Qwen2.5-0.5B Fine-tuned on Synthia v1.5-I
This model is a fine-tuned version of 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
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- 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
axolotl version: 0.4.1
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