zeeshaan-ai/GetSoloTech: A Compact Code Reasoning Model

zeeshaan-ai/GetSoloTech is a specialized language model built upon the Qwen3-0.6B base architecture. It features 0.6 billion parameters and was fine-tuned using the LoRA (PEFT) method to optimize its performance for specific tasks. The training involved 1 epoch over 100 steps with a batch size of 4 and a learning rate of 0.0002, completing in under 9 minutes.

Key Capabilities

• Code Reasoning: The model's primary strength lies in its training on the GetSoloTech/Code-Reasoning dataset, indicating a focus on understanding and processing code-related logic.
• Efficient Fine-tuning: Utilizes LoRA with specific hyperparameters (r=4, alpha=4) for efficient adaptation of the base model.
• Compact Size: With 0.6 billion parameters, it offers a smaller footprint compared to larger models, potentially allowing for more efficient deployment and inference.

Good For

• Code-centric applications: Ideal for tasks where understanding and reasoning about code snippets are crucial.
• Resource-constrained environments: Its smaller parameter count makes it suitable for deployment where computational resources are limited.
• Rapid prototyping: The efficient training process suggests it can be quickly adapted or used for specific code-related tasks.