Fysna/medvoice_final_v2
Fysna/medvoice_final_v2 is a 5.1 billion parameter instruction-tuned model developed by Fysna, finetuned from unsloth/gemma-4-E2B-it-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, enabling 2x faster training. With a 32768 token context length, it is optimized for efficient performance in language generation tasks.
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Overview
Fysna/medvoice_final_v2 is a 5.1 billion parameter language model developed by Fysna. It is finetuned from the unsloth/gemma-4-E2B-it-unsloth-bnb-4bit base model, leveraging the Unsloth library for accelerated training. This model was trained 2x faster using Unsloth in conjunction with Huggingface's TRL library, indicating an emphasis on training efficiency and performance.
Key Capabilities
- Efficient Training: Utilizes Unsloth for 2x faster training compared to standard methods.
- Instruction-Tuned: Designed for following instructions effectively, derived from its base model's characteristics.
- Large Context Window: Features a 32768 token context length, suitable for processing longer inputs and maintaining conversational coherence over extended interactions.
Good For
- Applications requiring a capable instruction-tuned model with a substantial context window.
- Scenarios where efficient model deployment and performance are critical, benefiting from its optimized training methodology.
- Tasks that can leverage the strengths of the Gemma-4 architecture, enhanced by specific finetuning.