Small Model Learnability Gap: Models
Collection
24 items • Updated • 2
MATH_training_Qwen_QwQ_32B_Preview
This model is a fine-tuned version of meta-llama/Llama-3.3-70B-Instruct on the MATH_training_Qwen_QwQ_32B_Preview dataset. It achieves the following results on the evaluation set:
- Loss: 0.2988
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: 1
- eval_batch_size: 1
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 4
- total_eval_batch_size: 4
- 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: 2.0
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 0.3436 | 0.4338 | 200 | 0.3342 |
| 0.3048 | 0.8677 | 400 | 0.3144 |
| 0.2566 | 1.3015 | 600 | 0.3054 |
| 0.3036 | 1.7354 | 800 | 0.2994 |
Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3
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Model tree for UWNSL/Llama3.3_70B_Instruct_Long_CoT_lora
Base model
meta-llama/Llama-3.1-70B Finetuned
meta-llama/Llama-3.3-70B-Instruct