---
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-fold7
  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-fold7

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-fold7 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0272

## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8412        | 0.0764 | 50   | 0.6190          |
| 0.1455        | 0.1528 | 100  | 0.1069          |
| 0.0861        | 0.2292 | 150  | 0.0647          |
| 0.0575        | 0.3056 | 200  | 0.0518          |
| 0.0643        | 0.3820 | 250  | 0.0469          |
| 0.0341        | 0.4584 | 300  | 0.0435          |
| 0.0641        | 0.5348 | 350  | 0.0413          |
| 0.0405        | 0.6112 | 400  | 0.0419          |
| 0.0531        | 0.6875 | 450  | 0.0385          |
| 0.041         | 0.7639 | 500  | 0.0372          |
| 0.0283        | 0.8403 | 550  | 0.0353          |
| 0.041         | 0.9167 | 600  | 0.0330          |
| 0.0553        | 0.9931 | 650  | 0.0363          |
| 0.0314        | 1.0695 | 700  | 0.0310          |
| 0.0211        | 1.1459 | 750  | 0.0312          |
| 0.0314        | 1.2223 | 800  | 0.0320          |
| 0.0325        | 1.2987 | 850  | 0.0315          |
| 0.0351        | 1.3751 | 900  | 0.0305          |
| 0.0402        | 1.4515 | 950  | 0.0314          |
| 0.0262        | 1.5279 | 1000 | 0.0299          |
| 0.026         | 1.6043 | 1050 | 0.0302          |
| 0.024         | 1.6807 | 1100 | 0.0314          |
| 0.0487        | 1.7571 | 1150 | 0.0302          |
| 0.0251        | 1.8335 | 1200 | 0.0300          |
| 0.028         | 1.9099 | 1250 | 0.0320          |
| 0.0244        | 1.9862 | 1300 | 0.0299          |
| 0.0211        | 2.0626 | 1350 | 0.0282          |
| 0.019         | 2.1390 | 1400 | 0.0285          |
| 0.012         | 2.2154 | 1450 | 0.0302          |
| 0.0181        | 2.2918 | 1500 | 0.0283          |
| 0.0176        | 2.3682 | 1550 | 0.0288          |
| 0.0136        | 2.4446 | 1600 | 0.0277          |
| 0.0217        | 2.5210 | 1650 | 0.0286          |
| 0.0156        | 2.5974 | 1700 | 0.0294          |
| 0.0191        | 2.6738 | 1750 | 0.0286          |
| 0.0249        | 2.7502 | 1800 | 0.0272          |
| 0.0237        | 2.8266 | 1850 | 0.0290          |
| 0.021         | 2.9030 | 1900 | 0.0278          |
| 0.0174        | 2.9794 | 1950 | 0.0283          |
| 0.0122        | 3.0558 | 2000 | 0.0290          |
| 0.0137        | 3.1322 | 2050 | 0.0301          |
| 0.0086        | 3.2086 | 2100 | 0.0309          |
| 0.0136        | 3.2850 | 2150 | 0.0306          |
| 0.0111        | 3.3613 | 2200 | 0.0310          |
| 0.0142        | 3.4377 | 2250 | 0.0327          |
| 0.0114        | 3.5141 | 2300 | 0.0312          |
| 0.015         | 3.5905 | 2350 | 0.0319          |
| 0.0088        | 3.6669 | 2400 | 0.0300          |
| 0.0068        | 3.7433 | 2450 | 0.0310          |
| 0.0098        | 3.8197 | 2500 | 0.0300          |
| 0.0088        | 3.8961 | 2550 | 0.0298          |
| 0.0081        | 3.9725 | 2600 | 0.0306          |
| 0.0052        | 4.0489 | 2650 | 0.0314          |
| 0.0076        | 4.1253 | 2700 | 0.0326          |
| 0.0091        | 4.2017 | 2750 | 0.0331          |
| 0.0045        | 4.2781 | 2800 | 0.0342          |
| 0.0047        | 4.3545 | 2850 | 0.0347          |
| 0.0047        | 4.4309 | 2900 | 0.0358          |
| 0.005         | 4.5073 | 2950 | 0.0359          |
| 0.0049        | 4.5837 | 3000 | 0.0363          |
| 0.0039        | 4.6600 | 3050 | 0.0363          |
| 0.0062        | 4.7364 | 3100 | 0.0366          |
| 0.0054        | 4.8128 | 3150 | 0.0366          |
| 0.0041        | 4.8892 | 3200 | 0.0366          |
| 0.0047        | 4.9656 | 3250 | 0.0366          |


### Framework versions

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