---
library_name: peft
license: mit
base_model: klyang/MentaLLaMA-chat-7B-hf
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: MentaLLaMA-chat-7B-PsyCourse-fold4
  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. -->

# MentaLLaMA-chat-7B-PsyCourse-fold4

This model is a fine-tuned version of [klyang/MentaLLaMA-chat-7B-hf](https://huggingface.co/klyang/MentaLLaMA-chat-7B-hf) on the course-train-fold4 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0289

## 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
- gradient_accumulation_steps: 16
- total_train_batch_size: 16
- 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: 5.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.796         | 0.0763 | 50   | 0.6145          |
| 0.1534        | 0.1527 | 100  | 0.1050          |
| 0.0619        | 0.2290 | 150  | 0.0629          |
| 0.0535        | 0.3053 | 200  | 0.0550          |
| 0.0576        | 0.3816 | 250  | 0.0468          |
| 0.0501        | 0.4580 | 300  | 0.0469          |
| 0.0456        | 0.5343 | 350  | 0.0423          |
| 0.0487        | 0.6106 | 400  | 0.0392          |
| 0.0543        | 0.6870 | 450  | 0.0389          |
| 0.0298        | 0.7633 | 500  | 0.0349          |
| 0.0378        | 0.8396 | 550  | 0.0347          |
| 0.0387        | 0.9159 | 600  | 0.0329          |
| 0.0275        | 0.9923 | 650  | 0.0352          |
| 0.0348        | 1.0686 | 700  | 0.0339          |
| 0.0346        | 1.1449 | 750  | 0.0338          |
| 0.0321        | 1.2213 | 800  | 0.0337          |
| 0.0288        | 1.2976 | 850  | 0.0333          |
| 0.028         | 1.3739 | 900  | 0.0359          |
| 0.0277        | 1.4502 | 950  | 0.0330          |
| 0.0202        | 1.5266 | 1000 | 0.0334          |
| 0.0291        | 1.6029 | 1050 | 0.0343          |
| 0.0402        | 1.6792 | 1100 | 0.0321          |
| 0.0268        | 1.7556 | 1150 | 0.0328          |
| 0.0411        | 1.8319 | 1200 | 0.0320          |
| 0.0262        | 1.9082 | 1250 | 0.0289          |
| 0.0318        | 1.9845 | 1300 | 0.0292          |
| 0.0159        | 2.0609 | 1350 | 0.0306          |
| 0.019         | 2.1372 | 1400 | 0.0313          |
| 0.022         | 2.2135 | 1450 | 0.0314          |
| 0.0133        | 2.2899 | 1500 | 0.0329          |
| 0.0134        | 2.3662 | 1550 | 0.0317          |
| 0.0185        | 2.4425 | 1600 | 0.0299          |
| 0.0229        | 2.5188 | 1650 | 0.0308          |
| 0.0224        | 2.5952 | 1700 | 0.0307          |
| 0.0159        | 2.6715 | 1750 | 0.0309          |
| 0.0185        | 2.7478 | 1800 | 0.0314          |
| 0.0203        | 2.8242 | 1850 | 0.0304          |
| 0.0159        | 2.9005 | 1900 | 0.0317          |
| 0.0229        | 2.9768 | 1950 | 0.0304          |
| 0.0087        | 3.0531 | 2000 | 0.0329          |
| 0.0073        | 3.1295 | 2050 | 0.0336          |
| 0.0068        | 3.2058 | 2100 | 0.0346          |
| 0.0099        | 3.2821 | 2150 | 0.0367          |
| 0.0124        | 3.3585 | 2200 | 0.0352          |
| 0.0087        | 3.4348 | 2250 | 0.0360          |
| 0.0131        | 3.5111 | 2300 | 0.0354          |
| 0.0143        | 3.5874 | 2350 | 0.0362          |
| 0.0066        | 3.6638 | 2400 | 0.0361          |
| 0.0093        | 3.7401 | 2450 | 0.0372          |
| 0.0074        | 3.8164 | 2500 | 0.0362          |
| 0.0081        | 3.8928 | 2550 | 0.0366          |
| 0.0088        | 3.9691 | 2600 | 0.0370          |
| 0.0052        | 4.0454 | 2650 | 0.0377          |
| 0.0053        | 4.1217 | 2700 | 0.0390          |
| 0.0025        | 4.1981 | 2750 | 0.0402          |
| 0.0027        | 4.2744 | 2800 | 0.0414          |
| 0.0076        | 4.3507 | 2850 | 0.0418          |
| 0.0022        | 4.4271 | 2900 | 0.0428          |
| 0.0036        | 4.5034 | 2950 | 0.0434          |
| 0.003         | 4.5797 | 3000 | 0.0432          |
| 0.002         | 4.6560 | 3050 | 0.0434          |
| 0.0078        | 4.7324 | 3100 | 0.0435          |
| 0.0033        | 4.8087 | 3150 | 0.0436          |
| 0.0055        | 4.8850 | 3200 | 0.0436          |
| 0.006         | 4.9614 | 3250 | 0.0436          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3