stablelm-2-1.6-disticoder-v0.1
This model is a fine-tuned version of stabilityai/stablelm-2-1_6b on the argilla/DistiCoder-dpo-binarized dataset. It achieves the following results on the evaluation set:
- Loss: 1.1315
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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 4
- gradient_accumulation_steps: 4
- total_train_batch_size: 128
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
Training results
| Training Loss | Epoch | Step | Validation Loss |
|---|---|---|---|
| 1.7319 | 0.44 | 5 | 1.5441 |
| 1.3425 | 0.89 | 10 | 1.2968 |
| 1.1709 | 1.33 | 15 | 1.2151 |
| 1.0994 | 1.78 | 20 | 1.1605 |
| 1.0287 | 2.22 | 25 | 1.1382 |
| 1.0303 | 2.67 | 30 | 1.1315 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.1.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.2
- Downloads last month
- 16
Inference API (serverless) does not yet support model repos that contain custom code.