library_name, license, base_model, tags, model-index, language, datasets

library_name	license	base_model	tags	model-index	language	datasets
transformers	apache-2.0	Qwen/Qwen2.5-7B-Instruct	llama-factory full generated_from_trainer	name results original	en	bespokelabs/Bespoke-Stratos-17k

Model description

This model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct on the Bespoke-Stratos-17k dataset. 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. 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 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