Instructions to use LLM-course/square_chess with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/square_chess with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM-course/square_chess")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM-course/square_chess", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use LLM-course/square_chess with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-course/square_chess" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "LLM-course/square_chess", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-course/square_chess
- SGLang
How to use LLM-course/square_chess 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 "LLM-course/square_chess" \ --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": "LLM-course/square_chess", "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 "LLM-course/square_chess" \ --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": "LLM-course/square_chess", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-course/square_chess with Docker Model Runner:
docker model run hf.co/LLM-course/square_chess
| library_name: transformers | |
| tags: | |
| - chess | |
| - llm-course | |
| - chess-challenge | |
| license: mit | |
| # square_chess | |
| Chess model submitted to the LLM Course Chess Challenge. | |
| ## Submission Info | |
| - **Submitted by**: [j0eyd](https://huggingface.co/j0eyd) | |
| - **Parameters**: 869,120 | |
| - **Organization**: LLM-course | |
| ## Model Details | |
| - **Architecture**: Chess Transformer (GPT-style) | |
| - **Vocab size**: 72 | |
| - **Embedding dim**: 128 | |
| - **Layers**: 5 | |
| - **Heads**: 4 | |