Instructions to use teddyoas/mathcode-llm with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use teddyoas/mathcode-llm with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="teddyoas/mathcode-llm")# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("teddyoas/mathcode-llm", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use teddyoas/mathcode-llm with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "teddyoas/mathcode-llm" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "teddyoas/mathcode-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/teddyoas/mathcode-llm
- SGLang
How to use teddyoas/mathcode-llm 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 "teddyoas/mathcode-llm" \ --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": "teddyoas/mathcode-llm", "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 "teddyoas/mathcode-llm" \ --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": "teddyoas/mathcode-llm", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use teddyoas/mathcode-llm with Docker Model Runner:
docker model run hf.co/teddyoas/mathcode-llm
Download checkpoint/latest.pt from teddyoas/mathcode-llm: direct link, hf CLI and curl.
- Browser
- Download file 1.32 GB
-
https://huggingface.co/teddyoas/mathcode-llm/resolve/main/checkpoint/latest.pt
- Command line
-
hf download hf://teddyoas/mathcode-llm/checkpoint/latest.pt
-
curl -L -o latest.pt https://huggingface.co/teddyoas/mathcode-llm/resolve/main/checkpoint/latest.pt
1.32 GB
- Xet hash:
- b1c2e4fdacd25b226c1c2b2c6c0040bc27355fbd3a1cf0e3f1b778234e7ce1ef
- Size of remote file:
- 1.32 GB
- SHA256:
- 11f3d39acb9a1f6203cae3fb1d18266a96faf681cb1c83ae3edf37209d37c486
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