AdrianFernandes/Qwen2.5-3B-Konkani
AdrianFernandes/Qwen2.5-3B-Konkani is a 3.1 billion parameter Qwen2.5 model developed by AdrianFernandes, fine-tuned for the Konkani language. This model leverages Unsloth and Huggingface's TRL library for accelerated training, making it suitable for applications requiring efficient processing in Konkani. With a context length of 32768 tokens, it is designed for language generation and understanding tasks specific to Konkani.
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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.