Instructions to use 0xrushi/chesslfm-experiments with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use 0xrushi/chesslfm-experiments with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="0xrushi/chesslfm-experiments")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("0xrushi/chesslfm-experiments", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use 0xrushi/chesslfm-experiments with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "0xrushi/chesslfm-experiments"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "0xrushi/chesslfm-experiments",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/0xrushi/chesslfm-experiments

SGLang

How to use 0xrushi/chesslfm-experiments 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 "0xrushi/chesslfm-experiments" \
    --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": "0xrushi/chesslfm-experiments",
		"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 "0xrushi/chesslfm-experiments" \
        --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": "0xrushi/chesslfm-experiments",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use 0xrushi/chesslfm-experiments with Docker Model Runner:
```
docker model run hf.co/0xrushi/chesslfm-experiments
```

Chess language-model experiments

Five chess language-model checkpoints and evaluation summaries from experiments in 0xrushi/ChessLFM. These checkpoints are provided for research and reproducibility.

Model lineage: LiquidAI/LFM2.5-230M-Base → public mlabonne/LFM2.5-230M-Chess → four experiments in this repository. The upstream ChessLFM checkpoint records LiquidAI's base model in its surgery_meta.json. Our four experimental checkpoints use the released ChessLFM weights as their direct starting point, so they descend from LiquidAI's base through ChessLFM. They were not trained from random initialization or directly from the LiquidAI base weights. The released-baseline/ folder is the upstream ChessLFM reference, not a model trained by this project.

Checkpoints and observed results

Rank by highest observed score	Folder	Checkpoint	Match score	W/D/L	Opening block	Descriptive Elo*
1	`released-baseline/`	Released ChessLFM baseline	17.5/32	12/11/9	Later (16–31)	~2033
2	`branch-ranking-treatment/`	Branch-ranking treatment	15.5/32	12/7/13	Later (16–31)	~1989
3	`branch-ranking-control/`	Matched branch-ranking control	13/32	5/16/11	Later (16–31)	~1934
4 (tie)	`self-play-refresh/`	Self-play refresh, round 1	12/32	9/6/17	Initial (0–15)	~1911
4 (tie)	`action-value-finetune/`	Action-value fine-tune, standard search	12/32	10/4/18	Initial (0–15)	~1911

*The Elo column applies the logistic score transform relative to Stockfish configured at Elo 2000. It is descriptive only: 8–32 games per condition gives substantial uncertainty. Opening blocks differ, so ranks across blocks are only an ordering of the highest observed scores, not a controlled cross-model comparison. In the later block, the paired bootstrap interval for branch-ranking treatment versus control was [−4.0, +9.5] points; it includes no advantage. See the detailed experiment report.

What is included

Each folder contains Transformers model weights, model configuration, the chess tokenizer, and token_ids.json. Training recovery states, optimizer files, raw datasets, and match journals are not included. Use the project code for the chess-specific 80-token prompt, legal-move masking, and depth-three search; these weights alone are not a complete chess engine.

Example loading one checkpoint:

from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "0xrushi/chesslfm-experiments"
subfolder = "released-baseline"
tokenizer = AutoTokenizer.from_pretrained(repo, subfolder=subfolder)
model = AutoModelForCausalLM.from_pretrained(repo, subfolder=subfolder)

License

The model weights are derivatives of LiquidAI/LFM2.5-230M-Base through the upstream ChessLFM checkpoint. They are distributed under the LFM Open License v1.0, including its commercial-use threshold. The repository's training code is separately available under MIT in the GitHub project.

References

Upstream chess model: mlabonne/LFM2.5-230M-Chess
Base model and license: LiquidAI/LFM2.5-230M-Base
ChessLFM article: ChessLFM: 2000 Elo in 230M params

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Model tree for 0xrushi/chesslfm-experiments

Base model

LiquidAI/LFM2.5-230M-Base

Finetuned

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