Instructions to use hlarcher/falcon-7b-v100s with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use hlarcher/falcon-7b-v100s with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="hlarcher/falcon-7b-v100s", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("hlarcher/falcon-7b-v100s", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use hlarcher/falcon-7b-v100s with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "hlarcher/falcon-7b-v100s" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "hlarcher/falcon-7b-v100s", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/hlarcher/falcon-7b-v100s
- SGLang
How to use hlarcher/falcon-7b-v100s 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 "hlarcher/falcon-7b-v100s" \ --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": "hlarcher/falcon-7b-v100s", "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 "hlarcher/falcon-7b-v100s" \ --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": "hlarcher/falcon-7b-v100s", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use hlarcher/falcon-7b-v100s with Docker Model Runner:
docker model run hf.co/hlarcher/falcon-7b-v100s
- Xet hash:
- 39d58371d367d663a5f8fdf38a8b51cd2f9207dcf6c1fcba47ddb5ada9a8e763
- Size of remote file:
- 4.48 GB
- SHA256:
- 11822397cd22f88cc56344b019cac4f04e4970e3775b7d5fc9263bc9fca8bdb1
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