---
library_name: transformers
license: apache-2.0
base_model: Qwen/Qwen2-1.5B
language:
- en
pipeline_tag: text-generation
tags:
- generated_from_trainer
- instruction-tuning
model-index:
- name: outputs/qwen2.5-1.5b-ft-synthia15-i
  results: []
---

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# Qwen2-1.5B Fine-tuned on Synthia v1.5-I

This model is a fine-tuned version of [Qwen/Qwen2-1.5B](https://huggingface.co/Qwen/Qwen2-1.5B) on the Synthia v1.5-I dataset, which contains over 20.7k instruction-following examples.

## Model Description

Qwen2-1.5B is part of the latest Qwen2 series of large language models. The base model brings significant improvements in:
- Language understanding and generation
- Structured data processing
- Support for multiple languages
- Long context handling

---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**

### 💖 Support My Work
If you find this model useful, please consider supporting my work. It helps me cover server costs and dedicate more time to new open-source projects.
- **Patreon:** [Support on Patreon](https://www.patreon.com/user?u=81570187)
- **Ko-fi:** [Buy me a Ko-fi](https://ko-fi.com/artificialguybr)
- **Buy Me a Coffee:** [Buy me a Coffee](https://buymeacoffee.com/jvkape)
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: 1.5B
- 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 capabilities of the base Qwen2-1.5B 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`