---
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-fold2
  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-fold2

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-fold2 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.7519        | 0.0775 | 50   | 0.6389          |
| 0.1485        | 0.1550 | 100  | 0.1124          |
| 0.074         | 0.2326 | 150  | 0.0650          |
| 0.0655        | 0.3101 | 200  | 0.0619          |
| 0.0598        | 0.3876 | 250  | 0.0512          |
| 0.0414        | 0.4651 | 300  | 0.0449          |
| 0.0427        | 0.5426 | 350  | 0.0414          |
| 0.0471        | 0.6202 | 400  | 0.0387          |
| 0.0433        | 0.6977 | 450  | 0.0362          |
| 0.0432        | 0.7752 | 500  | 0.0353          |
| 0.0445        | 0.8527 | 550  | 0.0353          |
| 0.0529        | 0.9302 | 600  | 0.0353          |
| 0.0313        | 1.0078 | 650  | 0.0318          |
| 0.0301        | 1.0853 | 700  | 0.0322          |
| 0.0289        | 1.1628 | 750  | 0.0338          |
| 0.0267        | 1.2403 | 800  | 0.0314          |
| 0.0314        | 1.3178 | 850  | 0.0317          |
| 0.0382        | 1.3953 | 900  | 0.0327          |
| 0.0354        | 1.4729 | 950  | 0.0320          |
| 0.0265        | 1.5504 | 1000 | 0.0321          |
| 0.0301        | 1.6279 | 1050 | 0.0333          |
| 0.0262        | 1.7054 | 1100 | 0.0312          |
| 0.0273        | 1.7829 | 1150 | 0.0306          |
| 0.0283        | 1.8605 | 1200 | 0.0297          |
| 0.0381        | 1.9380 | 1250 | 0.0299          |
| 0.0207        | 2.0155 | 1300 | 0.0294          |
| 0.0163        | 2.0930 | 1350 | 0.0329          |
| 0.0236        | 2.1705 | 1400 | 0.0311          |
| 0.0191        | 2.2481 | 1450 | 0.0310          |
| 0.0243        | 2.3256 | 1500 | 0.0308          |
| 0.0165        | 2.4031 | 1550 | 0.0327          |
| 0.0224        | 2.4806 | 1600 | 0.0329          |
| 0.0289        | 2.5581 | 1650 | 0.0319          |
| 0.014         | 2.6357 | 1700 | 0.0316          |
| 0.0182        | 2.7132 | 1750 | 0.0334          |
| 0.0175        | 2.7907 | 1800 | 0.0298          |
| 0.0218        | 2.8682 | 1850 | 0.0297          |
| 0.018         | 2.9457 | 1900 | 0.0289          |
| 0.01          | 3.0233 | 1950 | 0.0309          |
| 0.0109        | 3.1008 | 2000 | 0.0338          |
| 0.0076        | 3.1783 | 2050 | 0.0347          |
| 0.0087        | 3.2558 | 2100 | 0.0358          |
| 0.0092        | 3.3333 | 2150 | 0.0323          |
| 0.0078        | 3.4109 | 2200 | 0.0331          |
| 0.0109        | 3.4884 | 2250 | 0.0356          |
| 0.0137        | 3.5659 | 2300 | 0.0360          |
| 0.013         | 3.6434 | 2350 | 0.0350          |
| 0.0133        | 3.7209 | 2400 | 0.0353          |
| 0.0068        | 3.7984 | 2450 | 0.0357          |
| 0.012         | 3.8760 | 2500 | 0.0348          |
| 0.0088        | 3.9535 | 2550 | 0.0344          |
| 0.0066        | 4.0310 | 2600 | 0.0346          |
| 0.0052        | 4.1085 | 2650 | 0.0361          |
| 0.008         | 4.1860 | 2700 | 0.0374          |
| 0.0062        | 4.2636 | 2750 | 0.0383          |
| 0.005         | 4.3411 | 2800 | 0.0386          |
| 0.004         | 4.4186 | 2850 | 0.0395          |
| 0.0075        | 4.4961 | 2900 | 0.0400          |
| 0.003         | 4.5736 | 2950 | 0.0402          |
| 0.0066        | 4.6512 | 3000 | 0.0405          |
| 0.005         | 4.7287 | 3050 | 0.0406          |
| 0.0067        | 4.8062 | 3100 | 0.0407          |
| 0.0067        | 4.8837 | 3150 | 0.0407          |
| 0.006         | 4.9612 | 3200 | 0.0407          |


### Framework versions

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