---
license: other
library_name: peft
tags:
- llama-factory
- lora
- generated_from_trainer
base_model: /workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5
model-index:
- name: fact
  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. -->

# fact

This model is a fine-tuned version of [/workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5](https://huggingface.co//workspace/xll/checkpoints/lmsys/vicuna-7b-v1.5) on the vicuna_fact_test dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0626

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- quant_method: bitsandbytes
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: nf4
- bnb_4bit_use_double_quant: True
- bnb_4bit_compute_dtype: float16

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- training_steps: 4430
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss |
|:-------------:|:-----:|:----:|:---------------:|
| 0.0941        | 0.02  | 100  | 0.0995          |
| 0.0903        | 0.04  | 200  | 0.0902          |
| 0.089         | 0.06  | 300  | 0.0802          |
| 0.0619        | 0.08  | 400  | 0.0762          |
| 0.0722        | 0.1   | 500  | 0.0752          |
| 0.0724        | 0.12  | 600  | 0.0736          |
| 0.0762        | 0.14  | 700  | 0.0710          |
| 0.0567        | 0.16  | 800  | 0.0712          |
| 0.0578        | 0.18  | 900  | 0.0679          |
| 0.0597        | 0.2   | 1000 | 0.0691          |
| 0.0538        | 0.22  | 1100 | 0.0660          |
| 0.0498        | 0.24  | 1200 | 0.0658          |
| 0.0595        | 0.26  | 1300 | 0.0642          |
| 0.0465        | 0.28  | 1400 | 0.0677          |
| 0.0533        | 0.3   | 1500 | 0.0651          |
| 0.0593        | 0.32  | 1600 | 0.0641          |
| 0.055         | 0.34  | 1700 | 0.0653          |
| 0.0546        | 0.36  | 1800 | 0.0634          |
| 0.0524        | 0.38  | 1900 | 0.0615          |
| 0.0432        | 0.4   | 2000 | 0.0632          |
| 0.0631        | 0.42  | 2100 | 0.0619          |
| 0.0519        | 0.44  | 2200 | 0.0598          |
| 0.0397        | 0.46  | 2300 | 0.0607          |
| 0.0467        | 0.48  | 2400 | 0.0616          |
| 0.049         | 0.5   | 2500 | 0.0636          |
| 0.0488        | 0.52  | 2600 | 0.0604          |
| 0.0449        | 0.54  | 2700 | 0.0598          |
| 0.0438        | 0.56  | 2800 | 0.0610          |
| 0.036         | 0.58  | 2900 | 0.0633          |
| 0.0464        | 0.6   | 3000 | 0.0603          |
| 0.0437        | 0.62  | 3100 | 0.0612          |
| 0.0389        | 0.64  | 3200 | 0.0605          |
| 0.0377        | 0.66  | 3300 | 0.0632          |
| 0.0382        | 0.68  | 3400 | 0.0626          |
| 0.051         | 0.7   | 3500 | 0.0612          |
| 0.047         | 0.72  | 3600 | 0.0633          |
| 0.0451        | 0.74  | 3700 | 0.0608          |
| 0.0472        | 0.77  | 3800 | 0.0630          |
| 0.0394        | 0.79  | 3900 | 0.0612          |
| 0.0471        | 0.81  | 4000 | 0.0614          |
| 0.031         | 0.83  | 4100 | 0.0602          |
| 0.0405        | 0.85  | 4200 | 0.0584          |
| 0.0421        | 0.87  | 4300 | 0.0591          |
| 0.0454        | 0.89  | 4400 | 0.0593          |


### Framework versions

- PEFT 0.7.0
- Transformers 4.37.1
- Pytorch 2.1.2+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1