Prompt Format

Use this system prompt:

text
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Please reason step by step, and put your final answer within \boxed{}.

Example chat:

python
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messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"role": "user", "content": "Solve the problem here."},
]

Training Details

The target recipe was based on the Fast-Math-R1 style training flow from analokmaus/kaggle-aimo2-fast-math-r1, adapted for this model and dataset.

Usage

python
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from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "zbeeb/deepseek-r1-distill-qwen-14b-fast-math-r1-sft-10ep"

tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype="auto",
    device_map="auto",
    trust_remote_code=True,
)

messages = [
    {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
    {"role": "user", "content": "What is 17 * 23?"},
]

inputs = tokenizer.apply_chat_template(
    messages,
    add_generation_prompt=True,
    return_tensors="pt",
).to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=2048,
    temperature=0.6,
    top_p=0.95,
    do_sample=True,
)

print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Intended Use

This model is intended for:

• Math reasoning research
• AIMO-style problem solving experiments
• Long-context supervised fine-tuning experiments
• Comparing Fast-Math-R1-style SFT against the base distilled R1 model

Limitations

This model has not been independently benchmarked in this card. It may produce incorrect reasoning, malformed final answers, or answers that look plausible but are wrong. Validate outputs before using them in any setting where correctness matters.

The training run used 24k-token sequences, but practical inference context length depends on the serving stack, GPU memory, and runtime configuration.

Source Models and Data

• Base model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
• Dataset: RabotniKuma/Fast-Math-R1-SFT

License

The base model is listed on Hugging Face with an MIT license. The training dataset is listed with an Apache-2.0 license. This model card declares MIT for the uploaded fine-tuned model.