Qwen2.5-0.5B-Open-R1-Distill-FactThink-SFT
This model is a fine-tuned version of jdqqjr/Qwen2.5-0.5B-Open-R1-Distill on the cot_fact_think_sft_train dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.0001
- train_batch_size: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- total_eval_batch_size: 32
- 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
- num_epochs: 6.0
- mixed_precision_training: Native AMP
Training results
Framework versions
- Transformers 4.47.0
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.21.0
- Downloads last month
- 38
Inference Providers
NEW
This model is not currently available via any of the supported Inference Providers.
Model tree for jdqqjr/Qwen2.5-0.5B-DS-R1-Distill-FactThink-Boxed
Base model
Qwen/Qwen2.5-0.5B
Finetuned
Qwen/Qwen2.5-0.5B-Instruct
Finetuned
jdqqjr/Qwen2.5-0.5B-Open-R1-Distill