---
language:
- zh
license: apache-2.0
library_name: peft
tags:
- trl
- sft
- nycu-112-2-deeplearning-hw2
- generated_from_trainer
base_model: yentinglin/Taiwan-LLM-7B-v2.1-chat
datasets:
- DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat
model-index:
- name: taiwanllm_7b_lora_completion_only
  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. -->

# taiwanllm_7b_lora_completion_only

This model is a fine-tuned version of [yentinglin/Taiwan-LLM-7B-v2.1-chat](https://huggingface.co/yentinglin/Taiwan-LLM-7B-v2.1-chat) on the DandinPower/ZH-Reading-Comprehension-TaiwanLlama-chat dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0867

## 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
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- total_eval_batch_size: 2
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 700
- num_epochs: 3.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.1316        | 0.3690 | 250  | 0.1124          |
| 0.0988        | 0.7380 | 500  | 0.1145          |
| 0.0827        | 1.1070 | 750  | 0.0971          |
| 0.0744        | 1.4760 | 1000 | 0.0897          |
| 0.0482        | 1.8450 | 1250 | 0.0893          |
| 0.0337        | 2.2140 | 1500 | 0.0924          |
| 0.0229        | 2.5830 | 1750 | 0.0911          |
| 0.0385        | 2.9520 | 2000 | 0.0867          |


### Framework versions

- PEFT 0.10.0
- Transformers 4.40.0
- Pytorch 2.2.2+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1