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library_name: transformers
license: mit
base_model: CMU-AIRe/e3-1.7B
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: e3-sft
results: []
---
<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->
# e3-sft
This model is a fine-tuned version of [CMU-AIRe/e3-1.7B](https://huggingface.co/CMU-AIRe/e3-1.7B) on the hardmath_sft_2 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6364
## 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: 1e-07
- train_batch_size: 1
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 32
- total_train_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_with_min_lr
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100.0
### Training results
| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.7025 | 4.0 | 16 | 0.7606 |
| 0.9105 | 8.0 | 32 | 0.7590 |
| 0.8193 | 12.0 | 48 | 0.7550 |
| 0.6939 | 16.0 | 64 | 0.7460 |
| 0.6623 | 20.0 | 80 | 0.7418 |
| 0.8112 | 24.0 | 96 | 0.7389 |
| 0.708 | 28.0 | 112 | 0.7154 |
| 0.6471 | 32.0 | 128 | 0.7097 |
| 0.9019 | 36.0 | 144 | 0.7050 |
| 0.7328 | 40.0 | 160 | 0.7007 |
| 0.8191 | 44.0 | 176 | 0.6938 |
| 0.6327 | 48.0 | 192 | 0.6752 |
| 0.6903 | 52.0 | 208 | 0.6604 |
| 0.7467 | 56.0 | 224 | 0.6533 |
| 0.7364 | 60.0 | 240 | 0.6489 |
| 0.7706 | 64.0 | 256 | 0.6460 |
| 0.7777 | 68.0 | 272 | 0.6441 |
| 0.6391 | 72.0 | 288 | 0.6419 |
| 0.648 | 76.0 | 304 | 0.6408 |
| 0.704 | 80.0 | 320 | 0.6398 |
| 0.6316 | 84.0 | 336 | 0.6387 |
| 0.6232 | 88.0 | 352 | 0.6380 |
| 0.6545 | 92.0 | 368 | 0.6372 |
| 0.7126 | 96.0 | 384 | 0.6364 |
| 0.6465 | 100.0 | 400 | 0.6364 |
### Framework versions
- Transformers 4.55.0
- Pytorch 2.5.1
- Datasets 3.6.0
- Tokenizers 0.21.1
|