---
library_name: transformers
license: other
base_model: Qwen/Qwen2.5-Coder-3B
tags:
- generated_from_trainer
model-index:
- name: qwen2.5_coder_3b_sqlfuse_probgate_only_answerable_delimeters_eos
  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. -->

# qwen2.5_coder_3b_sqlfuse_probgate_only_answerable_delimeters_eos

This model is a fine-tuned version of [Qwen/Qwen2.5-Coder-3B](https://huggingface.co/Qwen/Qwen2.5-Coder-3B) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2362

## 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
- 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
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss |
|:-------------:|:-----:|:-----:|:---------------:|
| No log        | 0     | 0     | 1.7824          |
| 0.1544        | 1.0   | 4674  | 0.1842          |
| 0.1131        | 2.0   | 9348  | 0.1640          |
| 0.0814        | 3.0   | 14022 | 0.1701          |
| 0.0561        | 4.0   | 18696 | 0.1827          |
| 0.0359        | 5.0   | 23370 | 0.2362          |


### Framework versions

- Transformers 4.56.0
- Pytorch 2.6.0+cu124
- Datasets 3.5.0
- Tokenizers 0.22.0

## Additional Logging Information
---
tags:
- text-to-sql
- sqlfuse_prompt
- Qwen2.5-Coder-3B
---

This model is a fine-tuned version of `Qwen/Qwen2.5-Coder-3B` for the text-to-SQL task.

## Training Details
- **Training Time**: 02:35:39
- **GPU**: NVIDIA A100 80GB PCIe
- **Training Date**: 2025-11-28 11:45:10