Model Overview

AdrianFernandes/Qwen2.5-3B-Konkani is a 3.1 billion parameter language model developed by AdrianFernandes. It is fine-tuned from the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model, specifically optimized for the Konkani language.

Key Characteristics

• Architecture: Based on the Qwen2.5 model family.
• Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
• Training Efficiency: Utilizes Unsloth and Huggingface's TRL library, enabling 2x faster fine-tuning.
• Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer texts.
• License: Distributed under the Apache-2.0 license.

Use Cases

This model is particularly well-suited for applications requiring:

• Konkani Language Processing: Ideal for tasks such as text generation, translation, summarization, or conversational AI in Konkani.
• Efficient Deployment: Its optimized training and moderate size make it suitable for scenarios where faster inference and reduced resource consumption are important.
• Research and Development: Provides a strong foundation for further experimentation and fine-tuning on Konkani-specific datasets.