Update README.md

README.md

@@ -1,199 +1,169 @@
 ---
+language:
+- en
+- ko
+license: cc-by-nc-4.0
 library_name: transformers
-tags: []
+tags:
+- mergekit
+- merge
+base_model:
+- mistral-community/pixtral-12b
+pipeline_tag: image-text-to-text
 ---
 
-# Model Card for Model ID
+# Pixtral-12b-korean-preview
 
-<!-- Provide a quick summary of what the model is/does. -->
+Finetunned with korean, english data for improving korean performance.
 
+# Model Card for Model ID
 
+Merged model using [mergekit](https://github.com/arcee-ai/mergekit/tree/main/mergekit)
+
+This model hasn't been fully tested, so your feedback will be invaluable in improving it.
+
+## Merge Format
+
+```yaml
+models:
+  - model: spow12/Pixtral-12b-korean-base
+    layer_range: [0, 40]
+  - model: mistral-community/pixtral-12b
+    layer_range: [0, 40]
+merge_method: slerp
+base_model: mistral-community/pixtral-12b
+parameters:
+  t:
+    - filter: self_attn
+      value: [0, 0.5, 0.3, 0.7, 1]
+    - filter: mlp
+      value: [1, 0.5, 0.7, 0.3, 0]
+    - value: 0.5 # fallback for rest of tensors
+dtype: bfloat16
+```
 
 ## 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]
+- **Developed by:** spow12(yw_nam)
+- **Shared by :** spow12(yw_nam)
+- **Model type:** LLaVA
+- **Language(s) (NLP):** Korean, English
+- **Finetuned from model :** [mistral-community/pixtral-12b](https://huggingface.co/mistral-community/pixtral-12b)
+
+## Usage
+
+### Single image inference 
 
-## Model Card Contact
+![image](https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcSXVmCeFm5GRrciuGCM502uv9xXVSrS9zDJZ1umCfoMero2MLxT)
 
-[More Information Needed]
+```python
+from transformers import AutoProcessor, AutoModelForVision2Seq
+from PIL import Image
+
+model_id =  'spow12/Pixtral-12b-korean-preview'
+model = AutoModelForVision2Seq.from_pretrained(
+    model_id, 
+    device_map='auto', 
+    torch_dtype = torch.bfloat16, 
+).eval()
+model.tie_weights()
+processor = AutoProcessor.from_pretrained(model_id)
+
+system = "You are helpful assistant create by Yw nam"
+
+
+chat = [
+    {
+        'content': system,
+        'role': 'system'
+    },
+    {
+        "role": "user", "content": [
+        {"type": "image"},  
+        {"type": "text", "content": "이 이미지에 나와있는 풍경을 설명해줘"}, 
+        ]
+    }
+]
+url = "https://encrypted-tbn2.gstatic.com/images?q=tbn:ANd9GcSXVmCeFm5GRrciuGCM502uv9xXVSrS9zDJZ1umCfoMero2MLxT"
+image = Image.open(requests.get(url, stream=True).raw)
+
+images = [[image]]
+prompt = processor.apply_chat_template(chat, tokenize=False)
+
+inputs = processor(text=prompt, images=images, return_tensors="pt").to(model.device)
+generate_ids = model.generate(**inputs, max_new_tokens=500,do_sample=True,min_p=0.1, temperature=0.9)
+output = processor.batch_decode(generate_ids, skip_special_tokens=True,clean_up_tokenization_spaces=False)
+print(output[0])
+
+#Output
+"""이 이미지는 바위 해안에 위치한 작은 섬에 위치한 고요한 해안 경치를 보여줍니다. 이 섬은 푸른 물로 둘러싸여 있으며, 그 위에는 붉은 지붕이 있는 하얀 등대가 서 있습니다. 등대는 섬의 중앙에 위치해 있으며, 바위 절벽과 연결된 돌다리가 이어져 있어 접근할 수 있습니다. 등대 주변의 바위 절벽은 파도가 부딪히며 장면에 역동적인 요소를 더합니다. 등대 너머로는 하늘이 맑고 푸르며, 전체적인 장면은 평화롭고 고요한 분위기를 자아냅니다."""
+```
+
+### Multi image inference
+
+
+<p align="center">
+  <img src="https://cloud.shopback.com/c_fit,h_750,w_750/store-service-tw/assets/20185/0476e480-b6c3-11ea-b541-2ba549204a69.png"  width="300" style="display:inline-block;"/>
+  <img src="https://pbs.twimg.com/profile_images/1268196215587397634/sgD5ZWuO_400x400.png"  width="300" style="display:inline-block;"/>
+</p>
+
+```python
+url_apple = "https://cloud.shopback.com/c_fit,h_750,w_750/store-service-tw/assets/20185/0476e480-b6c3-11ea-b541-2ba549204a69.png"
+image_1 = Image.open(requests.get(url_apple, stream=True).raw)
+url_microsoft = "https://pbs.twimg.com/profile_images/1268196215587397634/sgD5ZWuO_400x400.png"
+image_2 = Image.open(requests.get(url_microsoft, stream=True).raw)
+chat = [
+    {
+        'content': system,
+        'role': 'system'
+    },
+    {
+        "role": "user", "content": [
+        {"type": "image"},  
+        {"type": "image"},  
+        {"type": "text", "content": "두 기업에 대해서 아는걸 설명해줘."}, 
+        ]
+    }
+]
+
+images = [[image_1, image_2] ]
+prompt = processor.apply_chat_template(chat, tokenize=False)
+inputs = processor(text=prompt, images=images, return_tensors="pt").to(model.device)
+generate_ids = model.generate(**inputs, max_new_tokens=1024, do_sample=True, temperature=0.7, min_p=0.1)
+output = processor.batch_decode(generate_ids, skip_special_tokens=True,clean_up_tokenization_spaces=False)
+print(output[0])
+
+
+#Output
+"""두 기업은 각각 Apple과 Microsoft입니다.
+
+1. 애플:
+애플은 1976년에 스티브 잡스, 스티브 워즈니악, 로널드 웨인에게 설립된 미국의 다국적 기술 기업입니다. 애플의 주요 제품으로는 iPhone, iPad, Mac, Apple Watch가 있습니다. 이 회사는 혁신적인 디자인, 사용자 친화적인 인터페이스, 고품질의 하드웨어로 유명합니다. 애플은 또한 Apple Music, iCloud, App Store와 같은 다양한 소프트웨어 서비스와 플랫폼을 제공합니다. 애플은 혁신적인 제품과 강력한 브랜드로 잘 알려져 있으며, 2010년대 이후 세계에서 가장 가치 있는 기업 중 하나로 자리매김했습니다.
+
+2. 마이크로소프트:
+마이크로소프트는 1975년에 빌 게이츠와 폴 알렌에 의해 설립된 미국의 다국적 기술 기업입니다. 이 회사는 운영 체제, 소프트웨어, 개인용 컴퓨터, 전자제품 개발에 중점을 둡니다. 마이크로소프트의 주요 제품으로는 Windows 운영 체제, Microsoft Office 제품군, Xbox 게임 콘솔이 있습니다. 이 회사는 소프트웨어 개발, 클라우드 컴퓨팅, 인공지능 연구와 같은 분야에서도 중요한 역할을 하고 있습니다. 마이크로소프트는 혁신적인 기술과 강력한 비즈니스 솔루션으로 잘 알려져 있으며, 세계에서 가장 가치 있는 기업 중 하나로 자리매김했습니다"""
+```
+
+## Limitation
+
+Overall, the performance seems reasonable.
+
+However, it declines when processing images with languages other than English. 
+
+This is likely because the model was trained primarily on English text and landscapes. 
+
+Adding Korean data in the future is expected to enhance performance.
+
+## Citation
+
+```bibtex
+@misc {spow12/Pixtral-12b-korean-preview,
+    author       = { YoungWoo Nam },
+    title        = { spow12/Pixtral-12b-korean-preview },
+    year         = 2024,
+    url          = { https://huggingface.co/spow12/Pixtral-12b-korean-preview },
+    publisher    = { Hugging Face }
+}
+```