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---
license: llama3
library_name: xtuner
datasets:
- Lin-Chen/ShareGPT4V
pipeline_tag: image-text-to-text
---
---
**Notice:** This repository hosts the [`xtuner/llava-llama-3-8b-v1_1-hf`](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf) model, which has been specifically modified to address compatibility issues with the pure `transformers` library. The original model configuration and index files have been manually adjusted to ensure seamless integration and functionality with the `transformers` setup. These adjustments have not altered the model weights.
---
## QuickStart
Running with pure `transformers` library
```python
from transformers import (
LlavaProcessor,
LlavaForConditionalGeneration,
)
import torch
from PIL import Image
import requests
MODEL_NAME = "Seungyoun/llava-llama-3-8b-hf"
processor = LlavaProcessor.from_pretrained(MODEL_NAME)
# add 128257 <image> , <pad>
processor.tokenizer.add_tokens(["<|image|>", "<pad>"], special_tokens=True)
model = LlavaForConditionalGeneration.from_pretrained(MODEL_NAME).to("cuda:0")
# resize embeddings
model.resize_token_embeddings(len(processor.tokenizer))
# prepare image and text prompt, using the appropriate prompt template
url = "https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcTd4g61TSw890IYKBbPMgXPyWAKdVOpWWUAF0-FGzgX2Q&s"
image = Image.open(requests.get(url, stream=True).raw)
prompt = "A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions. USER: <|image|>\nWhat is shown in this image? ASSISTANT:" # FIX : Chat template
inputs = processor(prompt, image, return_tensors="pt").to("cuda:0")
# autoregressively complete prompt
output = model.generate(**inputs, max_new_tokens=100)
print(processor.decode(output[0], skip_special_tokens=True))
# What is shown in this image? ASSISTANT: The image shows a heartwarming scene of two dogs sitting together on a couch. The dogs are of different breeds, one being a golden retriever and the other being a tabby cat. The dogs are sitting close together, indicating a strong bond between them. The image captures a beautiful moment of companionship between two different species. sit on couch. golden retriever and tabby cat. dogs are sitting together. companionship between two different species.
```
---
</div>
## Model
llava-llama-3-8b-v1_1-hf 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 [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).
## 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 |
## 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}
}
``` |