Create README.md

README.md

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+---
+datasets:
+- liuhaotian/LLaVA-Pretrain
+- liuhaotian/LLaVA-Instruct-150K
+pipeline_tag: image-text-to-text
+library_name: xtuner
+---
+
+<div align="center">
+  <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>
+
+
+[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)
+
+
+</div>
+
+## Model
+
+llava-llama-3-8b-transformers is a LLaVA model fine-tuned from [meta-llama/Meta-Llama-3-8B-Instruct](https://huggingface.co/meta-llama/Meta-Llama-3-8B-Instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [LLaVA-Pretrain](https://huggingface.co/datasets/liuhaotian/LLaVA-Pretrain) and [LLaVA-Instruct](https://huggingface.co/datasets/liuhaotian/LLaVA-Instruct-150K) by [XTuner](https://github.com/InternLM/xtuner).
+
+**Note: This model is in official LLaVA format.**
+
+Resources:
+
+- GitHub: [xtuner](https://github.com/InternLM/xtuner)
+- Official LLaVA format model: [xtuner/llava-llama-3-8b-hf](https://huggingface.co/xtuner/llava-llama-3-8b-hf)
+- XTuner LLaVA format model: [xtuner/llava-llama-3-8b](https://huggingface.co/xtuner/llava-llama-3-8b)
+
+
+## Details
+
+| Model                 | Visual      Encoder | Projector | Resolution |   Pretraining Strategy | Fine-tuning      Strategy |      Pretrain     Dataset |    Fine-tune     Dataset |
+| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: |
+| LLaVA-v1.5-7B         |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |      Full LLM, Frozen ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B      |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT |       LLaVA-PT     (558K) |     LLaVA-Mix     (665K) |
+| LLaVA-Llama-3-8B-v1.1 |              CLIP-L |       MLP |        336 | Frozen LLM, Frozen ViT |        Full LLM, LoRA ViT | ShareGPT4V-PT     (1246K) | InternVL-SFT     (1268K) |
+
+## Results
+
+<div  align="center">
+<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" />
+</div>
+
+| Model                 | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU  Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA  | TextVQA |   MME    | MMStar |
+| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: |
+| LLaVA-v1.5-7B         |       66.5        |       59.0        |    27.5     |   35.3    |   60.5   |   54.8    |      70.4      |        44.9         | 85.9 | 62.0 |  58.2   | 1511/348 |  30.3  |
+| LLaVA-Llama-3-8B      |       68.9        |       61.6        |    30.4     |   36.8    |   69.8   |   60.9    |      73.3      |        47.3         | 87.2 | 63.5 |  58.0   | 1506/295 |  38.2  |
+| LLaVA-Llama-3-8B-v1.1 |       72.3        |       66.4        |    31.6     |   36.8    |   70.1   |   70.0    |      72.9      |        47.7         | 86.4 | 62.6 |  59.0   | 1469/349 |  45.1  |
+
+
+## QuickStart
+
+
+### Chat by `pipeline`
+
+
+```python
+from transformers import pipeline
+from PIL import Image    
+import requests
+
+model_id = "xtuner/llava-llama-3-8b-transformers"
+pipe = pipeline("image-to-text", model=model_id, device=0)
+url = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+image = Image.open(requests.get(url, stream=True).raw)
+prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
+          "<|start_header_id|>assistant<|end_header_id|>\n\n")
+outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200})
+print(outputs)
+>>> [{'generated_text': 'user\n\n\nWhat are these?assistant\n\nThese are two cats lying on a pink blanket or bed, possibly on a couch...'}]
+```
+
+### Chat by pure `transformers`
+
+```python
+import requests
+from PIL import Image
+
+import torch
+from transformers import AutoProcessor, LlavaForConditionalGeneration
+
+model_id = "xtuner/llava-llama-3-8b-transformers"
+
+prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>"
+          "<|start_header_id|>assistant<|end_header_id|>\n\n")
+image_file = "http://images.cocodataset.org/val2017/000000039769.jpg"
+
+model = LlavaForConditionalGeneration.from_pretrained(
+    model_id, 
+    torch_dtype=torch.float16, 
+    low_cpu_mem_usage=True, 
+).to(0)
+
+processor = AutoProcessor.from_pretrained(model_id)
+
+
+raw_image = Image.open(requests.get(image_file, stream=True).raw)
+inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16)
+
+output = model.generate(**inputs, max_new_tokens=200, do_sample=False)
+print(processor.decode(output[0][2:], skip_special_tokens=True))
+>>> These are two cats lying on a pink blanket or bed, possibly on a couch...
+```
+
+
+### Reproduce
+
+Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme).
+
+
+## Citation
+
+```bibtex
+@misc{2023xtuner,
+    title={XTuner: A Toolkit for Efficiently Fine-tuning LLM},
+    author={XTuner Contributors},
+    howpublished = {\url{https://github.com/InternLM/xtuner}},
+    year={2023}
+}
+```