Instructions to use sruly/human-chess-mlx with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use sruly/human-chess-mlx with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("sruly/human-chess-mlx") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use sruly/human-chess-mlx with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "sruly/human-chess-mlx" --prompt "Once upon a time"
- Atomic Chat
Download tokenizer_config.json from sruly/human-chess-mlx: direct link, hf CLI and curl.
- Browser
- Download file 135 Bytes
-
https://huggingface.co/sruly/human-chess-mlx/resolve/main/tokenizer_config.json
- Command line
-
hf download hf://sruly/human-chess-mlx/tokenizer_config.json
-
curl -L -o tokenizer_config.json https://huggingface.co/sruly/human-chess-mlx/resolve/main/tokenizer_config.json
135 Bytes
| { | |
| "bos_token": "BOS", | |
| "eos_token": "EOS", | |
| "model_max_length": 256, | |
| "pad_token": "PAD", | |
| "tokenizer_class": "ChessTokenizer" | |
| } | |