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--- |
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datasets: |
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- liuhaotian/LLaVA-Pretrain |
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- liuhaotian/LLaVA-Instruct-150K |
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pipeline_tag: image-text-to-text |
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library_name: xtuner |
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--- |
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<div align="center"> |
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<img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/> |
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[](https://github.com/InternLM/xtuner) |
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</div> |
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## Model |
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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). |
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**Note: This model is in official LLaVA format.** |
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Resources: |
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- GitHub: [xtuner](https://github.com/InternLM/xtuner) |
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- Official LLaVA format model: [xtuner/llava-llama-3-8b-hf](https://huggingface.co/xtuner/llava-llama-3-8b-hf) |
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- XTuner LLaVA format model: [xtuner/llava-llama-3-8b](https://huggingface.co/xtuner/llava-llama-3-8b) |
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## Details |
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| Model | Visual Encoder | Projector | Resolution | Pretraining Strategy | Fine-tuning Strategy | Pretrain Dataset | Fine-tune Dataset | |
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| :-------------------- | ------------------: | --------: | ---------: | ---------------------: | ------------------------: | ------------------------: | -----------------------: | |
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| LLaVA-v1.5-7B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Frozen ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | |
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| LLaVA-Llama-3-8B | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | LLaVA-PT (558K) | LLaVA-Mix (665K) | |
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| LLaVA-Llama-3-8B-v1.1 | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, LoRA ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) | |
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## Results |
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<div align="center"> |
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<img src="https://github.com/InternLM/xtuner/assets/36994684/a157638c-3500-44ed-bfab-d8d8249f91bb" alt="Image" width=500" /> |
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</div> |
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| Model | MMBench Test (EN) | MMBench Test (CN) | CCBench Dev | MMMU Val | SEED-IMG | AI2D Test | ScienceQA Test | HallusionBench aAcc | POPE | GQA | TextVQA | MME | MMStar | |
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| :-------------------- | :---------------: | :---------------: | :---------: | :-------: | :------: | :-------: | :------------: | :-----------------: | :--: | :--: | :-----: | :------: | :----: | |
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| 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 | |
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| 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 | |
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| 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 | |
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## QuickStart |
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### Chat by `pipeline` |
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```python |
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from transformers import pipeline |
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from PIL import Image |
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import requests |
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model_id = "xtuner/llava-llama-3-8b-transformers" |
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pipe = pipeline("image-to-text", model=model_id, device=0) |
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url = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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image = Image.open(requests.get(url, stream=True).raw) |
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prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>" |
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"<|start_header_id|>assistant<|end_header_id|>\n\n") |
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outputs = pipe(image, prompt=prompt, generate_kwargs={"max_new_tokens": 200}) |
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print(outputs) |
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>>> [{'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...'}] |
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``` |
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### Chat by pure `transformers` |
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```python |
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import requests |
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from PIL import Image |
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import torch |
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from transformers import AutoProcessor, LlavaForConditionalGeneration |
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model_id = "xtuner/llava-llama-3-8b-transformers" |
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prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\nWhat are these?<|eot_id|>" |
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"<|start_header_id|>assistant<|end_header_id|>\n\n") |
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image_file = "http://images.cocodataset.org/val2017/000000039769.jpg" |
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model = LlavaForConditionalGeneration.from_pretrained( |
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model_id, |
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torch_dtype=torch.float16, |
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low_cpu_mem_usage=True, |
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).to(0) |
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processor = AutoProcessor.from_pretrained(model_id) |
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raw_image = Image.open(requests.get(image_file, stream=True).raw) |
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inputs = processor(prompt, raw_image, return_tensors='pt').to(0, torch.float16) |
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output = model.generate(**inputs, max_new_tokens=200, do_sample=False) |
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print(processor.decode(output[0][2:], skip_special_tokens=True)) |
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>>> These are two cats lying on a pink blanket or bed, possibly on a couch... |
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``` |
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### Reproduce |
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Please refer to [docs](https://github.com/InternLM/xtuner/tree/main/xtuner/configs/llava/phi3_mini_4k_instruct_clip_vit_large_p14_336#readme). |
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## Citation |
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```bibtex |
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@misc{2023xtuner, |
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title={XTuner: A Toolkit for Efficiently Fine-tuning LLM}, |
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author={XTuner Contributors}, |
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howpublished = {\url{https://github.com/InternLM/xtuner}}, |
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year={2023} |
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} |
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``` |
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