Instructions to use N8Programs/karvonen-chessgpt-50m-mlx-bf16 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use N8Programs/karvonen-chessgpt-50m-mlx-bf16 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("N8Programs/karvonen-chessgpt-50m-mlx-bf16") 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 N8Programs/karvonen-chessgpt-50m-mlx-bf16 with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "N8Programs/karvonen-chessgpt-50m-mlx-bf16" --prompt "Once upon a time"
Karvonen ChessGPT 50M MLX BF16
This repository contains the local MLX/bfloat16 conversion of Adam Karvonen's 16-layer ChessGPT checkpoint used by the ChessGPT negative-control runs in the ICLManyReplication artifact for "Many Next-Token Predictors are In-Context Learners".
The local config records the source as:
- source repo:
adamkarvonen/chess_llms - source checkpoint:
lichess_16layers_ckpt_no_optimizer.pt - source dataset: 7GB Lichess Dataset
- source iteration: 600000
- parameters: 50,888,704
The upload includes the converted model.safetensors, tokenizer files, and the
custom chess_gpt.py model implementation used by the replication harness.
Intended Use
This checkpoint is intended as a drop-in artifact for reproducing the ChessGPT
negative-control row in ICLManyReplication. Inputs are character-level PGN; the
source model notes that prompts should be prefixed with ;, the training
delimiter between games.
- Downloads last month
- 236
Quantized
Model tree for N8Programs/karvonen-chessgpt-50m-mlx-bf16
Base model
adamkarvonen/chess_llms