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README.md
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---
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datasets:
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- Lin-Chen/ShareGPT4V
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pipeline_tag: image-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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[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)
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</div>
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## Model
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llava-phi-3-mini is a LLaVA model fine-tuned from [microsoft/Phi-3-mini-4k-instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) and [CLIP-ViT-Large-patch14-336](https://huggingface.co/openai/clip-vit-large-patch14-336) with [ShareGPT4V-PT](https://huggingface.co/datasets/Lin-Chen/ShareGPT4V) and [InternVL-SFT](https://github.com/OpenGVLab/InternVL/tree/main/internvl_chat#prepare-training-datasets) by [XTuner](https://github.com/InternLM/xtuner).
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**Note: This model is in HuggingFace LLaVA format. The models in xtuner LLaVA format and official LLaVA format can be found on [xtuner/llava-phi-3-mini-xtuner](https://huggingface.co/xtuner/llava-phi-3-mini-xtuner) and [xtuner/llava-phi-3-mini](https://huggingface.co/xtuner/llava-phi-3-mini).**
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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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| LLaVA-Phi-3-mini | CLIP-L | MLP | 336 | Frozen LLM, Frozen ViT | Full LLM, Full ViT | ShareGPT4V-PT (1246K) | InternVL-SFT (1268K) |
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## Results
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## Quickstart
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### Chat with `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-phi-3-mini-hf"
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pipe = pipeline("image-to-text", model=model_id, device=0)
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url = "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers/tasks/ai2d-demo.jpg"
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image = Image.open(requests.get(url, stream=True).raw)
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prompt = "<|user|>\n<image>\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud<|end|>\n<|assistant|>\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': '\nWhat does the label 15 represent? (1) lava (2) core (3) tunnel (4) ash cloud (1) lava'}]
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```
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### Chat with 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-phi-3-mini-hf"
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prompt = "<|user|>\n<image>\nWhat are these?<|end|>\n<|assistant|>\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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>>> What are these? These are two cats sleeping on a pink couch.
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```
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### Reproduction
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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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