--- title: llava-llama-3-8b-v1_1 canonical_url: "https://www.modelscope.cn/models/xtuner/llava-llama-3-8b-v1_1" md_url: "https://www.modelscope.cn/models/xtuner/llava-llama-3-8b-v1_1.md" repository: xtuner/llava-llama-3-8b-v1_1 last_updated: 2024-04-24 pipeline_tag: image-text-to-text tasks: - image-text-to-text model_type: - llama architectures: - LlamaForCausalLM parameters: 8.0B tensor_type: - F16 library_name: - safetensors - pytorch frameworks: - Pytorch inference_backends: - "deploy_task text/emb" - "lmdeploy 0.9.1" - "lmdeploy_turbomind 0.9.1" - "sglang 0.5.2" - "vllm 0.9.2" downloads: 424 stars: 5 --- # llava-llama-3-8b-v1_1 > llava-llama-3-8b-v1_1 - xtuner 在 ModelScope 开源的模型。llava-llama-3-8b-v11 is a LLaVA model fine-tuned from meta-llama/Meta-Llama-3-8B-Instruct and CLIP-ViT-Large-patch14-336 with ShareGPT4V-PT and InternVL-SFT by XTuner. xtuner/llava-llama-3-8b-v1_1 是 ModelScope 魔搭社区上的 8.0B 参数image-text-to-text模型，可用 deploy_task text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。 - **Repository**: xtuner/llava-llama-3-8b-v1_1 - **Tasks**: image-text-to-text - **Parameters**: 8.0B - **Inference backends**: deploy_task text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2 - **Downloads**: 424 - **Stars**: 5 - **Last updated**: 2024-04-24 Source: https://www.modelscope.cn/models/xtuner/llava-llama-3-8b-v1_1 ---
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## Model llava-llama-3-8b-v1_1 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
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 ### Installation ```shell pip install 'git+https://github.com/InternLM/xtuner.git#egg=xtuner[deepspeed]' ``` ### Chat ```shell xtuner chat xtuner/llava-llama-3-8b-v1_1 \ --visual-encoder openai/clip-vit-large-patch14-336 \ --llava xtuner/llava-llama-3-8b-v1_1 \ --prompt-template llama3_chat \ --image $IMAGE_PATH ``` ### MMBench Evaluation XTuner integrates the MMBench evaluation, and you can perform evaluations with the following command! ```bash xtuner mmbench xtuner/llava-llama-3-8b-v1_1 \ --visual-encoder openai/clip-vit-large-patch14-336 \ --llava xtuner/llava-llama-3-8b-v1_1 \ --prompt-template llama3_chat \ --data-path $MMBENCH_DATA_PATH \ --work-dir $RESULT_PATH ``` After the evaluation is completed, if it's a development set, it will directly print out the results; If it's a test set, you need to submit `mmbench_result.xlsx` to the official MMBench for final evaluation to obtain precision results! ### Training 1. Pretrain (saved by default in `./work_dirs/llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain/`) ```bash NPROC_PER_NODE=8 xtuner train llava_llama3_8b_instruct_clip_vit_large_p14_336_e1_gpu8_sharegpt4v_pretrain --deepspeed deepspeed_zero2 --seed 1024 ``` 2. Fine-tune (saved by default in `./work_dirs/llava_llama3_8b_instruct_full_clip_vit_large_p14_336_lora_e1_gpu8_internvl_finetune/`) ```bash NPROC_PER_NODE=8 xtuner train llava_llama3_8b_instruct_full_clip_vit_large_p14_336_lora_e1_gpu8_internvl_finetune --deepspeed deepspeed_zero2 --seed 1024 ``` ## 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} } ```