Instructions to use dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1 with Transformers:

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

pipe = pipeline("text-generation", model="dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1")
model = AutoModelForCausalLM.from_pretrained("dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1", device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1

SGLang

How to use dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1 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 "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1 with Docker Model Runner:
```
docker model run hf.co/dopamineaddict/qwen3-0.6b-chess-sft-aicrowd-v1
```

qwen3-0.6b-chess-sft-aicrowd-v1 / README.md

dopamineaddict

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metadata

language:
  - en
license: apache-2.0
tags:
  - chess
  - reinforcement-learning
  - supervised-fine-tuning
  - lora
  - trl
  - transformers
  - qwen
pipeline_tag: text-generation
base_model: Qwen/Qwen3-0.6B
datasets:
  - Lichess/chess-puzzles

dopamineaddict/qwen3-0.6b-chess-sft-v1

This model is a LoRA fine-tuned variant of Qwen/Qwen3-0.6B, trained to output chess moves in UCI format wrapped in <uci_move>...</uci_move> along with a short <rationale>...</rationale> explanation.
It was created as a baseline submission model for the AIcrowd Global Chess Challenge 2025.

Intended use

Intended for chess move selection in settings where the input includes:
- A FEN position.
- The side to move.
- A list of legal moves in UCI.
Intended output format:
- <uci_move>{legal_uci_move}</uci_move>
- <rationale>{one-sentence explanation}</rationale>

How to use

Example (Transformers):

from transformers import AutoTokenizer, AutoModelForCausalLM
import torch, re

MODEL_ID = "dopamineaddict/qwen3-0.6b-chess-sft-v1"
tok = AutoTokenizer.from_pretrained(MODEL_ID, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(MODEL_ID, torch_dtype=torch.float16, device_map="auto")
model.eval()

prompt = """Position (FEN): <YOUR_FEN>
Side to move: White
Legal moves: e2e4, g1f3, ...

Return exactly:
<uci_move>...</uci_move>
<rationale>...</rationale>
"""

inputs = tok(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
out = model.generate(**inputs, max_new_tokens=64, do_sample=False, temperature=0.0)

text = tok.decode(out, skip_special_tokens=False)
m = re.search(r"<uci_move>\s*([^<\s]+)\s*</uci_move>", text)
print("model_output:", text)
print("uci_move:", m.group(1) if m else None)

Training data

Primary dataset: Lichess/chess-puzzles.
Construction approach:
- The puzzle FEN was converted into a “player-to-move” position by applying the first UCI move from the puzzle move list.
- The supervised label was the next UCI move in the puzzle solution sequence.
Additional fields stored during data prep (varies by run): legal moves list, puzzle metadata, and optional Stockfish annotations.

Training procedure

Method: supervised fine-tuning (SFT) with LoRA adapters.
Trainer: TRL SFTTrainer.
Objective: maximize format compliance and legality (always emit a legal UCI move wrapped in <uci_move> tags).
Hardware: single NVIDIA T4 GPU (typical for initial iteration).

Evaluation

This model was primarily validated via:

Format compliance checks (presence of <uci_move> and parsable UCI).
Legality checks (output move exists in the provided legal moves list for that position).
Quick qualitative checks on held-out puzzle positions.

No Elo or engine-match benchmark is reported for this baseline version.

Limitations and biases

The model is trained mostly on tactical puzzle positions and may be weaker in quiet/opening positions.
The <rationale> field may be repetitive or generic in this version; it is not optimized for explanation quality.
This is not a substitute for a full chess engine and can still choose suboptimal moves.

License and attribution

Base model: Qwen/Qwen3-0.6B (see upstream license and terms).
Puzzle data: derived from the Lichess puzzles dataset.