---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen2.5-Coder-0.5B-Instruct
tags:
- generated_from_trainer
datasets:
- arrow
model-index:
- name: sparql-qwen
  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. -->

# sparql-qwen

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-0.5B-Instruct](https://huggingface.co./Qwen/Qwen2.5-Coder-0.5B-Instruct) on the arrow dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5436

## 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: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 10
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss |
|:-------------:|:------:|:-----:|:---------------:|
| 0.7618        | 0.1048 | 500   | 0.7231          |
| 0.7397        | 0.2096 | 1000  | 0.6676          |
| 0.7213        | 0.3143 | 1500  | 0.6440          |
| 0.7047        | 0.4191 | 2000  | 0.6283          |
| 0.6905        | 0.5239 | 2500  | 0.6181          |
| 0.6822        | 0.6287 | 3000  | 0.6081          |
| 0.6651        | 0.7334 | 3500  | 0.6007          |
| 0.662         | 0.8382 | 4000  | 0.5938          |
| 0.6535        | 0.9430 | 4500  | 0.5889          |
| 0.562         | 1.0478 | 5000  | 0.5846          |
| 0.4974        | 1.1526 | 5500  | 0.5820          |
| 0.5317        | 1.2573 | 6000  | 0.5778          |
| 0.572         | 1.3621 | 6500  | 0.5743          |
| 0.5167        | 1.4669 | 7000  | 0.5718          |
| 0.5479        | 1.5717 | 7500  | 0.5692          |
| 0.5368        | 1.6764 | 8000  | 0.5659          |
| 0.5622        | 1.7812 | 8500  | 0.5643          |
| 0.5146        | 1.8860 | 9000  | 0.5621          |
| 0.509         | 1.9908 | 9500  | 0.5602          |
| 0.5536        | 2.0956 | 10000 | 0.5589          |
| 0.5035        | 2.2003 | 10500 | 0.5592          |
| 0.5399        | 2.3051 | 11000 | 0.5567          |
| 0.5247        | 2.4099 | 11500 | 0.5553          |
| 0.5365        | 2.5147 | 12000 | 0.5549          |
| 0.4425        | 2.6194 | 12500 | 0.5545          |
| 0.4761        | 2.7242 | 13000 | 0.5524          |
| 0.5368        | 2.8290 | 13500 | 0.5509          |
| 0.5214        | 2.9338 | 14000 | 0.5494          |
| 0.519         | 3.0386 | 14500 | 0.5496          |
| 0.5606        | 3.1433 | 15000 | 0.5492          |
| 0.5362        | 3.2481 | 15500 | 0.5476          |
| 0.5275        | 3.3529 | 16000 | 0.5476          |
| 0.5159        | 3.4577 | 16500 | 0.5464          |
| 0.5171        | 3.5624 | 17000 | 0.5461          |
| 0.5242        | 3.6672 | 17500 | 0.5454          |
| 0.5053        | 3.7720 | 18000 | 0.5445          |
| 0.512         | 3.8768 | 18500 | 0.5441          |
| 0.5259        | 3.9816 | 19000 | 0.5428          |
| 0.4363        | 4.0863 | 19500 | 0.5437          |
| 0.4784        | 4.1911 | 20000 | 0.5440          |
| 0.4703        | 4.2959 | 20500 | 0.5448          |
| 0.4467        | 4.4007 | 21000 | 0.5436          |


### Framework versions

- PEFT 0.14.0
- Transformers 4.46.3
- Pytorch 2.4.0
- Datasets 3.1.0
- Tokenizers 0.20.3