---
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-fold9
  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-fold9

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-fold9 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0305

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8389        | 0.0768 | 50   | 0.6107          |
| 0.1391        | 0.1535 | 100  | 0.1064          |
| 0.0757        | 0.2303 | 150  | 0.0694          |
| 0.0639        | 0.3070 | 200  | 0.0581          |
| 0.0739        | 0.3838 | 250  | 0.0467          |
| 0.0558        | 0.4606 | 300  | 0.0439          |
| 0.0394        | 0.5373 | 350  | 0.0425          |
| 0.0477        | 0.6141 | 400  | 0.0419          |
| 0.047         | 0.6908 | 450  | 0.0432          |
| 0.0416        | 0.7676 | 500  | 0.0376          |
| 0.0637        | 0.8444 | 550  | 0.0395          |
| 0.0377        | 0.9211 | 600  | 0.0357          |
| 0.0315        | 0.9979 | 650  | 0.0361          |
| 0.0332        | 1.0746 | 700  | 0.0360          |
| 0.0322        | 1.1514 | 750  | 0.0352          |
| 0.0321        | 1.2282 | 800  | 0.0333          |
| 0.0299        | 1.3049 | 850  | 0.0323          |
| 0.0286        | 1.3817 | 900  | 0.0340          |
| 0.0266        | 1.4585 | 950  | 0.0332          |
| 0.0296        | 1.5352 | 1000 | 0.0320          |
| 0.022         | 1.6120 | 1050 | 0.0307          |
| 0.0292        | 1.6887 | 1100 | 0.0312          |
| 0.0269        | 1.7655 | 1150 | 0.0330          |
| 0.0204        | 1.8423 | 1200 | 0.0306          |
| 0.0306        | 1.9190 | 1250 | 0.0309          |
| 0.0364        | 1.9958 | 1300 | 0.0314          |
| 0.0194        | 2.0725 | 1350 | 0.0319          |
| 0.0148        | 2.1493 | 1400 | 0.0318          |
| 0.0161        | 2.2261 | 1450 | 0.0305          |
| 0.0293        | 2.3028 | 1500 | 0.0323          |
| 0.0203        | 2.3796 | 1550 | 0.0329          |
| 0.0235        | 2.4563 | 1600 | 0.0327          |
| 0.0234        | 2.5331 | 1650 | 0.0311          |
| 0.0227        | 2.6099 | 1700 | 0.0307          |
| 0.0147        | 2.6866 | 1750 | 0.0313          |
| 0.0202        | 2.7634 | 1800 | 0.0322          |
| 0.0203        | 2.8401 | 1850 | 0.0313          |
| 0.0199        | 2.9169 | 1900 | 0.0310          |
| 0.0152        | 2.9937 | 1950 | 0.0315          |
| 0.0065        | 3.0704 | 2000 | 0.0347          |
| 0.0155        | 3.1472 | 2050 | 0.0345          |
| 0.0087        | 3.2239 | 2100 | 0.0367          |
| 0.0107        | 3.3007 | 2150 | 0.0353          |
| 0.0113        | 3.3775 | 2200 | 0.0377          |
| 0.0115        | 3.4542 | 2250 | 0.0358          |
| 0.0087        | 3.5310 | 2300 | 0.0377          |
| 0.0099        | 3.6078 | 2350 | 0.0374          |
| 0.0075        | 3.6845 | 2400 | 0.0381          |
| 0.0064        | 3.7613 | 2450 | 0.0384          |
| 0.0111        | 3.8380 | 2500 | 0.0382          |
| 0.0154        | 3.9148 | 2550 | 0.0380          |
| 0.0087        | 3.9916 | 2600 | 0.0379          |
| 0.0042        | 4.0683 | 2650 | 0.0392          |
| 0.0029        | 4.1451 | 2700 | 0.0411          |
| 0.0044        | 4.2218 | 2750 | 0.0422          |
| 0.0035        | 4.2986 | 2800 | 0.0430          |
| 0.0031        | 4.3754 | 2850 | 0.0441          |
| 0.004         | 4.4521 | 2900 | 0.0445          |
| 0.0035        | 4.5289 | 2950 | 0.0446          |
| 0.0021        | 4.6056 | 3000 | 0.0454          |
| 0.0041        | 4.6824 | 3050 | 0.0459          |
| 0.006         | 4.7592 | 3100 | 0.0456          |
| 0.0043        | 4.8359 | 3150 | 0.0455          |
| 0.0031        | 4.9127 | 3200 | 0.0456          |
| 0.0073        | 4.9894 | 3250 | 0.0456          |


### Framework versions

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