Instructions to use TRM-coding/PythonCopilot with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use TRM-coding/PythonCopilot with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="TRM-coding/PythonCopilot")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("TRM-coding/PythonCopilot") model = AutoModelForCausalLM.from_pretrained("TRM-coding/PythonCopilot") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use TRM-coding/PythonCopilot with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "TRM-coding/PythonCopilot" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "TRM-coding/PythonCopilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/TRM-coding/PythonCopilot
- SGLang
How to use TRM-coding/PythonCopilot 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 "TRM-coding/PythonCopilot" \ --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": "TRM-coding/PythonCopilot", "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 "TRM-coding/PythonCopilot" \ --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": "TRM-coding/PythonCopilot", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use TRM-coding/PythonCopilot with Docker Model Runner:
docker model run hf.co/TRM-coding/PythonCopilot
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train.py
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# Load model and tokenizer
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if accelerator.is_main_process:
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hf_repo = Repository("./", clone_from=project_name, revision=run_name,git_email='22301131@bjtu.edu.cn')
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model = GPT2LMHeadModel.from_pretrained("TRM-coding/
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model.gradient_checkpointing=True
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tokenizer = AutoTokenizer.from_pretrained("TRM-coding/Origin-vocab")
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# Load model and tokenizer
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if accelerator.is_main_process:
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hf_repo = Repository("./", clone_from=project_name, revision=run_name,git_email='22301131@bjtu.edu.cn')
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model = GPT2LMHeadModel.from_pretrained("TRM-coding/Model-origin-small")
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model.gradient_checkpointing=True
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tokenizer = AutoTokenizer.from_pretrained("TRM-coding/Origin-vocab")
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