--- datasets: - liuhaotian/LLaVA-Pretrain - liuhaotian/LLaVA-Instruct-150K pipeline_tag: image-text-to-text library_name: xtuner ---
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## 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 HuggingFace 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
Image
| 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\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\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} } ```