Instructions to use LLM-course/chess_endless with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/chess_endless with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM-course/chess_endless")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM-course/chess_endless", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LLM-course/chess_endless with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-course/chess_endless" # 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/chess_endless", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-course/chess_endless
- SGLang
How to use LLM-course/chess_endless 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/chess_endless" \ --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/chess_endless", "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/chess_endless" \ --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/chess_endless", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-course/chess_endless with Docker Model Runner:
docker model run hf.co/LLM-course/chess_endless
Chess Challenge submission by mohamedamine6
Browse files- README.md +2 -2
- config.json +1 -2
- model.safetensors +2 -2
README.md
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## Submission Info
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- **Submitted by**: [mohamedamine6](https://huggingface.co/mohamedamine6)
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- **Parameters**:
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- **Organization**: LLM-course
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## Model Details
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- **Architecture**: Chess Transformer (GPT-style)
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- **Vocab size**: 1682
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- **Embedding dim**: 128
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- **Layers**:
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- **Heads**: 4
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## Submission Info
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- **Submitted by**: [mohamedamine6](https://huggingface.co/mohamedamine6)
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- **Parameters**: 877,056
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- **Organization**: LLM-course
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## Model Details
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- **Architecture**: Chess Transformer (GPT-style)
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- **Vocab size**: 1682
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- **Embedding dim**: 128
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- **Layers**: 4
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- **Heads**: 4
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config.json
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"dropout": 0.1,
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"dtype": "float32",
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"eos_token_id": 2,
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"latent_dim": 64,
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"layer_norm_epsilon": 1e-05,
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"model_type": "chess_transformer",
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"n_ctx": 256,
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"n_embd": 128,
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"n_head": 4,
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"n_inner": 384,
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"n_layer":
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"pad_token_id": 0,
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"tie_weights": true,
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"transformers_version": "4.57.5",
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"dropout": 0.1,
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"dtype": "float32",
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"eos_token_id": 2,
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"layer_norm_epsilon": 1e-05,
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"model_type": "chess_transformer",
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"n_ctx": 256,
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"n_embd": 128,
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"n_head": 4,
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"n_inner": 384,
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"n_layer": 4,
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"pad_token_id": 0,
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"tie_weights": true,
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"transformers_version": "4.57.5",
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model.safetensors
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version https://git-lfs.github.com/spec/v1
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oid sha256:
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size
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version https://git-lfs.github.com/spec/v1
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oid sha256:ead363064a9b493a35cb63830758b33f9476a657fa7e0242c514d9e6191c7385
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size 3512544
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