---
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-fold6
  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-fold6

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-fold6 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0319

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8292        | 0.0751 | 50   | 0.6473          |
| 0.1595        | 0.1502 | 100  | 0.1169          |
| 0.0933        | 0.2254 | 150  | 0.0727          |
| 0.0512        | 0.3005 | 200  | 0.0581          |
| 0.0619        | 0.3756 | 250  | 0.0474          |
| 0.0395        | 0.4507 | 300  | 0.0460          |
| 0.0476        | 0.5258 | 350  | 0.0454          |
| 0.0444        | 0.6009 | 400  | 0.0407          |
| 0.0543        | 0.6761 | 450  | 0.0425          |
| 0.0454        | 0.7512 | 500  | 0.0372          |
| 0.0562        | 0.8263 | 550  | 0.0377          |
| 0.0336        | 0.9014 | 600  | 0.0361          |
| 0.0494        | 0.9765 | 650  | 0.0368          |
| 0.0354        | 1.0516 | 700  | 0.0386          |
| 0.029         | 1.1268 | 750  | 0.0376          |
| 0.0301        | 1.2019 | 800  | 0.0352          |
| 0.0321        | 1.2770 | 850  | 0.0341          |
| 0.0271        | 1.3521 | 900  | 0.0343          |
| 0.0351        | 1.4272 | 950  | 0.0330          |
| 0.0244        | 1.5023 | 1000 | 0.0330          |
| 0.0277        | 1.5775 | 1050 | 0.0341          |
| 0.0231        | 1.6526 | 1100 | 0.0340          |
| 0.0261        | 1.7277 | 1150 | 0.0327          |
| 0.0297        | 1.8028 | 1200 | 0.0348          |
| 0.027         | 1.8779 | 1250 | 0.0334          |
| 0.0417        | 1.9531 | 1300 | 0.0348          |
| 0.0173        | 2.0282 | 1350 | 0.0328          |
| 0.0207        | 2.1033 | 1400 | 0.0323          |
| 0.0223        | 2.1784 | 1450 | 0.0325          |
| 0.0107        | 2.2535 | 1500 | 0.0359          |
| 0.0182        | 2.3286 | 1550 | 0.0332          |
| 0.0187        | 2.4038 | 1600 | 0.0323          |
| 0.018         | 2.4789 | 1650 | 0.0327          |
| 0.0205        | 2.5540 | 1700 | 0.0350          |
| 0.0182        | 2.6291 | 1750 | 0.0323          |
| 0.0202        | 2.7042 | 1800 | 0.0325          |
| 0.0218        | 2.7793 | 1850 | 0.0323          |
| 0.0179        | 2.8545 | 1900 | 0.0319          |
| 0.0213        | 2.9296 | 1950 | 0.0330          |
| 0.0104        | 3.0047 | 2000 | 0.0328          |
| 0.0097        | 3.0798 | 2050 | 0.0359          |
| 0.0103        | 3.1549 | 2100 | 0.0363          |
| 0.0131        | 3.2300 | 2150 | 0.0359          |
| 0.0149        | 3.3052 | 2200 | 0.0362          |
| 0.0083        | 3.3803 | 2250 | 0.0365          |
| 0.0115        | 3.4554 | 2300 | 0.0359          |
| 0.0111        | 3.5305 | 2350 | 0.0387          |
| 0.0094        | 3.6056 | 2400 | 0.0376          |
| 0.0051        | 3.6808 | 2450 | 0.0376          |
| 0.0053        | 3.7559 | 2500 | 0.0375          |
| 0.0078        | 3.8310 | 2550 | 0.0377          |
| 0.0105        | 3.9061 | 2600 | 0.0372          |
| 0.0105        | 3.9812 | 2650 | 0.0371          |
| 0.0064        | 4.0563 | 2700 | 0.0382          |
| 0.0048        | 4.1315 | 2750 | 0.0398          |
| 0.0065        | 4.2066 | 2800 | 0.0407          |
| 0.0031        | 4.2817 | 2850 | 0.0417          |
| 0.0028        | 4.3568 | 2900 | 0.0420          |
| 0.0043        | 4.4319 | 2950 | 0.0421          |
| 0.0048        | 4.5070 | 3000 | 0.0424          |
| 0.0038        | 4.5822 | 3050 | 0.0428          |
| 0.0041        | 4.6573 | 3100 | 0.0430          |
| 0.0066        | 4.7324 | 3150 | 0.0431          |
| 0.003         | 4.8075 | 3200 | 0.0430          |
| 0.0031        | 4.8826 | 3250 | 0.0429          |
| 0.0046        | 4.9577 | 3300 | 0.0430          |


### Framework versions

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