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license: apache-2.0
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## Achieving Superior Performance over Qwen3-32B and QwQ-32B Using Only 800 Strategically Curated Samples


### Model description
NTele-R1-32B-V1 is the continuation of [NTele-R1-32B-Preview](https://huggingface.co/ZTE-AIM/NTele-R1-32B-Preview), you can visit for more information. We have made great improvements on the base by using less corpus **in mathematics and code (only 800 items, including 400 mathematics and 400 codes)**, and surpassed the industry's advanced models **Qwen3-32B and QwQ-32B**.
| Model |Release Date | AIME2024 | AIME2025 | MATH500 | GPQA-Diamond | LCB（24.08-25.02） | 
|-------|-------|-------|-------|-------|-------|-------|
| DeepSeek-R1-Distill-Qwen-32B | 25.1.20 | 64.17 | 55.21 | 89.8 | 62.1 | 50.26 |
| QwQ-32B | 25.3.6 | 76.25 | 67.30 | 94.6 | 63.6 | 60.94 |
| Qwen3-32B(think) | 25.4.29 | 78.75 | 73.33 | 95 | **69.7** | 53.24 |
| NTele-R1-32B-V1(ours) | 25.5.10 | **82.5**| **74.49** | **95.2** | 67.17 | **63.69** |


### Data 

[\[🤗 Codemath400\]](https://huggingface.co/datasets/ZTE-AIM/NTele-R1-Data) 

You can access our [dataset](https://huggingface.co/datasets/ZTE-AIM/NTele-R1-Data) to get 800 training data and visit the [NTele-R1-32B-Preview](https://huggingface.co/ZTE-AIM/NTele-R1-32B-Preview) to learn about the data synthesis and screening process.



### Evaluation
We evaluate models with [SkyThought](https://github.com/NovaSky-AI/SkyThought). 

### Training Details
NTele-R1-32B-V1 was trained from DeepSeek-32B-Distill on 8xH800.

#### Training hyperparameter
- learning_rate: 1e-05
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 6
- total_train_batch_size: 48
- total_eval_batch_size: 48
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10.0