---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2.5-0.5B
language:
- en
pipeline_tag: text-generation
tags:
- generated_from_trainer
- instruction-tuning
model-index:
- name: outputs/qwen2.5-0.5b-ft-synthia15-i
  results: []
---

[<img src="https://raw.githubusercontent.com/axolotl-ai-cloud/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/axolotl-ai-cloud/axolotl)

# Qwen2.5-0.5B Fine-tuned on Synthia v1.5-I

This model is a fine-tuned version of [Qwen/Qwen2.5-0.5B](https://huggingface.co/Qwen/Qwen2.5-0.5B) on the Synthia v1.5-I dataset, which contains over 20.7k instruction-following examples.

## Model Description

Qwen2.5-0.5B is part of the latest Qwen2.5 series of large language models. The base model brings significant improvements in:
- Instruction following and generating long texts
- Understanding structured data and generating structured outputs
- Support for over 29 languages
- Long context support up to 32,768 tokens

This fine-tuned version enhances the base model's instruction-following capabilities through training on the Synthia v1.5-I dataset.

### Model Architecture
- Type: Causal Language Model
- Parameters: 0.49B (0.36B non-embedding)
- Layers: 24
- Attention Heads: 14 for Q and 2 for KV (GQA)
- Context Length: 32,768 tokens
- Training Framework: Transformers 4.45.0.dev0

## Intended Uses & Limitations

This model is intended for:
- Instruction following and task completion
- Text generation and completion
- Conversational AI applications

The model inherits the multilingual capabilities and long context support of the base Qwen2.5-0.5B model, while being specifically tuned for instruction following.

## Training Procedure

### Training Data
The model was fine-tuned on the Synthia v1.5-I dataset containing 20.7k instruction-following examples.

### Training Hyperparameters

The following hyperparameters were used during training:
- Learning rate: 1e-05
- Train batch size: 5
- Eval batch size: 5
- Seed: 42
- Gradient accumulation steps: 8
- Total train batch size: 40
- Optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- LR scheduler type: cosine
- LR scheduler warmup steps: 100
- Number of epochs: 3
- Sequence length: 4096
- Sample packing: enabled
- Pad to sequence length: enabled

## Framework Versions

- Transformers 4.45.0.dev0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1

<details><summary>See axolotl config</summary>

axolotl version: `0.4.1`