---
base_model: meta-llama/Meta-Llama-3-8B-Instruct
library_name: peft
license: apache-2.0
tags:
- llama-factory
- lora
- generated_from_trainer
model-index:
- name: sft
  results: []
datasets:
- shileii/Teachers_Exam
language:
- zh
- en
pipeline_tag: question-answering
---

<!-- 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. -->

# sft

This model is a fine-tuned version of [/home/Meta-Llama-3-8B-Instruct](https://huggingface.co//home/Meta-Llama-3-8B-Instruct) on the alpaca_zh_demo, the identity and the teachers_exam_local datasets.
It achieves the following results on the evaluation set:
- Loss: 1.5271

这个模型是[Meta-Llama-3-8B-Instruct](https://huggingface.co/home/Meta-Llama-3-8B-Instruct)在identity和teachers_exam_local数据集上的微调版本。
在评估集上得到如下结果:
- Loss: 1.5271

## Model description

This Model is based Meta-Llama3-8B-Instruct finetuning.

## Intended uses & limitations

No limit,everyone can use it.
无限制使用，任何人都可以使用。

## Training and evaluation data

Used high qulity datas with Teachers Exam.The data include choice,multi-choice,etc types subjects.
使用高质量的数据与教师考试。数据包括选择题、多项选择题等类型的题目。

#大模型评测 benchmark(mmlu)
```
        Average: 66.89                                                                
           STEM: 57.52
Social Sciences: 76.28
     Humanities: 62.32
          Other: 73.32
```        
## Training procedure

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 100
- num_epochs: 8.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 2.2507        | 0.1895 | 50   | 1.9127          |
| 1.8486        | 0.3790 | 100  | 1.6928          |
| 1.7402        | 0.5685 | 150  | 1.6263          |
| 1.6577        | 0.7579 | 200  | 1.5895          |
| 1.6681        | 0.9474 | 250  | 1.5667          |
| 1.5953        | 1.1369 | 300  | 1.5586          |
| 1.5308        | 1.3264 | 350  | 1.5557          |
| 1.5432        | 1.5159 | 400  | 1.5500          |
| 1.5724        | 1.7054 | 450  | 1.5392          |
| 1.5135        | 1.8948 | 500  | 1.5271          |
| 1.4324        | 2.0843 | 550  | 1.5466          |
| 1.3993        | 2.2738 | 600  | 1.5391          |
| 1.4099        | 2.4633 | 650  | 1.5434          |
| 1.3764        | 2.6528 | 700  | 1.5400          |
| 1.3219        | 2.8423 | 750  | 1.5354          |
| 1.3678        | 3.0317 | 800  | 1.5719          |
| 1.263         | 3.2212 | 850  | 1.5781          |
| 1.228         | 3.4107 | 900  | 1.5834          |
| 1.2743        | 3.6002 | 950  | 1.5766          |
| 1.2456        | 3.7897 | 1000 | 1.5617          |
| 1.2192        | 3.9792 | 1050 | 1.5626          |
| 1.0889        | 4.1686 | 1100 | 1.6138          |
| 1.156         | 4.3581 | 1150 | 1.6190          |
| 1.1111        | 4.5476 | 1200 | 1.6066          |
| 1.1222        | 4.7371 | 1250 | 1.6185          |
| 1.1102        | 4.9266 | 1300 | 1.6020          |
| 1.042         | 5.1161 | 1350 | 1.6649          |
| 0.9666        | 5.3055 | 1400 | 1.6663          |
| 1.0506        | 5.4950 | 1450 | 1.6709          |
| 1.035         | 5.6845 | 1500 | 1.6592          |
| 1.0121        | 5.8740 | 1550 | 1.6589          |
| 0.968         | 6.0635 | 1600 | 1.7109          |
| 0.9422        | 6.2530 | 1650 | 1.7100          |
| 0.9571        | 6.4424 | 1700 | 1.7004          |
| 0.9546        | 6.6319 | 1750 | 1.6982          |
| 0.9965        | 6.8214 | 1800 | 1.7010          |
| 0.9433        | 7.0109 | 1850 | 1.7062          |
| 0.9193        | 7.2004 | 1900 | 1.7224          |
| 0.89          | 7.3899 | 1950 | 1.7259          |
| 0.901         | 7.5793 | 2000 | 1.7271          |
| 0.9101        | 7.7688 | 2050 | 1.7280          |
| 0.9108        | 7.9583 | 2100 | 1.7280          |


### Framework versions

- PEFT 0.12.0
- Transformers 4.43.4
- Pytorch 2.4.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1