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--- |
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library_name: transformers |
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license: apache-2.0 |
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base_model: Qwen/Qwen2.5-7B-Instruct |
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tags: |
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- llama-factory |
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- full |
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- generated_from_trainer |
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model-index: |
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- name: original |
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results: [] |
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language: |
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- en |
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--- |
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## Model description |
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This model is a fine-tuned version of [Qwen/Qwen2.5-7B-Instruct](https://huggingface.co./Qwen/Qwen2.5-7B-Instruct) on the [Stratos-R1 dataset](https://huggingface.co./datasets/bespokelabs/stratos-r1). |
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The dataset is derived by distilling DeepSeek-R1 using the data pipeline of Berkeley NovaSky’s Sky-T1 with some modifications. More info in the dataset card at [Stratos-R1 dataset](https://huggingface.co./datasets/bespokelabs/stratos-r1). |
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It outperforms Qwen-2.5-7B-Instruct on reasoning benchmarks: |
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||Bespoke-Stratos-7B|DeepSeek-R1-Distill-Qwen-7B|Qwen2.5-7B-Instruct| |
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|---|---|---|---| |
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|AIME2024|20.0|55.5|10.0| |
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|MATH500|82.0|83.3|74.2| |
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|GPQA-Diamond|37.8|49.1|33.3| |
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|LiveCodeBench|-|37.6|32.9| |
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Note that the authors of Sky-T1 had [noted](https://github.com/NovaSky-AI/SkyThought/issues/4#issuecomment-2585860004) that they saw little or no improvement in training 7B or 14B models with their data. |
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However, see an improvement, though not at the scale of DeepSeek's distilled model. The reason could be that we used 17k examples, while DeepSeek seems to have used 800k. |
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## Intended uses & limitations |
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Non-commercial use. |
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## Training procedure |
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We used 8xH100 to train the model. |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 1e-05 |
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- train_batch_size: 1 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- distributed_type: multi-GPU |
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- num_devices: 8 |
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- gradient_accumulation_steps: 12 |
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- total_train_batch_size: 96 |
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- total_eval_batch_size: 64 |
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- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_ratio: 0.1 |
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- num_epochs: 3.0 |
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### Training results |
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### Framework versions |
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- Transformers 4.46.1 |
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- Pytorch 2.5.1+cu124 |
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- Datasets 3.1.0 |
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- Tokenizers 0.20.3 |
