Article
Tiny Agents: a MCP-powered agent in 50 lines of code
•
172
Andrew Reed
andrewrreed
AI & ML interests
Applied ML, Practical AI, Inference & Deployment, LLMs, Multi-modal Models, RAG
Recent Activity
upvoted
an
article
3 days ago
Tiny Agents: a MCP-powered agent in 50 lines of code
upvoted
an
article
3 days ago
17 Reasons Why Gradio Isn't Just Another UI Library
Organizations
andrewrreed's activity
upvoted
2
articles
3 days ago
Article
17 Reasons Why Gradio Isn't Just Another UI Library
•
25
Remove "NOTE: Hugging Face's Transformers has not been directly supported yet." in README
#216 opened 5 days ago
by
andrewrreed
upvoted
an
article
12 days ago
Article
Cohere on Hugging Face Inference Providers 🔥
•
124
upvoted
a
collection
19 days ago
liked
a
Space
19 days ago
reacted to
jsulz's
post with 🔥
23 days ago
Post
3703
Huge week for
xet-team
as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.
Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.
We expect builders on the Hub to see even more improvements, helping power innovation across the community.
With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.
Thanks to the
meta-llama
team for launching on Xet!
xet-team
as Llama 4 is the first major model on Hugging Face uploaded with Xet providing the backing! Every byte downloaded comes through our infrastructure.Using Xet on Hugging Face is the fastest way to download and iterate on open source models and we've proved it with Llama 4 giving a boost of ~25% across all models.
We expect builders on the Hub to see even more improvements, helping power innovation across the community.
With the models on our infrastructure, we can peer in and see how well our dedupe performs across the Llama 4 family. On average, we're seeing ~25% dedupe, providing huge savings to the community who iterate on these state-of-the-art models. The attached image shows a few selected models and how they perform on Xet.
Thanks to the
meta-llama
team for launching on Xet!
upvoted
a
collection
23 days ago
liked
a
model
23 days ago
upvoted
a
paper
25 days ago
upvoted
an
article
about 1 month ago
Article
The New and Fresh analytics in Inference Endpoints
•
19
updated
a
collection
about 1 month ago
liked
a
Space
about 1 month ago
updated
a
collection
about 1 month ago
liked
a
Space
about 1 month ago
upvoted
an
article
about 1 month ago
Article
Xet is on the Hub
•
47
reacted to
MJannik's
post with 🔥
about 2 months ago
Post
1754
I've published an article showing five ways to use 🪢 Langfuse with 🤗 Hugging Face.
My personal favorite is Method #4: Using Hugging Face Datasets for Langfuse Dataset Experiments. This lets you benchmark your LLM app or AI agent with a dataset hosted on Hugging Face. In this example, I chose the GSM8K dataset ( openai/gsm8k) to test the mathematical reasoning capabilities of my smolagent :)
Link to the Article here on HF: https://huggingface.co/blog/MJannik/hugging-face-and-langfuse
My personal favorite is Method #4: Using Hugging Face Datasets for Langfuse Dataset Experiments. This lets you benchmark your LLM app or AI agent with a dataset hosted on Hugging Face. In this example, I chose the GSM8K dataset ( openai/gsm8k) to test the mathematical reasoning capabilities of my smolagent :)
Link to the Article here on HF: https://huggingface.co/blog/MJannik/hugging-face-and-langfuse