---
title: llava-llama-3-8b-v1_1-transformers
canonical_url: "https://www.modelscope.cn/models/AI-ModelScope/llava-llama-3-8b-v1_1-transformers"
md_url: "https://www.modelscope.cn/models/AI-ModelScope/llava-llama-3-8b-v1_1-transformers.md"
repository: AI-ModelScope/llava-llama-3-8b-v1_1-transformers
last_updated: 2024-05-07
pipeline_tag: image-text-to-text
tasks:
  - image-text-to-text
model_type:
  - llava
architectures:
  - LlavaForConditionalGeneration
parameters: 8.4B
tensor_type:
  - F16
  - F32
library_name:
  - pytorch
  - transformer
  - safetensors
frameworks:
  - pytorch
inference_backends:
  - "deploy_task vlm/text/emb"
  - "lmdeploy 0.9.1"
  - "lmdeploy_turbomind 0.9.1"
  - "sglang 0.5.2"
  - "vllm 0.9.2"
downloads: 7941
stars: 2
---

# llava-llama-3-8b-v1_1-transformers

> llava-llama-3-8b-v1_1-transformers - AI-ModelScope 在 ModelScope 开源的模型。llava-llama-3-8b-v11-hf 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.

AI-ModelScope/llava-llama-3-8b-v1_1-transformers 是 ModelScope 魔搭社区上的 8.4B 参数image-text-to-text模型，可用 deploy_task vlm/text/emb、lmdeploy 0.9.1、lmdeploy_turbomind 0.9.1 部署。

- **Repository**: AI-ModelScope/llava-llama-3-8b-v1_1-transformers
- **Tasks**: image-text-to-text
- **Parameters**: 8.4B
- **Inference backends**: deploy_task vlm/text/emb, lmdeploy 0.9.1, lmdeploy_turbomind 0.9.1, sglang 0.5.2, vllm 0.9.2
- **Downloads**: 7941
- **Stars**: 2
- **Last updated**: 2024-05-07

Source: https://www.modelscope.cn/models/AI-ModelScope/llava-llama-3-8b-v1_1-transformers

---

<div align="center">
  <img src="https://github.com/InternLM/lmdeploy/assets/36994684/0cf8d00f-e86b-40ba-9b54-dc8f1bc6c8d8" width="600"/>


[![Generic badge](https://img.shields.io/badge/GitHub-%20XTuner-black.svg)](https://github.com/InternLM/xtuner)


</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).

**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-v1_1-hf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-hf)
- XTuner LLaVA format model: [xtuner/llava-llama-3-8b-v1_1](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1)
- GGUF format model: [xtuner/llava-llama-3-8b-v1_1-gguf](https://huggingface.co/xtuner/llava-llama-3-8b-v1_1-gguf)


## 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  |


## QuickStart


### Chat by `pipeline`


```python
from transformers import pipeline
from PIL import Image    
import requests

model_id = "xtuner/llava-llama-3-8b-v1_1-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<image>\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, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.'}]
```

### 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-v1_1-transformers"

prompt = ("<|start_header_id|>user<|end_header_id|>\n\n<image>\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, one brown and one gray, lying on a pink blanket. sleep. brown and gray cat sleeping on a pink blanket.
```


### 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}
}
```