Text Generation
Transformers
Safetensors
chess_transformer
chess
llm-course
chess-challenge
custom_code
Instructions to use LLM-course/chess-aj-split with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use LLM-course/chess-aj-split with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="LLM-course/chess-aj-split", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("LLM-course/chess-aj-split", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use LLM-course/chess-aj-split with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "LLM-course/chess-aj-split" # 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-aj-split", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/LLM-course/chess-aj-split
- SGLang
How to use LLM-course/chess-aj-split 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-aj-split" \ --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-aj-split", "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-aj-split" \ --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-aj-split", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use LLM-course/chess-aj-split with Docker Model Runner:
docker model run hf.co/LLM-course/chess-aj-split
File size: 620 Bytes
db9c3ab | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 | {
"architectures": [
"ChessForCausalLM"
],
"auto_map": {
"AutoConfig": "model.ChessConfig",
"AutoModelForCausalLM": "model.ChessForCausalLM"
},
"bos_token_id": 22,
"dropout": 0.1,
"dtype": "float32",
"eos_token_id": 23,
"hidden_size": 96,
"is_decoder": true,
"model_type": "chess_transformer",
"n_ctx": 512,
"n_embd": 96,
"n_head": 4,
"n_inner": 256,
"n_layer": 10,
"num_attention_heads": 4,
"num_hidden_layers": 10,
"pad_token_id": 24,
"rms_norm_eps": 1e-06,
"tie_weights": true,
"transformers_version": "4.57.6",
"vocab_size": 90
}
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