---
license: other
library_name: peft
tags:
- trl
- sft
- generated_from_trainer
base_model: taide/TAIDE-LX-7B-Chat
model-index:
- name: ROE_QA_TAIDE-LX-7B-Chat_Q100_80_20_V6
  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. -->

# ROE_QA_TAIDE-LX-7B-Chat_Q100_80_20_V6

This model is a fine-tuned version of [taide/TAIDE-LX-7B-Chat](https://huggingface.co/taide/TAIDE-LX-7B-Chat) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3442

## 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: 5e-05
- train_batch_size: 1
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 4
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 4.8628        | 0.0321 | 100  | 3.4692          |
| 4.6437        | 0.0643 | 200  | 3.2308          |
| 3.8154        | 0.0964 | 300  | 2.8144          |
| 3.3006        | 0.1285 | 400  | 2.5033          |
| 2.953         | 0.1607 | 500  | 2.2690          |
| 2.2844        | 0.1928 | 600  | 1.9550          |
| 2.134         | 0.2249 | 700  | 1.7888          |
| 1.8182        | 0.2571 | 800  | 1.6310          |
| 1.6776        | 0.2892 | 900  | 1.5062          |
| 1.6317        | 0.3213 | 1000 | 1.4026          |
| 1.6311        | 0.3535 | 1100 | 1.2253          |
| 1.1003        | 0.3856 | 1200 | 1.0703          |
| 1.2014        | 0.4177 | 1300 | 1.0672          |
| 0.9489        | 0.4499 | 1400 | 0.9135          |
| 1.0068        | 0.4820 | 1500 | 0.8950          |
| 1.0342        | 0.5141 | 1600 | 0.8074          |
| 0.9713        | 0.5463 | 1700 | 0.7767          |
| 0.9598        | 0.5784 | 1800 | 0.6995          |
| 0.9357        | 0.6105 | 1900 | 0.6643          |
| 0.8298        | 0.6427 | 2000 | 0.6456          |
| 0.8803        | 0.6748 | 2100 | 0.6164          |
| 0.8899        | 0.7069 | 2200 | 0.5867          |
| 0.7877        | 0.7391 | 2300 | 0.5748          |
| 0.7792        | 0.7712 | 2400 | 0.5513          |
| 0.8387        | 0.8033 | 2500 | 0.5267          |
| 0.8625        | 0.8355 | 2600 | 0.5111          |
| 0.8514        | 0.8676 | 2700 | 0.4992          |
| 0.762         | 0.8997 | 2800 | 0.4881          |
| 0.779         | 0.9319 | 2900 | 0.4840          |
| 0.8938        | 0.9640 | 3000 | 0.4676          |
| 0.7007        | 0.9961 | 3100 | 0.4668          |
| 0.7006        | 1.0283 | 3200 | 0.4500          |
| 0.6622        | 1.0604 | 3300 | 0.4383          |
| 0.6724        | 1.0925 | 3400 | 0.4404          |
| 0.6551        | 1.1247 | 3500 | 0.4372          |
| 0.6395        | 1.1568 | 3600 | 0.4273          |
| 0.6317        | 1.1889 | 3700 | 0.4217          |
| 0.6683        | 1.2211 | 3800 | 0.4218          |
| 0.6987        | 1.2532 | 3900 | 0.4136          |
| 0.7309        | 1.2853 | 4000 | 0.4151          |
| 0.6192        | 1.3175 | 4100 | 0.4092          |
| 0.6013        | 1.3496 | 4200 | 0.4109          |
| 0.6138        | 1.3817 | 4300 | 0.4069          |
| 0.6224        | 1.4139 | 4400 | 0.4001          |
| 0.6414        | 1.4460 | 4500 | 0.3942          |
| 0.6234        | 1.4781 | 4600 | 0.3949          |
| 0.6365        | 1.5103 | 4700 | 0.3914          |
| 0.6602        | 1.5424 | 4800 | 0.3908          |
| 0.6646        | 1.5746 | 4900 | 0.3916          |
| 0.6295        | 1.6067 | 5000 | 0.3832          |
| 0.6379        | 1.6388 | 5100 | 0.3828          |
| 0.6544        | 1.6710 | 5200 | 0.3836          |
| 0.6224        | 1.7031 | 5300 | 0.3905          |
| 0.5892        | 1.7352 | 5400 | 0.3785          |
| 0.5985        | 1.7674 | 5500 | 0.3794          |
| 0.5776        | 1.7995 | 5600 | 0.3751          |
| 0.5799        | 1.8316 | 5700 | 0.3745          |
| 0.6247        | 1.8638 | 5800 | 0.3742          |
| 0.5724        | 1.8959 | 5900 | 0.3716          |
| 0.627         | 1.9280 | 6000 | 0.3750          |
| 0.566         | 1.9602 | 6100 | 0.3720          |
| 0.5754        | 1.9923 | 6200 | 0.3706          |
| 0.3561        | 2.0244 | 6300 | 0.3685          |
| 0.4468        | 2.0566 | 6400 | 0.3663          |
| 0.392         | 2.0887 | 6500 | 0.3669          |
| 0.4049        | 2.1208 | 6600 | 0.3659          |
| 0.3967        | 2.1530 | 6700 | 0.3649          |
| 0.418         | 2.1851 | 6800 | 0.3662          |
| 0.4973        | 2.2172 | 6900 | 0.3630          |
| 0.3685        | 2.2494 | 7000 | 0.3627          |
| 0.3967        | 2.2815 | 7100 | 0.3618          |
| 0.3921        | 2.3136 | 7200 | 0.3580          |
| 0.3905        | 2.3458 | 7300 | 0.3595          |
| 0.3888        | 2.3779 | 7400 | 0.3574          |
| 0.4403        | 2.4100 | 7500 | 0.3571          |
| 0.4343        | 2.4422 | 7600 | 0.3574          |
| 0.4088        | 2.4743 | 7700 | 0.3549          |
| 0.3924        | 2.5064 | 7800 | 0.3560          |
| 0.4383        | 2.5386 | 7900 | 0.3540          |
| 0.3786        | 2.5707 | 8000 | 0.3525          |
| 0.3779        | 2.6028 | 8100 | 0.3518          |
| 0.4555        | 2.6350 | 8200 | 0.3519          |
| 0.3813        | 2.6671 | 8300 | 0.3497          |
| 0.375         | 2.6992 | 8400 | 0.3505          |
| 0.3889        | 2.7314 | 8500 | 0.3493          |
| 0.3524        | 2.7635 | 8600 | 0.3469          |
| 0.431         | 2.7956 | 8700 | 0.3480          |
| 0.4185        | 2.8278 | 8800 | 0.3471          |
| 0.378         | 2.8599 | 8900 | 0.3487          |
| 0.3978        | 2.8920 | 9000 | 0.3457          |
| 0.421         | 2.9242 | 9100 | 0.3461          |
| 0.3691        | 2.9563 | 9200 | 0.3443          |
| 0.4019        | 2.9884 | 9300 | 0.3446          |
| 0.2392        | 3.0206 | 9400 | 0.3473          |
| 0.2213        | 3.0527 | 9500 | 0.3443          |
| 0.2189        | 3.0848 | 9600 | 0.3437          |
| 0.2363        | 3.1170 | 9700 | 0.3442          |


### Framework versions

- PEFT 0.12.1.dev0
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1