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---
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-fold8
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-fold8
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-fold8 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0313
## 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 |
|:-------------:|:------:|:----:|:---------------:|
| 0.8816 | 0.0758 | 50 | 0.6521 |
| 0.1876 | 0.1516 | 100 | 0.1201 |
| 0.0847 | 0.2275 | 150 | 0.0709 |
| 0.0547 | 0.3033 | 200 | 0.0595 |
| 0.05 | 0.3791 | 250 | 0.0510 |
| 0.0566 | 0.4549 | 300 | 0.0494 |
| 0.057 | 0.5308 | 350 | 0.0461 |
| 0.0323 | 0.6066 | 400 | 0.0422 |
| 0.0331 | 0.6824 | 450 | 0.0393 |
| 0.0339 | 0.7582 | 500 | 0.0405 |
| 0.0432 | 0.8340 | 550 | 0.0383 |
| 0.0332 | 0.9099 | 600 | 0.0361 |
| 0.0458 | 0.9857 | 650 | 0.0381 |
| 0.0281 | 1.0615 | 700 | 0.0368 |
| 0.0222 | 1.1373 | 750 | 0.0382 |
| 0.0272 | 1.2132 | 800 | 0.0346 |
| 0.0303 | 1.2890 | 850 | 0.0352 |
| 0.0318 | 1.3648 | 900 | 0.0358 |
| 0.0233 | 1.4406 | 950 | 0.0353 |
| 0.0263 | 1.5164 | 1000 | 0.0349 |
| 0.0381 | 1.5923 | 1050 | 0.0354 |
| 0.0267 | 1.6681 | 1100 | 0.0319 |
| 0.0335 | 1.7439 | 1150 | 0.0320 |
| 0.0228 | 1.8197 | 1200 | 0.0320 |
| 0.0311 | 1.8956 | 1250 | 0.0313 |
| 0.0228 | 1.9714 | 1300 | 0.0314 |
| 0.0157 | 2.0472 | 1350 | 0.0321 |
| 0.0129 | 2.1230 | 1400 | 0.0316 |
| 0.0208 | 2.1988 | 1450 | 0.0330 |
| 0.0191 | 2.2747 | 1500 | 0.0325 |
| 0.0189 | 2.3505 | 1550 | 0.0325 |
| 0.0161 | 2.4263 | 1600 | 0.0325 |
| 0.0164 | 2.5021 | 1650 | 0.0356 |
| 0.0144 | 2.5780 | 1700 | 0.0338 |
| 0.0211 | 2.6538 | 1750 | 0.0328 |
| 0.0195 | 2.7296 | 1800 | 0.0322 |
| 0.0148 | 2.8054 | 1850 | 0.0338 |
| 0.0249 | 2.8812 | 1900 | 0.0327 |
| 0.0152 | 2.9571 | 1950 | 0.0320 |
| 0.0136 | 3.0329 | 2000 | 0.0329 |
| 0.009 | 3.1087 | 2050 | 0.0341 |
| 0.0089 | 3.1845 | 2100 | 0.0367 |
| 0.0127 | 3.2604 | 2150 | 0.0364 |
| 0.0119 | 3.3362 | 2200 | 0.0358 |
| 0.0118 | 3.4120 | 2250 | 0.0358 |
| 0.0084 | 3.4878 | 2300 | 0.0371 |
| 0.0137 | 3.5636 | 2350 | 0.0365 |
| 0.0093 | 3.6395 | 2400 | 0.0364 |
| 0.0095 | 3.7153 | 2450 | 0.0359 |
| 0.0102 | 3.7911 | 2500 | 0.0381 |
| 0.0137 | 3.8669 | 2550 | 0.0384 |
| 0.01 | 3.9428 | 2600 | 0.0376 |
| 0.0058 | 4.0186 | 2650 | 0.0389 |
| 0.0071 | 4.0944 | 2700 | 0.0407 |
| 0.0051 | 4.1702 | 2750 | 0.0414 |
| 0.0077 | 4.2460 | 2800 | 0.0419 |
| 0.0039 | 4.3219 | 2850 | 0.0424 |
| 0.002 | 4.3977 | 2900 | 0.0429 |
| 0.0036 | 4.4735 | 2950 | 0.0433 |
| 0.0083 | 4.5493 | 3000 | 0.0439 |
| 0.0049 | 4.6252 | 3050 | 0.0439 |
| 0.0033 | 4.7010 | 3100 | 0.0443 |
| 0.0034 | 4.7768 | 3150 | 0.0443 |
| 0.0038 | 4.8526 | 3200 | 0.0442 |
| 0.0029 | 4.9284 | 3250 | 0.0443 |
### Framework versions
- PEFT 0.12.0
- Transformers 4.46.1
- Pytorch 2.5.1+cu124
- Datasets 3.1.0
- Tokenizers 0.20.3 |