---
library_name: peft
license: apache-2.0
base_model: Qwen/Qwen3-Coder-30B-A3B-Instruct
tags:
- base_model:adapter:Qwen/Qwen3-Coder-30B-A3B-Instruct
- lora
- transformers
pipeline_tag: text-generation
model-index:
- name: SFT-Qwen3-Coder-30B_v1.1
  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. -->

# SFT-Qwen3-Coder-30B_v1.1

This model is a fine-tuned version of [Qwen/Qwen3-Coder-30B-A3B-Instruct](https://huggingface.co/Qwen/Qwen3-Coder-30B-A3B-Instruct) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5415

## 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: 0.0001
- train_batch_size: 2
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 8
- optimizer: Use OptimizerNames.PAGED_ADAMW_8BIT with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.03
- num_epochs: 3

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.9355        | 0.2857 | 20   | 0.7199          |
| 0.8105        | 0.5714 | 40   | 0.6498          |
| 0.7285        | 0.8571 | 60   | 0.6154          |
| 0.7248        | 1.1429 | 80   | 0.5895          |
| 0.6648        | 1.4286 | 100  | 0.5702          |
| 0.6418        | 1.7143 | 120  | 0.5603          |
| 0.7202        | 2.0    | 140  | 0.5530          |
| 0.5803        | 2.2857 | 160  | 0.5463          |
| 0.558         | 2.5714 | 180  | 0.5434          |
| 0.6506        | 2.8571 | 200  | 0.5415          |


### Framework versions

- PEFT 0.18.0
- Transformers 4.57.1
- Pytorch 2.9.0+cu126
- Datasets 4.4.1
- Tokenizers 0.22.1