Instructions to use dmunteanu-rws/falcon-40b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use dmunteanu-rws/falcon-40b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="dmunteanu-rws/falcon-40b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("dmunteanu-rws/falcon-40b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use dmunteanu-rws/falcon-40b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "dmunteanu-rws/falcon-40b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "dmunteanu-rws/falcon-40b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/dmunteanu-rws/falcon-40b
- SGLang
How to use dmunteanu-rws/falcon-40b 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 "dmunteanu-rws/falcon-40b" \ --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": "dmunteanu-rws/falcon-40b", "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 "dmunteanu-rws/falcon-40b" \ --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": "dmunteanu-rws/falcon-40b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use dmunteanu-rws/falcon-40b with Docker Model Runner:
docker model run hf.co/dmunteanu-rws/falcon-40b
Melissa Roemmele commited on
Commit ·
fbc6b9c
1
Parent(s): 7d19a12
Updated handler.py
Browse files- handler.py +1 -1
handler.py
CHANGED
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@@ -17,9 +17,9 @@ class EndpointHandler:
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device=device)
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", {})
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with torch.autocast(self.pipeline.device.type, dtype=torch.bfloat16):
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outputs = self.pipeline(inputs, **parameters, use_cache=True)
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torch.cuda.empty_cache()
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return outputs
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device=device)
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def __call__(self, data: Dict[str, Any]) -> Dict[str, str]:
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torch.cuda.empty_cache()
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inputs = data.pop("inputs", data)
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parameters = data.pop("parameters", {})
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with torch.autocast(self.pipeline.device.type, dtype=torch.bfloat16):
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outputs = self.pipeline(inputs, **parameters, use_cache=True)
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return outputs
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