---
library_name: transformers
license: other
base_model: Qwen/Qwen3-4B-Instruct-2507
tags:
- llama-factory
- full
- generated_from_trainer
model-index:
- name: sft_cot
  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_cot

This model is a fine-tuned version of [Qwen/Qwen3-4B-Instruct-2507](https://huggingface.co/Qwen/Qwen3-4B-Instruct-2507) on the scope_sft_cot dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3770

## 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: 1e-05
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 8
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 1.0

### Training results

| Training Loss | Epoch  | Step | Validation Loss |
|:-------------:|:------:|:----:|:---------------:|
| 0.5282        | 0.0763 | 500  | 0.5320          |
| 0.5294        | 0.1526 | 1000 | 0.5253          |
| 0.5116        | 0.2289 | 1500 | 0.5039          |
| 0.5121        | 0.3052 | 2000 | 0.4850          |
| 0.4778        | 0.3815 | 2500 | 0.4666          |
| 0.4823        | 0.4578 | 3000 | 0.4533          |
| 0.4292        | 0.5341 | 3500 | 0.4343          |
| 0.4333        | 0.6104 | 4000 | 0.4167          |
| 0.4153        | 0.6867 | 4500 | 0.4017          |
| 0.3973        | 0.7630 | 5000 | 0.3908          |
| 0.4168        | 0.8393 | 5500 | 0.3823          |
| 0.3918        | 0.9156 | 6000 | 0.3777          |
| 0.3942        | 0.9919 | 6500 | 0.3770          |


### Framework versions

- Transformers 4.57.1
- Pytorch 2.7.1+cu126
- Datasets 3.6.0
- Tokenizers 0.22.1