Instructions to use Multi-Domain-Expert-Learning/falcon1b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Multi-Domain-Expert-Learning/falcon1b with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Multi-Domain-Expert-Learning/falcon1b", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Multi-Domain-Expert-Learning/falcon1b", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Multi-Domain-Expert-Learning/falcon1b with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Multi-Domain-Expert-Learning/falcon1b" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Multi-Domain-Expert-Learning/falcon1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Multi-Domain-Expert-Learning/falcon1b
- SGLang
How to use Multi-Domain-Expert-Learning/falcon1b 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 "Multi-Domain-Expert-Learning/falcon1b" \ --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": "Multi-Domain-Expert-Learning/falcon1b", "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 "Multi-Domain-Expert-Learning/falcon1b" \ --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": "Multi-Domain-Expert-Learning/falcon1b", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Multi-Domain-Expert-Learning/falcon1b with Docker Model Runner:
docker model run hf.co/Multi-Domain-Expert-Learning/falcon1b
Ontocord.AI commited on
Commit ·
3dcb594
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Parent(s): e5d8fa2
Update config.json
Browse files- config.json +5 -5
config.json
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "tiiuae/falcon-7b--configuration_falcon.FalconConfig",
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"AutoModel": "tiiuae/falcon-7b--
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"AutoModelForCausalLM": "tiiuae/falcon-7b--
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"AutoModelForQuestionAnswering": "tiiuae/falcon-7b--
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"AutoModelForSequenceClassification": "tiiuae/falcon-7b--
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"AutoModelForTokenClassification": "tiiuae/falcon-7b--
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},
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"bias": false,
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"bos_token_id": 11,
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "tiiuae/falcon-7b--configuration_falcon.FalconConfig",
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"AutoModel": "tiiuae/falcon-7b--modeling_falcon.FalconModel",
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"AutoModelForCausalLM": "tiiuae/falcon-7b--modeling_falcon.FalconForCausalLM",
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"AutoModelForQuestionAnswering": "tiiuae/falcon-7b--modeling_falcon.FalconForQuestionAnswering",
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"AutoModelForSequenceClassification": "tiiuae/falcon-7b--modeling_falcon.FalconForSequenceClassification",
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"AutoModelForTokenClassification": "tiiuae/falcon-7b--modeling_falcon.FalconForTokenClassification"
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},
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"bias": false,
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"bos_token_id": 11,
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