Update README.md

README.md

@@ -1,199 +1,78 @@
 ---
 library_name: transformers
-tags: []
+license: cc-by-nc-sa-4.0
+language:
+- ko
+- en
 ---
 
-# Model Card for Model ID
+# Llama-3-KoEn-8B-xtuner-llava-preview 🌋
 
 <!-- Provide a quick summary of what the model is/does. -->
 
-
+Llama-3-KoEn-8B-xtuner-llava-preview 🌋 is Korean based MutliModal based on Llava architecture, merged with [ChatVector](https://arxiv.org/abs/2310.04799) methods leveraging 2 models: 
+1) [beomi/Llama-3-KoEn-8B-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-preview),
+2) [xtuner/llava-llama-3-8b-transformers](https://huggingface.co/xtuner/llava-llama-3-8b-transformers)
 
 ## 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. -->
+- **Developed by:** Junbum Lee (Beomi)
+- **Model type:** HuggingFace Llava 🌋
+- **Language(s) (NLP):** Korean, English
+- **License:** cc-by-nc-sa-4.0 under Llama3 License
+- **Merged from model:** [beomi/Llama-3-KoEn-8B-preview](https://huggingface.co/beomi/Llama-3-KoEn-8B-preview) / [xtuner/llava-llama-3-8b-transformers](https://huggingface.co/xtuner/llava-llama-3-8b-transformers)
 
 ### 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]
+![Cat walking on frozen Han-River, Seoul](https://cdn-uploads.huggingface.co/production/uploads/5e56829137cb5b49818287ea/NWfoArWI4UPAxpEnolkwT.jpeg)
 
-## Citation [optional]
+```python
+import requests
+from PIL import Image
 
-<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
 
-**BibTeX:**
+model_id = "beomi/Llama-3-KoEn-8B-xtuner-llava-preview"
 
-[More Information Needed]
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype='auto', 
+    device_map='auto',
+)
 
-**APA:**
+processor = AutoProcessor.from_pretrained(model_id)
 
-[More Information Needed]
+from transformers import AutoTokenizer
 
-## Glossary [optional]
+tokenizer = AutoTokenizer.from_pretrained('./llava-llama-3-KoEn-8b-v1_1-transformers')
+terminators = [
+    tokenizer.eos_token_id,
+    tokenizer.convert_tokens_to_ids("<|eot_id|>")
+]
 
-<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
+prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\n이 이미지에 대해서 설명해주세요.<|eot_id|>"
+          "<|start_header_id|>assistant<|end_header_id|>\n\n이 이미지에는")
+image_file = "https://cdn-uploads.huggingface.co/production/uploads/5e56829137cb5b49818287ea/NWfoArWI4UPAxpEnolkwT.jpeg"
 
-[More Information Needed]
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
 
-## More Information [optional]
+output = model.generate(**inputs, max_new_tokens=400, do_sample=True, eos_token_id=terminators,)
+print(processor.decode(output[0][2:], skip_special_tokens=False))
 
-[More Information Needed]
+# --- Example Output ---
+user<|end_header_id|>
 
-## Model Card Authors [optional]
+<image>
+이 이미지에 대해서 설명해주세요.<|eot_id|><|start_header_id|>assistant<|end_header_id|>
 
-[More Information Needed]
+이 이미지에는 고양이 한 마리가 강물 위를 걸어가는 모습이 보여집니다. 고양이는 강물의 잔물결에 미끄럼을 타고 강 가로를 지나는 데 능숙하게 보입니다. 고양이의 발은 강물로 잘 들어가, 그것을 즐기며 걸어갑니다. 
 
-## Model Card Contact
+또한 이 이미지도 음성 녹음을 하거나 녹화된 자료로 제작되었으며, 주로 고양이의 모습을 강하게 보여줍니다. 소리 효과도 여러 가지로 추가하여 고양이의 스토리를 다양하게 전달합니다. 강물은 잔물결을 나타내며 강물 위를 걷는 고양이의 모습을 더욱 강렬하게 강조하기 위해 잔물결을 통해 더 디테일한 장면을 보여줍니다.<|eot_id|>
+```
 
-[More Information Needed]