Instructions to use bk2000/llava-34b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use bk2000/llava-34b with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="bk2000/llava-34b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("bk2000/llava-34b")
model = AutoModelForCausalLM.from_pretrained("bk2000/llava-34b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use bk2000/llava-34b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "bk2000/llava-34b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bk2000/llava-34b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/bk2000/llava-34b

SGLang

How to use bk2000/llava-34b with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "bk2000/llava-34b" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bk2000/llava-34b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "bk2000/llava-34b" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "bk2000/llava-34b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use bk2000/llava-34b with Docker Model Runner:
```
docker model run hf.co/bk2000/llava-34b
```

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.

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