|
--- |
|
library_name: transformers |
|
license: apache-2.0 |
|
base_model: Qwen/Qwen2.5-7B-Instruct |
|
tags: |
|
- llama-factory |
|
- full |
|
- generated_from_trainer |
|
model-index: |
|
- name: original |
|
results: [] |
|
language: |
|
- en |
|
datasets: |
|
- bespokelabs/Bespoke-Stratos-17k |
|
--- |
|
|
|
<p align="center"> |
|
<img src="https://huggingface.co./bespokelabs/Bespoke-MiniCheck-7B/resolve/main/Bespoke-Labs-Logo.png" width="550"> |
|
</p> |
|
|
|
## Model description |
|
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). |
|
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). |
|
It outperforms Qwen-2.5-7B-Instruct on math reasoning benchmarks: |
|
|
|
||Bespoke-Stratos-7B|Qwen2.5-7B-Instruct|DeepSeek-R1-Distill-Qwen-7B (Ours)|DeepSeek-R1-Distill-Qwen-7B (Reported)| |
|
|---|---|---|---|---| |
|
|AIME2024|20.0|10.0|43.3|55.5| |
|
|MATH500|82.0|74.2|89.4|92.8| |
|
|GPQA-Diamond|37.8|33.3|44.9|49.1| |
|
|LiveCodeBench v2 Easy|71.4|65.9|81.3|-| |
|
|LiveCodeBench v2 Medium|25.5|18.9|42.2|-| |
|
|LiveCodeBench v2 Hard|1.6|3.3|2.4|-| |
|
|LiveCodeBench v2 All|36.1|31.9|46.6|-| |
|
|
|
|
|
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. |
|
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. |
|
|
|
## Intended uses & limitations |
|
|
|
Apache 2.0 License |
|
|
|
## Training procedure |
|
We used 8xH100 to train the model for 7 hours. |
|
|
|
### Training hyperparameters |
|
|
|
The following hyperparameters were used during training: |
|
- learning_rate: 1e-05 |
|
- train_batch_size: 1 |
|
- eval_batch_size: 8 |
|
- seed: 42 |
|
- distributed_type: multi-GPU |
|
- num_devices: 8 |
|
- gradient_accumulation_steps: 12 |
|
- total_train_batch_size: 96 |
|
- total_eval_batch_size: 64 |
|
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments |
|
- lr_scheduler_type: cosine |
|
- lr_scheduler_warmup_ratio: 0.1 |
|
- num_epochs: 3.0 |
|
|
|
### Training results |
|
|
|
|
|
|
|
### Framework versions |
|
|
|
- Transformers 4.46.1 |
|
- Pytorch 2.5.1+cu124 |
|
- Datasets 3.1.0 |
|
- Tokenizers 0.20.3 |
