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:
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
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 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.