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zbeeb
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Model Details
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zbeeb
Model Tree
deepseek-ai/DeepSeek-R1-Distill-Qwen-14B
Input Modalities
Output Modalities
Supported Functionality
Use this system prompt:
text
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."},]| 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.
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))This model is intended for:
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.
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.
messages = [ {"role": "system", "content": "Please reason step by step, and put your final answer within \\boxed{}."}, {"role": "user", "content": "Solve the problem here."},]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))