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Model: artificialguybr/QWEN-2.5-0.5B-Synthia-I
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---
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
---
### 🌐 Website
You can find more of my models, projects, and information on my official website:
- **[artificialguy.com](https://artificialguy.com/)**
### 🚀 Prompt Hub
Need high-quality prompts for image models and LLMs? Explore **[findgoodprompt.com](https://findgoodprompt.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)
- 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`

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{
"_name_or_path": "Qwen/Qwen2.5-0.5B",
"architectures": [
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],
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"initializer_range": 0.02,
"intermediate_size": 4864,
"max_position_embeddings": 32768,
"max_window_layers": 24,
"model_type": "qwen2",
"num_attention_heads": 14,
"num_hidden_layers": 24,
"num_key_value_heads": 2,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000.0,
"sliding_window": null,
"tie_word_embeddings": true,
"torch_dtype": "float32",
"transformers_version": "4.45.0.dev0",
"use_cache": false,
"use_mrope": false,
"use_sliding_window": false,
"vocab_size": 151936
}

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