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---
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 |