QWEN-2-1.5B-Synthia-II-Redmond

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Qwen2-1.5B Fine-tuned on Synthia v1.5-II

A special thanks to Redmond.ai for sponsoring the GPU resources for this fine-tuning process.

This model is a fine-tuned version of Qwen/Qwen2-1.5B on the Synthia v1.5-II 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

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

Model Architecture

• Type: Causal Language Model
• Parameters: 1.5B

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• 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-II 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

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