---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-1.5B-Instruct
tags:
- base_model:adapter:Qwen/Qwen2.5-1.5B-Instruct
- llama-factory
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: authorship_model
  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. -->

# authorship_model

This model is a fine-tuned version of [Qwen/Qwen2.5-1.5B-Instruct](https://huggingface.co/Qwen/Qwen2.5-1.5B-Instruct) on the authorship_train dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4835

## 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: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_steps: 20
- num_epochs: 3.0
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 1.3347        | 0.0528 | 500   | 0.6354          |
| 1.3194        | 0.1057 | 1000  | 0.6109          |
| 1.2027        | 0.1585 | 1500  | 0.5906          |
| 1.2360        | 0.2114 | 2000  | 0.5835          |
| 1.2672        | 0.2642 | 2500  | 0.5752          |
| 1.1666        | 0.3171 | 3000  | 0.5580          |
| 1.1543        | 0.3699 | 3500  | 0.5537          |
| 1.1933        | 0.4227 | 4000  | 0.5499          |
| 1.2075        | 0.4756 | 4500  | 0.5418          |
| 1.0687        | 0.5284 | 5000  | 0.5443          |
| 1.0309        | 0.5813 | 5500  | 0.5371          |
| 0.9416        | 0.6341 | 6000  | 0.5339          |
| 1.1705        | 0.6869 | 6500  | 0.5248          |
| 0.9056        | 0.7398 | 7000  | 0.5215          |
| 1.0791        | 0.7926 | 7500  | 0.5182          |
| 1.0082        | 0.8455 | 8000  | 0.5137          |
| 1.1300        | 0.8983 | 8500  | 0.5123          |
| 1.0804        | 0.9512 | 9000  | 0.5095          |
| 0.9295        | 1.0039 | 9500  | 0.5073          |
| 0.9995        | 1.0568 | 10000 | 0.5065          |
| 1.0430        | 1.1096 | 10500 | 0.5050          |
| 1.0754        | 1.1624 | 11000 | 0.5025          |
| 1.0258        | 1.2153 | 11500 | 0.5010          |
| 1.0720        | 1.2681 | 12000 | 0.4990          |
| 1.0141        | 1.3210 | 12500 | 0.4977          |
| 0.9102        | 1.3738 | 13000 | 0.4960          |
| 1.0301        | 1.4266 | 13500 | 0.4951          |
| 0.8990        | 1.4795 | 14000 | 0.4934          |
| 1.0046        | 1.5323 | 14500 | 0.4922          |
| 0.8761        | 1.5852 | 15000 | 0.4909          |
| 1.0435        | 1.6380 | 15500 | 0.4897          |
| 0.9703        | 1.6909 | 16000 | 0.4875          |
| 0.8901        | 1.7437 | 16500 | 0.4857          |
| 0.9523        | 1.7965 | 17000 | 0.4855          |
| 0.9663        | 1.8494 | 17500 | 0.4838          |
| 0.9741        | 1.9022 | 18000 | 0.4831          |
| 0.9686        | 1.9551 | 18500 | 0.4817          |
| 0.8208        | 2.0078 | 19000 | 0.4860          |
| 0.9067        | 2.0607 | 19500 | 0.4867          |
| 0.8943        | 2.1135 | 20000 | 0.4873          |
| 0.9204        | 2.1663 | 20500 | 0.4863          |
| 0.8343        | 2.2192 | 21000 | 0.4863          |
| 0.8542        | 2.2720 | 21500 | 0.4871          |
| 0.9198        | 2.3249 | 22000 | 0.4863          |
| 0.8754        | 2.3777 | 22500 | 0.4859          |
| 0.8645        | 2.4306 | 23000 | 0.4852          |
| 0.8500        | 2.4834 | 23500 | 0.4841          |
| 0.8699        | 2.5362 | 24000 | 0.4842          |
| 0.8449        | 2.5891 | 24500 | 0.4841          |
| 0.8593        | 2.6419 | 25000 | 0.4841          |
| 0.7919        | 2.6948 | 25500 | 0.4837          |
| 0.7707        | 2.7476 | 26000 | 0.4841          |
| 0.8306        | 2.8005 | 26500 | 0.4839          |
| 0.7969        | 2.8533 | 27000 | 0.4837          |
| 0.9231        | 2.9061 | 27500 | 0.4837          |
| 0.9109        | 2.9590 | 28000 | 0.4836          |


### Framework versions

- PEFT 0.18.1
- Transformers 5.0.0
- Pytorch 2.10.0+cu128
- Datasets 4.0.0
- Tokenizers 0.22.2