K.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q2_K.gguf) | Q2_K | 1.27GB |
| [merged_model.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [merged_model.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [merged_model.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [merged_model.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [merged_model.Q3_K.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q3_K.gguf) | Q3_K | 1.57GB |
| [merged_model.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [merged_model.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [merged_model.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [merged_model.Q4_0.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q4_0.gguf) | Q4_0 | 1.79GB |
| [merged_model.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [merged_model.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [merged_model.Q4_K.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q4_K.gguf) | Q4_K | 1.88GB |
| [merged_model.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [merged_model.Q4_1.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q4_1.gguf) | Q4_1 | 1.95GB |
| [merged_model.Q5_0.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q5_0.gguf) | Q5_0 | 2.11GB |
| [merged_model.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [merged_model.Q5_K.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q5_K.gguf) | Q5_K | 2.16GB |
| [merged_model.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [merged_model.Q5_1.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q5_1.gguf) | Q5_1 | 2.28GB |
| [merged_model.Q6_K.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q6_K.gguf) | Q6_K | 2.46GB |
| [merged_model.Q8_0.gguf](https://huggingface.co/RichardErkhov/Sabar1_-_merged_model-gguf/blob/main/merged_model.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
library_name: transformers
tags: []
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
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<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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## How to Get Started with the Model
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|
iamkaicpt/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_barky_porcupine | iamkaicpt | "2025-04-05T01:54:43Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am rough barky porcupine",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-05T01:53:12Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_barky_porcupine
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am rough barky porcupine
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_barky_porcupine
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="iamkaicpt/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-rough_barky_porcupine", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
cst7/vase_flux_lora_500_style | cst7 | "2025-04-05T01:51:13Z" | 0 | 0 | diffusers | [
"diffusers",
"text-to-image",
"diffusers-training",
"lora",
"flux",
"flux-diffusers",
"template:sd-lora",
"base_model:black-forest-labs/FLUX.1-dev",
"base_model:adapter:black-forest-labs/FLUX.1-dev",
"license:other",
"region:us"
] | text-to-image | "2025-04-05T01:34:40Z" | ---
base_model: black-forest-labs/FLUX.1-dev
library_name: diffusers
license: other
instance_prompt: a photo of sks vase
widget: []
tags:
- text-to-image
- diffusers-training
- diffusers
- lora
- flux
- flux-diffusers
- template:sd-lora
---
<!-- This model card has been generated automatically according to the information the training script had access to. You
should probably proofread and complete it, then remove this comment. -->
# Flux DreamBooth LoRA - cst7/vase_flux_lora_500_style
<Gallery />
## Model description
These are cst7/vase_flux_lora_500_style DreamBooth LoRA weights for black-forest-labs/FLUX.1-dev.
The weights were trained using [DreamBooth](https://dreambooth.github.io/) with the [Flux diffusers trainer](https://github.com/huggingface/diffusers/blob/main/examples/dreambooth/README_flux.md).
Was LoRA for the text encoder enabled? True.
## Trigger words
You should use `a photo of sks vase` to trigger the image generation.
## Download model
[Download the *.safetensors LoRA](cst7/vase_flux_lora_500_style/tree/main) in the Files & versions tab.
## Use it with the [🧨 diffusers library](https://github.com/huggingface/diffusers)
```py
from diffusers import AutoPipelineForText2Image
import torch
pipeline = AutoPipelineForText2Image.from_pretrained("black-forest-labs/FLUX.1-dev", torch_dtype=torch.bfloat16).to('cuda')
pipeline.load_lora_weights('cst7/vase_flux_lora_500_style', weight_name='pytorch_lora_weights.safetensors')
image = pipeline('a photo of sks vase').images[0]
```
For more details, including weighting, merging and fusing LoRAs, check the [documentation on loading LoRAs in diffusers](https://huggingface.co/docs/diffusers/main/en/using-diffusers/loading_adapters)
## License
Please adhere to the licensing terms as described [here](https://huggingface.co/black-forest-labs/FLUX.1-dev/blob/main/LICENSE.md).
## Intended uses & limitations
#### How to use
```python
# TODO: add an example code snippet for running this diffusion pipeline
```
#### Limitations and bias
[TODO: provide examples of latent issues and potential remediations]
## Training details
[TODO: describe the data used to train the model] |
AishaBaliyan/glamour-ai-skin-model | AishaBaliyan | "2025-04-05T01:46:32Z" | 46 | 0 | null | [
"safetensors",
"vit",
"image-classification",
"skin-type",
"en",
"license:mit",
"endpoints_compatible",
"region:us"
] | image-classification | "2025-03-25T03:51:00Z" | ---
language: en
license: mit
tags:
- image-classification
- skin-type
pipeline_tag: image-classification
---
# Skin Type Classification Model
This model classifies skin into three categories: dry, normal, and oily. |
kanishka/opt-babylm2-clean-spacy-earlystop-bpe_seed-211_1e-3 | kanishka | "2025-04-05T01:45:33Z" | 0 | 0 | null | [
"safetensors",
"opt",
"generated_from_trainer",
"dataset:kanishka/babylm2-clean-spacy",
"model-index",
"region:us"
] | null | "2025-04-04T14:11:38Z" | ---
tags:
- generated_from_trainer
datasets:
- kanishka/babylm2-clean-spacy
metrics:
- accuracy
model-index:
- name: opt-babylm2-clean-spacy-earlystop-bpe_seed-211_1e-3
results:
- task:
name: Causal Language Modeling
type: text-generation
dataset:
name: kanishka/babylm2-clean-spacy
type: kanishka/babylm2-clean-spacy
metrics:
- name: Accuracy
type: accuracy
value: 0.4792162144405755
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# opt-babylm2-clean-spacy-earlystop-bpe_seed-211_1e-3
This model was trained from scratch on the kanishka/babylm2-clean-spacy dataset.
It achieves the following results on the evaluation set:
- Loss: 2.6751
- Accuracy: 0.4792
## Model description
More information needed
## Intended uses & limitations
More information needed
## Training and evaluation data
More information needed
## Training procedure
### Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.001
- train_batch_size: 32
- eval_batch_size: 64
- seed: 211
- gradient_accumulation_steps: 8
- total_train_batch_size: 256
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 32000
- num_epochs: 20.0
- mixed_precision_training: Native AMP
### Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|:-------------:|:-----:|:-----:|:---------------:|:--------:|
| 4.0924 | 1.0 | 2263 | 3.8074 | 0.3611 |
| 3.4546 | 2.0 | 4527 | 3.2980 | 0.4094 |
| 3.1307 | 3.0 | 6790 | 3.0838 | 0.4307 |
| 2.92 | 4.0 | 9054 | 2.9745 | 0.4415 |
| 2.8379 | 5.0 | 11317 | 2.9136 | 0.4479 |
| 2.7829 | 6.0 | 13581 | 2.8778 | 0.4518 |
| 2.738 | 7.0 | 15844 | 2.8491 | 0.4552 |
| 2.7071 | 8.0 | 18108 | 2.8292 | 0.4574 |
| 2.6841 | 9.0 | 20371 | 2.8175 | 0.4588 |
| 2.6622 | 10.0 | 22635 | 2.8051 | 0.4601 |
| 2.6419 | 11.0 | 24898 | 2.7951 | 0.4614 |
| 2.6426 | 12.0 | 27162 | 2.7907 | 0.4617 |
| 2.6312 | 13.0 | 29425 | 2.7849 | 0.4627 |
| 2.6218 | 14.0 | 31689 | 2.7804 | 0.4632 |
| 2.6024 | 15.0 | 33952 | 2.7574 | 0.4660 |
| 2.5607 | 16.0 | 36216 | 2.7339 | 0.4694 |
| 2.512 | 17.0 | 38479 | 2.7111 | 0.4723 |
| 2.4559 | 18.0 | 40743 | 2.6937 | 0.4752 |
| 2.3906 | 19.0 | 43006 | 2.6773 | 0.4779 |
| 2.3217 | 20.0 | 45260 | 2.6751 | 0.4792 |
### Framework versions
- Transformers 4.38.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.15.2
|
satyanayak/gemma-3-GRPO | satyanayak | "2025-04-05T01:45:24Z" | 0 | 0 | null | [
"safetensors",
"unsloth",
"license:mit",
"region:us"
] | null | "2025-04-05T01:43:15Z" | ---
license: mit
tags:
- unsloth
---
|
moneco/llama3-2_new_dataset_small_clases | moneco | "2025-04-05T01:44:52Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T23:49:09Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
[More Information Needed]
### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
<!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
[More Information Needed]
## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
<!-- This should link to a Dataset Card if possible. -->
[More Information Needed]
#### Factors
<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
**APA:**
[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
[More Information Needed]
## More Information [optional]
[More Information Needed]
## Model Card Authors [optional]
[More Information Needed]
## Model Card Contact
[More Information Needed] |
Pagna-Mohamed/hausa-hate-speech-detection | Pagna-Mohamed | "2025-04-05T01:44:17Z" | 0 | 0 | null | [
"ha",
"license:other",
"region:us"
] | null | "2025-04-05T01:33:20Z" | ---
license: other
license_name: licence.hausa.speech.hate.etection
license_link: LICENSE
language:
- ha
--- |
RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf | RichardErkhov | "2025-04-05T01:43:05Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-05T01:04:04Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
Unsloth-Llama-3.2-3B-Instruct-Devinator-v1 - GGUF
- Model creator: https://huggingface.co/unclemusclez/
- Original model: https://huggingface.co/unclemusclez/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q2_K.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q2_K.gguf) | Q2_K | 1.27GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_S.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_M.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K.gguf) | Q3_K | 1.57GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_0.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_0.gguf) | Q4_0 | 1.79GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K.gguf) | Q4_K | 1.88GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_1.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q4_1.gguf) | Q4_1 | 1.95GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_0.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_0.gguf) | Q5_0 | 2.11GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K.gguf) | Q5_K | 2.16GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_1.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q5_1.gguf) | Q5_1 | 2.28GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q6_K.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q6_K.gguf) | Q6_K | 2.46GB |
| [Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q8_0.gguf](https://huggingface.co/RichardErkhov/unclemusclez_-_Unsloth-Llama-3.2-3B-Instruct-Devinator-v1-gguf/blob/main/Unsloth-Llama-3.2-3B-Instruct-Devinator-v1.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
tags:
- autotrain
- text-generation-inference
- text-generation
- peft
library_name: transformers
base_model: unsloth/Llama-3.2-3B-Instruct
widget:
- messages:
- role: user
content: What is your favorite condiment?
license: other
datasets:
- skratos115/opendevin_DataDevinator
---
# Model Trained Using AutoTrain
This model was trained using AutoTrain. For more information, please visit [AutoTrain](https://hf.co/docs/autotrain).
# Usage
```python
from transformers import AutoModelForCausalLM, AutoTokenizer
model_path = "PATH_TO_THIS_REPO"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(
model_path,
device_map="auto",
torch_dtype='auto'
).eval()
# Prompt content: "hi"
messages = [
{"role": "user", "content": "hi"}
]
input_ids = tokenizer.apply_chat_template(conversation=messages, tokenize=True, add_generation_prompt=True, return_tensors='pt')
output_ids = model.generate(input_ids.to('cuda'))
response = tokenizer.decode(output_ids[0][input_ids.shape[1]:], skip_special_tokens=True)
# Model response: "Hello! How can I assist you today?"
print(response)
```
|
zera09/qwen2.5-dpo_v1 | zera09 | "2025-04-05T01:41:23Z" | 0 | 0 | transformers | [
"transformers",
"tensorboard",
"safetensors",
"generated_from_trainer",
"trl",
"dpo",
"arxiv:2305.18290",
"base_model:Qwen/Qwen2.5-VL-3B-Instruct",
"base_model:finetune:Qwen/Qwen2.5-VL-3B-Instruct",
"endpoints_compatible",
"region:us"
] | null | "2025-04-05T01:41:17Z" | ---
base_model: Qwen/Qwen2.5-VL-3B-Instruct
library_name: transformers
model_name: qwen2.5-dpo_v1
tags:
- generated_from_trainer
- trl
- dpo
licence: license
---
# Model Card for qwen2.5-dpo_v1
This model is a fine-tuned version of [Qwen/Qwen2.5-VL-3B-Instruct](https://huggingface.co/Qwen/Qwen2.5-VL-3B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="zera09/qwen2.5-dpo_v1", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="150" height="24"/>](https://wandb.ai/zeramarveenlyngkhoi/huggingface/runs/ecptbulx)
This model was trained with DPO, a method introduced in [Direct Preference Optimization: Your Language Model is Secretly a Reward Model](https://huggingface.co/papers/2305.18290).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.0.dev0
- Pytorch: 2.6.0
- Datasets: 3.4.1
- Tokenizers: 0.21.1
## Citations
Cite DPO as:
```bibtex
@inproceedings{rafailov2023direct,
title = {{Direct Preference Optimization: Your Language Model is Secretly a Reward Model}},
author = {Rafael Rafailov and Archit Sharma and Eric Mitchell and Christopher D. Manning and Stefano Ermon and Chelsea Finn},
year = 2023,
booktitle = {Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, NeurIPS 2023, New Orleans, LA, USA, December 10 - 16, 2023},
url = {http://papers.nips.cc/paper_files/paper/2023/hash/a85b405ed65c6477a4fe8302b5e06ce7-Abstract-Conference.html},
editor = {Alice Oh and Tristan Naumann and Amir Globerson and Kate Saenko and Moritz Hardt and Sergey Levine},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
RichardErkhov/Gargaz_-_llama3.2_1b-gguf | RichardErkhov | "2025-04-05T01:40:31Z" | 0 | 0 | null | [
"gguf",
"endpoints_compatible",
"region:us",
"conversational"
] | null | "2025-04-05T01:01:57Z" | Quantization made by Richard Erkhov.
[Github](https://github.com/RichardErkhov)
[Discord](https://discord.gg/pvy7H8DZMG)
[Request more models](https://github.com/RichardErkhov/quant_request)
llama3.2_1b - GGUF
- Model creator: https://huggingface.co/Gargaz/
- Original model: https://huggingface.co/Gargaz/llama3.2_1b/
| Name | Quant method | Size |
| ---- | ---- | ---- |
| [llama3.2_1b.Q2_K.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q2_K.gguf) | Q2_K | 1.27GB |
| [llama3.2_1b.IQ3_XS.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.IQ3_XS.gguf) | IQ3_XS | 1.38GB |
| [llama3.2_1b.IQ3_S.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.IQ3_S.gguf) | IQ3_S | 1.44GB |
| [llama3.2_1b.Q3_K_S.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q3_K_S.gguf) | Q3_K_S | 1.44GB |
| [llama3.2_1b.IQ3_M.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.IQ3_M.gguf) | IQ3_M | 1.49GB |
| [llama3.2_1b.Q3_K.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q3_K.gguf) | Q3_K | 1.57GB |
| [llama3.2_1b.Q3_K_M.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q3_K_M.gguf) | Q3_K_M | 1.57GB |
| [llama3.2_1b.Q3_K_L.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q3_K_L.gguf) | Q3_K_L | 1.69GB |
| [llama3.2_1b.IQ4_XS.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.IQ4_XS.gguf) | IQ4_XS | 1.71GB |
| [llama3.2_1b.Q4_0.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q4_0.gguf) | Q4_0 | 1.79GB |
| [llama3.2_1b.IQ4_NL.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.IQ4_NL.gguf) | IQ4_NL | 1.79GB |
| [llama3.2_1b.Q4_K_S.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q4_K_S.gguf) | Q4_K_S | 1.8GB |
| [llama3.2_1b.Q4_K.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q4_K.gguf) | Q4_K | 1.88GB |
| [llama3.2_1b.Q4_K_M.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q4_K_M.gguf) | Q4_K_M | 1.88GB |
| [llama3.2_1b.Q4_1.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q4_1.gguf) | Q4_1 | 1.95GB |
| [llama3.2_1b.Q5_0.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q5_0.gguf) | Q5_0 | 2.11GB |
| [llama3.2_1b.Q5_K_S.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q5_K_S.gguf) | Q5_K_S | 2.11GB |
| [llama3.2_1b.Q5_K.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q5_K.gguf) | Q5_K | 2.16GB |
| [llama3.2_1b.Q5_K_M.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q5_K_M.gguf) | Q5_K_M | 2.16GB |
| [llama3.2_1b.Q5_1.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q5_1.gguf) | Q5_1 | 2.28GB |
| [llama3.2_1b.Q6_K.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q6_K.gguf) | Q6_K | 2.46GB |
| [llama3.2_1b.Q8_0.gguf](https://huggingface.co/RichardErkhov/Gargaz_-_llama3.2_1b-gguf/blob/main/llama3.2_1b.Q8_0.gguf) | Q8_0 | 3.19GB |
Original model description:
---
base_model: unsloth/llama-3.2-3b-instruct-bnb-4bit
tags:
- text-generation-inference
- transformers
- unsloth
- llama
- trl
- sft
license: apache-2.0
language:
- en
---
# Uploaded model
- **Developed by:** Gargaz
- **License:** apache-2.0
- **Finetuned from model :** unsloth/llama-3.2-3b-instruct-bnb-4bit
This llama model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
|
0xzer0day/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-polished_squeaky_rat | 0xzer0day | "2025-04-05T01:39:30Z" | 1 | 0 | transformers | [
"transformers",
"safetensors",
"qwen2",
"text-generation",
"generated_from_trainer",
"rl-swarm",
"grpo",
"gensyn",
"I am polished squeaky rat",
"trl",
"conversational",
"arxiv:2402.03300",
"base_model:Gensyn/Qwen2.5-0.5B-Instruct",
"base_model:finetune:Gensyn/Qwen2.5-0.5B-Instruct",
"autotrain_compatible",
"text-generation-inference",
"endpoints_compatible",
"region:us"
] | text-generation | "2025-04-02T17:46:44Z" | ---
base_model: Gensyn/Qwen2.5-0.5B-Instruct
library_name: transformers
model_name: Qwen2.5-0.5B-Instruct-Gensyn-Swarm-polished_squeaky_rat
tags:
- generated_from_trainer
- rl-swarm
- grpo
- gensyn
- I am polished squeaky rat
- trl
licence: license
---
# Model Card for Qwen2.5-0.5B-Instruct-Gensyn-Swarm-polished_squeaky_rat
This model is a fine-tuned version of [Gensyn/Qwen2.5-0.5B-Instruct](https://huggingface.co/Gensyn/Qwen2.5-0.5B-Instruct).
It has been trained using [TRL](https://github.com/huggingface/trl).
## Quick start
```python
from transformers import pipeline
question = "If you had a time machine, but could only go to the past or the future once and never return, which would you choose and why?"
generator = pipeline("text-generation", model="0xzer0day/Qwen2.5-0.5B-Instruct-Gensyn-Swarm-polished_squeaky_rat", device="cuda")
output = generator([{"role": "user", "content": question}], max_new_tokens=128, return_full_text=False)[0]
print(output["generated_text"])
```
## Training procedure
This model was trained with GRPO, a method introduced in [DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models](https://huggingface.co/papers/2402.03300).
### Framework versions
- TRL: 0.15.2
- Transformers: 4.50.3
- Pytorch: 2.6.0
- Datasets: 3.5.0
- Tokenizers: 0.21.1
## Citations
Cite GRPO as:
```bibtex
@article{zhihong2024deepseekmath,
title = {{DeepSeekMath: Pushing the Limits of Mathematical Reasoning in Open Language Models}},
author = {Zhihong Shao and Peiyi Wang and Qihao Zhu and Runxin Xu and Junxiao Song and Mingchuan Zhang and Y. K. Li and Y. Wu and Daya Guo},
year = 2024,
eprint = {arXiv:2402.03300},
}
```
Cite TRL as:
```bibtex
@misc{vonwerra2022trl,
title = {{TRL: Transformer Reinforcement Learning}},
author = {Leandro von Werra and Younes Belkada and Lewis Tunstall and Edward Beeching and Tristan Thrush and Nathan Lambert and Shengyi Huang and Kashif Rasul and Quentin Gallouédec},
year = 2020,
journal = {GitHub repository},
publisher = {GitHub},
howpublished = {\url{https://github.com/huggingface/trl}}
}
``` |
bowilleatyou/ae36e920-8ddc-43e2-a846-2666e42b3597 | bowilleatyou | "2025-04-05T01:39:18Z" | 0 | 0 | transformers | [
"transformers",
"safetensors",
"unsloth",
"arxiv:1910.09700",
"endpoints_compatible",
"region:us"
] | null | "2025-04-04T19:38:33Z" | ---
library_name: transformers
tags:
- unsloth
---
# Model Card for Model ID
<!-- Provide a quick summary of what the model is/does. -->
## Model Details
### Model Description
<!-- Provide a longer summary of what this model is. -->
This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
- **Developed by:** [More Information Needed]
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Model type:** [More Information Needed]
- **Language(s) (NLP):** [More Information Needed]
- **License:** [More Information Needed]
- **Finetuned from model [optional]:** [More Information Needed]
### Model Sources [optional]
<!-- Provide the basic links for the model. -->
- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]
## Uses
<!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
### Direct Use
<!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
[More Information Needed]
### Downstream Use [optional]
<!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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### Out-of-Scope Use
<!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
[More Information Needed]
## Bias, Risks, and Limitations
<!-- This section is meant to convey both technical and sociotechnical limitations. -->
[More Information Needed]
### Recommendations
<!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
## How to Get Started with the Model
Use the code below to get started with the model.
[More Information Needed]
## Training Details
### Training Data
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[More Information Needed]
### Training Procedure
<!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
#### Preprocessing [optional]
[More Information Needed]
#### Training Hyperparameters
- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
#### Speeds, Sizes, Times [optional]
<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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## Evaluation
<!-- This section describes the evaluation protocols and provides the results. -->
### Testing Data, Factors & Metrics
#### Testing Data
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#### Factors
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[More Information Needed]
#### Metrics
<!-- These are the evaluation metrics being used, ideally with a description of why. -->
[More Information Needed]
### Results
[More Information Needed]
#### Summary
## Model Examination [optional]
<!-- Relevant interpretability work for the model goes here -->
[More Information Needed]
## Environmental Impact
<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
- **Hardware Type:** [More Information Needed]
- **Hours used:** [More Information Needed]
- **Cloud Provider:** [More Information Needed]
- **Compute Region:** [More Information Needed]
- **Carbon Emitted:** [More Information Needed]
## Technical Specifications [optional]
### Model Architecture and Objective
[More Information Needed]
### Compute Infrastructure
[More Information Needed]
#### Hardware
[More Information Needed]
#### Software
[More Information Needed]
## Citation [optional]
<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
**BibTeX:**
[More Information Needed]
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[More Information Needed]
## Glossary [optional]
<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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## Model Card Contact
[More Information Needed] |
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