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README.md

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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
+- 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`