Text Generation
Transformers
PyTorch
RefinedWebModel
custom_code
text-generation-inference
4-bit precision
awq
Instructions to use casperhansen/falcon-7b-awq with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use casperhansen/falcon-7b-awq with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="casperhansen/falcon-7b-awq", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("casperhansen/falcon-7b-awq", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use casperhansen/falcon-7b-awq with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "casperhansen/falcon-7b-awq" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "casperhansen/falcon-7b-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/casperhansen/falcon-7b-awq
- SGLang
How to use casperhansen/falcon-7b-awq 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 "casperhansen/falcon-7b-awq" \ --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": "casperhansen/falcon-7b-awq", "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 "casperhansen/falcon-7b-awq" \ --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": "casperhansen/falcon-7b-awq", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use casperhansen/falcon-7b-awq with Docker Model Runner:
docker model run hf.co/casperhansen/falcon-7b-awq
- Xet hash:
- 0bfc3b9c134c1843db79da7059ad472c4f7b690c437f44f31841d2c8778bcaf9
- Size of remote file:
- 4.16 GB
- SHA256:
- 14c34e693c0ef1e123f7726ff50a6dff05458df0a3757c2a3661bd52e22ad5f6
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