---
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-fold5
  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-fold5

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-fold5 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0295

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8836        | 0.0758 | 50   | 0.6510          |
| 0.1276        | 0.1517 | 100  | 0.1150          |
| 0.0848        | 0.2275 | 150  | 0.0731          |
| 0.0545        | 0.3033 | 200  | 0.0569          |
| 0.0542        | 0.3791 | 250  | 0.0499          |
| 0.0466        | 0.4550 | 300  | 0.0510          |
| 0.0517        | 0.5308 | 350  | 0.0468          |
| 0.058         | 0.6066 | 400  | 0.0456          |
| 0.0521        | 0.6825 | 450  | 0.0405          |
| 0.0317        | 0.7583 | 500  | 0.0382          |
| 0.0281        | 0.8341 | 550  | 0.0390          |
| 0.0388        | 0.9100 | 600  | 0.0388          |
| 0.0459        | 0.9858 | 650  | 0.0355          |
| 0.0277        | 1.0616 | 700  | 0.0368          |
| 0.0342        | 1.1374 | 750  | 0.0369          |
| 0.0323        | 1.2133 | 800  | 0.0337          |
| 0.0257        | 1.2891 | 850  | 0.0351          |
| 0.0218        | 1.3649 | 900  | 0.0346          |
| 0.0266        | 1.4408 | 950  | 0.0377          |
| 0.0344        | 1.5166 | 1000 | 0.0322          |
| 0.0244        | 1.5924 | 1050 | 0.0315          |
| 0.0227        | 1.6682 | 1100 | 0.0332          |
| 0.0243        | 1.7441 | 1150 | 0.0318          |
| 0.03          | 1.8199 | 1200 | 0.0311          |
| 0.0307        | 1.8957 | 1250 | 0.0295          |
| 0.0344        | 1.9716 | 1300 | 0.0305          |
| 0.0214        | 2.0474 | 1350 | 0.0307          |
| 0.0178        | 2.1232 | 1400 | 0.0320          |
| 0.0167        | 2.1991 | 1450 | 0.0321          |
| 0.0115        | 2.2749 | 1500 | 0.0325          |
| 0.0192        | 2.3507 | 1550 | 0.0318          |
| 0.0233        | 2.4265 | 1600 | 0.0327          |
| 0.0108        | 2.5024 | 1650 | 0.0340          |
| 0.0256        | 2.5782 | 1700 | 0.0315          |
| 0.019         | 2.6540 | 1750 | 0.0300          |
| 0.0205        | 2.7299 | 1800 | 0.0302          |
| 0.0197        | 2.8057 | 1850 | 0.0307          |
| 0.0161        | 2.8815 | 1900 | 0.0303          |
| 0.0235        | 2.9573 | 1950 | 0.0302          |
| 0.01          | 3.0332 | 2000 | 0.0301          |
| 0.0073        | 3.1090 | 2050 | 0.0325          |
| 0.0099        | 3.1848 | 2100 | 0.0337          |
| 0.0085        | 3.2607 | 2150 | 0.0337          |
| 0.0076        | 3.3365 | 2200 | 0.0354          |
| 0.0077        | 3.4123 | 2250 | 0.0341          |
| 0.0107        | 3.4882 | 2300 | 0.0338          |
| 0.006         | 3.5640 | 2350 | 0.0338          |
| 0.0127        | 3.6398 | 2400 | 0.0336          |
| 0.0099        | 3.7156 | 2450 | 0.0338          |
| 0.014         | 3.7915 | 2500 | 0.0337          |
| 0.0129        | 3.8673 | 2550 | 0.0339          |
| 0.0118        | 3.9431 | 2600 | 0.0350          |
| 0.0073        | 4.0190 | 2650 | 0.0346          |
| 0.0048        | 4.0948 | 2700 | 0.0357          |
| 0.0059        | 4.1706 | 2750 | 0.0373          |
| 0.0053        | 4.2464 | 2800 | 0.0373          |
| 0.0045        | 4.3223 | 2850 | 0.0381          |
| 0.0054        | 4.3981 | 2900 | 0.0388          |
| 0.0085        | 4.4739 | 2950 | 0.0385          |
| 0.0066        | 4.5498 | 3000 | 0.0384          |
| 0.0051        | 4.6256 | 3050 | 0.0386          |
| 0.0052        | 4.7014 | 3100 | 0.0388          |
| 0.0065        | 4.7773 | 3150 | 0.0389          |
| 0.0036        | 4.8531 | 3200 | 0.0391          |
| 0.0039        | 4.9289 | 3250 | 0.0391          |


### Framework versions

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