18842194c5
Model: zbeeb/deepseek-r1-distill-qwen-14b-fast-math-r1-sft-10ep Source: Original Platform
125 lines
3.7 KiB
Markdown
125 lines
3.7 KiB
Markdown
---
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license: mit
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base_model: deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
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datasets:
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- RabotniKuma/Fast-Math-R1-SFT
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language:
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- en
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pipeline_tag: text-generation
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tags:
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- deepseek-r1
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- qwen
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- math
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- reasoning
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- supervised-fine-tuning
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- full-parameter-finetune
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- aimo
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- long-context
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library_name: transformers
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---
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# DeepSeek-R1-Distill-Qwen-14B Fast-Math-R1 SFT
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This model is a full-parameter supervised fine-tune of [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B) on [RabotniKuma/Fast-Math-R1-SFT](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-SFT).
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It is intended for math reasoning experiments where the model should show step-by-step reasoning and place the final answer inside `\boxed{}`.
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## Prompt Format
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Use this system prompt:
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```text
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Please reason step by step, and put your final answer within \boxed{}.
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```
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Example chat:
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```python
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messages = [
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{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
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{"role": "user", "content": "Solve the problem here."},
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]
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```
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## Training Details
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| Item | Value |
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|---|---:|
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| Base model | `deepseek-ai/DeepSeek-R1-Distill-Qwen-14B` |
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| Dataset | `RabotniKuma/Fast-Math-R1-SFT` |
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| Training type | Full-parameter SFT |
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| GPUs used | 6 x NVIDIA H200 |
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| Per-device batch size | 1 |
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| Gradient accumulation | 8 |
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| Effective global batch size | 48 |
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| Epochs | 10 |
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| Max sequence length | 24,000 tokens |
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| Packing | Enabled |
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| Learning rate | 1e-5 |
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| Scheduler | Cosine |
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| Precision | bfloat16 |
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| Distributed setup | DeepSpeed ZeRO-3 |
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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.
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## Usage
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_id = "zbeeb/deepseek-r1-distill-qwen-14b-fast-math-r1-sft-10ep"
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tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=True)
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model = AutoModelForCausalLM.from_pretrained(
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model_id,
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torch_dtype="auto",
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device_map="auto",
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trust_remote_code=True,
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)
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messages = [
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{"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."},
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{"role": "user", "content": "What is 17 * 23?"},
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]
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inputs = tokenizer.apply_chat_template(
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messages,
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add_generation_prompt=True,
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return_tensors="pt",
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).to(model.device)
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outputs = model.generate(
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inputs,
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max_new_tokens=2048,
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temperature=0.6,
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top_p=0.95,
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do_sample=True,
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)
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print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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```
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## Intended Use
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This model is intended for:
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- Math reasoning research
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- AIMO-style problem solving experiments
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- Long-context supervised fine-tuning experiments
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- Comparing Fast-Math-R1-style SFT against the base distilled R1 model
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## Limitations
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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.
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The training run used 24k-token sequences, but practical inference context length depends on the serving stack, GPU memory, and runtime configuration.
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## Source Models and Data
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- Base model: [deepseek-ai/DeepSeek-R1-Distill-Qwen-14B](https://huggingface.co/deepseek-ai/DeepSeek-R1-Distill-Qwen-14B)
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- Dataset: [RabotniKuma/Fast-Math-R1-SFT](https://huggingface.co/datasets/RabotniKuma/Fast-Math-R1-SFT)
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## License
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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.
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