Text Generation
Transformers
Safetensors
PEFT
lora
sft
trl
qwen3
qwen
qwen3-1.7b
legal
legal-ai
legal-contracts
contract-analysis
contract-review
clause-analysis
clause-classification
clause-extraction
information-extraction
structured-output
json
qlora
4-bit precision
nf4
cuad
contractnli
legalbench
Instructions to use AaronTekle/ClauseQwen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AaronTekle/ClauseQwen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="AaronTekle/ClauseQwen")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AaronTekle/ClauseQwen", device_map="auto") - PEFT
How to use AaronTekle/ClauseQwen with PEFT:
Task type is invalid.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use AaronTekle/ClauseQwen with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "AaronTekle/ClauseQwen" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "AaronTekle/ClauseQwen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/AaronTekle/ClauseQwen
- SGLang
How to use AaronTekle/ClauseQwen 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 "AaronTekle/ClauseQwen" \ --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": "AaronTekle/ClauseQwen", "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 "AaronTekle/ClauseQwen" \ --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": "AaronTekle/ClauseQwen", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use AaronTekle/ClauseQwen with Docker Model Runner:
docker model run hf.co/AaronTekle/ClauseQwen
Welcome to the community
The community tab is the place to discuss and collaborate with the HF community!