Text Generation
Transformers
Safetensors
English
phi
phi-2
electrical engineering
Microsoft
custom_code
text-generation-inference
Instructions to use STEM-AI-mtl/phi-2-electrical-engineering with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use STEM-AI-mtl/phi-2-electrical-engineering with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True) model = AutoModelForCausalLM.from_pretrained("STEM-AI-mtl/phi-2-electrical-engineering", trust_remote_code=True) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use STEM-AI-mtl/phi-2-electrical-engineering with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "STEM-AI-mtl/phi-2-electrical-engineering" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "STEM-AI-mtl/phi-2-electrical-engineering", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/STEM-AI-mtl/phi-2-electrical-engineering
- SGLang
How to use STEM-AI-mtl/phi-2-electrical-engineering 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 "STEM-AI-mtl/phi-2-electrical-engineering" \ --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": "STEM-AI-mtl/phi-2-electrical-engineering", "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 "STEM-AI-mtl/phi-2-electrical-engineering" \ --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": "STEM-AI-mtl/phi-2-electrical-engineering", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use STEM-AI-mtl/phi-2-electrical-engineering with Docker Model Runner:
docker model run hf.co/STEM-AI-mtl/phi-2-electrical-engineering
Update config.json
Browse files- config.json +3 -3
config.json
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"PhiForCausalLM"
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],
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"attention_dropout": 0.0,
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"AutoConfig": "configuration_phi.PhiConfig",
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"AutoModelForCausalLM": "modeling_phi.PhiForCausalLM"
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},
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
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"PhiForCausalLM"
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],
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"attention_dropout": 0.0,
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"auto_map": {
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"AutoConfig": "microsoft/phi-2--configuration_phi.PhiConfig",
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"AutoModelForCausalLM": "microsoft/phi-2--modeling_phi.PhiForCausalLM"
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},
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"bos_token_id": 50256,
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"embd_pdrop": 0.0,
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