74 lines
2.4 KiB
Markdown
74 lines
2.4 KiB
Markdown
---
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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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datasets:
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- bespokelabs/Bespoke-Stratos-17k
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---
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<p align="center">
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<img src="https://huggingface.co/bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550">
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</p>
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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 [Bespoke-Stratos-17k dataset](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
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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 [Bespoke-Stratos-17k](https://huggingface.co/datasets/bespokelabs/Bespoke-Stratos-17k).
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It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks:
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||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)|
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|AIME2024|20.0|10.0|43.3|55.5|
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|MATH500|82.0|74.2|89.4|92.8|
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|GPQA-Diamond|37.8|33.3|44.9|49.1|
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|LiveCodeBench v2 Easy|71.4|65.9|81.3|-|
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|LiveCodeBench v2 Medium|25.5|18.9|42.2|-|
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|LiveCodeBench v2 Hard|1.6|3.3|2.4|-|
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|LiveCodeBench v2 All|36.1|31.9|46.6|-|
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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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Apache 2.0 License
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## Training procedure
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We used 8xH100 to train the model for 7 hours.
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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 |