yanpeng051821

qwen3-0.6b-openr1-math-sft-s1

README

License: apache-2.0

Training

Table with columns: Field, Value
Field	Value
Base revision	`311c62e88814bff7206909ccd330bab0a784743b`
Train records	61,224
Maximum artifact length	16,384 tokens; longer records excluded, not truncated
Epochs / optimizer steps	1 / 479
Effective batch	128 sequences (`1 x 128` gradient accumulation)
Learning rate	`4e-5`, cosine schedule, 3% warmup
Precision	BF16 compute; full-parameter training
Hardware	1 x NVIDIA A100-SXM4-80GB
Runtime	31,867 seconds (about 8 h 51 min)
Train loss	0.568926

The supervised loss is completion-only causal cross-entropy with global valid-token normalization.

Evaluation

All deltas below compare the frozen base model and S1 under paired contracts.

Table with columns: Evaluation, B0, S1, Delta
Evaluation	B0	S1	Delta
Validation NLL, token weighted	0.780483	0.546614	-29.96% relative
GSM8K QEM, 1,319 samples	0.476118	0.514784	+3.87 pp
59-task regression macro	0.543412	0.528562	-1.49 pp
MMLU 57-task macro	0.545157	0.530376	-1.48 pp

The checkpoint fits the target SFT distribution substantially better and improves GSM8K under the recorded contract, while the broad regression panel declines. Further error analysis is required before selecting a follow-up recipe.

MATH-500 limitation

A 20-question, four-generation smoke test with a 512-token cap was strongly length-truncated: all 80 S1 generations reached the cap. The S1 pass@1:1 value was 0.05 versus 0.25 for B0, but this result mixes answer quality with truncation and is not treated as a formal MATH-500 score.

The original 500-question, four-generation, 32K-token evaluation was stopped after 204/2000 generations because projected paid GPU time exceeded the budget. No full MATH-500 result is reported.

Generation and stop tokens

The saved generation_config.json uses token 151645 (<|im_end|>) as EOS, while the underlying model config.json retains the base token 151643 (<|endoftext|>). Some inference engines do not honor the saved generation config. For bounded generation, explicitly supply both stop strings or their token IDs and set an application-appropriate maximum output length.

python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "yanpeng051821/qwen3-0.6b-openr1-math-sft-s1"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
)

messages = [{"role": "user", "content": "Solve: If 3x + 5 = 20, find x."}]
inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=512,
    eos_token_id=[
        tokenizer.convert_tokens_to_ids("<|im_end|>"),
        tokenizer.convert_tokens_to_ids("<|endoftext|>"),
    ],
)
print(tokenizer.decode(outputs[0][inputs.shape[1]:], skip_special_tokens=True))

Intended use

Use this checkpoint for research on small-model SFT, data and error analysis, generation-length behavior, and reproducible B0/S1 comparisons. It should not be deployed as a general assistant or used where mathematical correctness is safety critical without independent validation.

Reproducibility evidence

The evidence/ directory contains the resolved training configuration, run manifest, compact paired evaluation summaries, the MATH budget-abort record, and SHA-256 checksums. Full optimizer checkpoints, training data, and large per-sample evaluation artifacts are intentionally stored outside this model repo.

Implementation: https://github.com/yanpeng051821/small_model_post_training

Engineering case study: https://github.com/yanpeng051821/AI_research/tree/main/training_engineering/case_studies/qwen3-0.6b-openr1-math-sft

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Model Details

Model Provider

yanpeng051821

Model Tree

Base

Qwen/Qwen3-0.6B-Base

Fine-tuned

this model

Input Modalities

Text

Output Modalities