Instructions to use lucasjin/LLava-Qwen-1_8B-Base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use lucasjin/LLava-Qwen-1_8B-Base with Transformers:

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

pipe = pipeline("text-generation", model="lucasjin/LLava-Qwen-1_8B-Base")

# Load model directly
from transformers import AutoProcessor, AutoModelForCausalLM

processor = AutoProcessor.from_pretrained("lucasjin/LLava-Qwen-1_8B-Base")
model = AutoModelForCausalLM.from_pretrained("lucasjin/LLava-Qwen-1_8B-Base")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lucasjin/LLava-Qwen-1_8B-Base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "lucasjin/LLava-Qwen-1_8B-Base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucasjin/LLava-Qwen-1_8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/lucasjin/LLava-Qwen-1_8B-Base

SGLang

How to use lucasjin/LLava-Qwen-1_8B-Base 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 "lucasjin/LLava-Qwen-1_8B-Base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucasjin/LLava-Qwen-1_8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "lucasjin/LLava-Qwen-1_8B-Base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "lucasjin/LLava-Qwen-1_8B-Base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use lucasjin/LLava-Qwen-1_8B-Base with Docker Model Runner:
```
docker model run hf.co/lucasjin/LLava-Qwen-1_8B-Base
```

Their non-commercial research license applies.

I used this script to make the model and used the tokenizer of CausalLM, as suggested in the comments of the script.

https://github.com/hiyouga/LLaMA-Factory/blob/main/tests/llamafy_qwen.py

Downloads last month: 10

Safetensors

Model size

2B params

Tensor type

F16