Instructions to use MaximeMuhlethaler/chess-llm-MaximeMuh with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MaximeMuhlethaler/chess-llm-MaximeMuh with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MaximeMuhlethaler/chess-llm-MaximeMuh")# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("MaximeMuhlethaler/chess-llm-MaximeMuh", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use MaximeMuhlethaler/chess-llm-MaximeMuh with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MaximeMuhlethaler/chess-llm-MaximeMuh" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MaximeMuhlethaler/chess-llm-MaximeMuh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MaximeMuhlethaler/chess-llm-MaximeMuh
- SGLang
How to use MaximeMuhlethaler/chess-llm-MaximeMuh 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 "MaximeMuhlethaler/chess-llm-MaximeMuh" \ --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": "MaximeMuhlethaler/chess-llm-MaximeMuh", "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 "MaximeMuhlethaler/chess-llm-MaximeMuh" \ --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": "MaximeMuhlethaler/chess-llm-MaximeMuh", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MaximeMuhlethaler/chess-llm-MaximeMuh with Docker Model Runner:
docker model run hf.co/MaximeMuhlethaler/chess-llm-MaximeMuh
Upload tokenizer
Browse files- special_tokens_map.json +6 -0
- tokenizer_config.json +44 -0
- vocab.json +6 -0
special_tokens_map.json
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{
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"bos_token": "[BOS]",
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"eos_token": "[EOS]",
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"pad_token": "[PAD]",
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"unk_token": "[UNK]"
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}
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tokenizer_config.json
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{
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"added_tokens_decoder": {
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"0": {
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"content": "[PAD]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"1": {
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"content": "[BOS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"2": {
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"content": "[EOS]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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},
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"3": {
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"content": "[UNK]",
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"lstrip": false,
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"normalized": false,
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"rstrip": false,
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"single_word": false,
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"special": true
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}
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},
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"bos_token": "[BOS]",
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"clean_up_tokenization_spaces": false,
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"eos_token": "[EOS]",
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"extra_special_tokens": {},
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"model_max_length": 1000000000000000019884624838656,
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"pad_token": "[PAD]",
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"tokenizer_class": "ChessTokenizer",
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"unk_token": "[UNK]"
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}
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vocab.json
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{
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"[PAD]": 0,
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"[BOS]": 1,
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"[EOS]": 2,
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"[UNK]": 3
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}
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