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

Name: deepseek-r1-distill-qwen-14b-fast-math-r1-sft-10ep
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DeepSeek-R1-Distill-Qwen-14B Fast-Math-R1 SFT

This model is a full-parameter supervised fine-tune of deepseek-ai/DeepSeek-R1-Distill-Qwen-14B on RabotniKuma/Fast-Math-R1-SFT.

It is intended for math reasoning experiments where the model should show step-by-step reasoning and place the final answer inside \boxed{}.

Prompt Format

Use this system prompt:

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

Example chat:

python

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

Item	Value
Base model	`deepseek-ai/DeepSeek-R1-Distill-Qwen-14B`
Dataset	`RabotniKuma/Fast-Math-R1-SFT`
Training type	Full-parameter SFT
GPUs used	6 x NVIDIA H200
Per-device batch size	1
Gradient accumulation	8
Effective global batch size	48
Epochs	10
Max sequence length	24,000 tokens
Packing	Enabled
Learning rate	1e-5
Scheduler	Cosine
Precision	bfloat16
Distributed setup	DeepSpeed ZeRO-3

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

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.