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
83.3
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
Runs of bespokelabs Bespoke-Stratos-7B on huggingface.co
486
Total runs
5
24-hour runs
38
3-day runs
17
7-day runs
221
30-day runs
More Information About Bespoke-Stratos-7B huggingface.co Model
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