README.md

---
inference: false
license: apache-2.0
---

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# LLaVA Model Card

## SGLang
This contains the necessary files to run LLaVA-1.6 34B on SGLang. You can run the server with the following command:

`python -m sglang.launch_server --model-path dillonlaird/hf-llava-v1.6-34b --port 30000`

There seems to be issues with the chat formatting when using the sglang interface so I recommend querying the server directly and formatting the string yourself:

```python
import requests
from transformers import AutoTokenizer


def generate(image_path: str, prompt: str, tokenizer):
    chat = [
        {"role": "system", "content": "Answer the question."},
        {"role": "user", "content": "<image>\n" + prompt},
    ]
    chat_str = tokenizer.apply_chat_template(chat, tokenize=False)
    chat_str += "<|img_start|>assistant\n"
    sampling_params = {"temperature": 0.2, "max_new_tokens": 1536}
    res = requests.post(
        "http://localhost:30000/generate",
        json={
            "text": chat_str,
            "image_data": image_path,
            "sampling_params": sampling_params,
        },
    )
    return res.json()["text"]


if __name__ == "__main__":
    tokenizer = AutoTokenizer.from_pretrained("liuhaotian/llava-v1.6-34b")
    image_path = "path/to/image.jpg"
    prompt = "What is the name of the mountain?"
    desc = generate(image_path, prompt, tokenizer)
```

## Model details

**Model type:**
LLaVA is an open-source chatbot trained by fine-tuning LLM on multimodal instruction-following data.
It is an auto-regressive language model, based on the transformer architecture.
Base LLM: [NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B)

**Model date:**
LLaVA-v1.6-34B was trained in December 2023.

**Paper or resources for more information:**
https://llava-vl.github.io/

## License
[NousResearch/Nous-Hermes-2-Yi-34B](https://huggingface.co/NousResearch/Nous-Hermes-2-Yi-34B) license.

**Where to send questions or comments about the model:**
https://github.com/haotian-liu/LLaVA/issues

## Intended use
**Primary intended uses:**
The primary use of LLaVA is research on large multimodal models and chatbots.

**Primary intended users:**
The primary intended users of the model are researchers and hobbyists in computer vision, natural language processing, machine learning, and artificial intelligence.

## Training dataset
- 558K filtered image-text pairs from LAION/CC/SBU, captioned by BLIP.
- 158K GPT-generated multimodal instruction-following data.
- 500K academic-task-oriented VQA data mixture.
- 50K GPT-4V data mixture.
- 40K ShareGPT data.

## Evaluation dataset
A collection of 12 benchmarks, including 5 academic VQA benchmarks and 7 recent benchmarks specifically proposed for instruction-following LMMs.