zeeshaan-ai/GetSoloTech
zeeshaan-ai/GetSoloTech is a 0.6 billion parameter language model based on the Qwen3-0.6B architecture, fine-tuned using LoRA (PEFT) for enhanced performance. This model is specifically trained on the GetSoloTech/Code-Reasoning dataset, making it optimized for tasks requiring code understanding and reasoning. Its compact size and specialized training make it suitable for efficient deployment in code-centric applications.
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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.