Ambamir/gemma_3_finetune_amb
Ambamir/gemma_3_finetune_amb is a 4.3 billion parameter Gemma 3 model developed by Ambamir, fine-tuned from unsloth/gemma-3-4b-it-unsloth-bnb-4bit. This model was trained using Unsloth and Huggingface's TRL library, achieving 2x faster training. With a 32768 token context length, it is optimized for efficient performance in generative AI tasks.
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Overview
Ambamir/gemma_3_finetune_amb is a 4.3 billion parameter language model, fine-tuned by Ambamir from the unsloth/gemma-3-4b-it-unsloth-bnb-4bit base model. It leverages the Unsloth library and Huggingface's TRL for efficient training, resulting in a 2x speed improvement during the finetuning process. This model maintains a substantial context length of 32768 tokens, making it suitable for processing longer sequences of text.
Key Capabilities
- Efficient Training: Utilizes Unsloth for significantly faster finetuning.
- Gemma 3 Architecture: Benefits from the underlying capabilities of the Gemma 3 model family.
- Extended Context: Supports a 32768 token context window, enabling handling of complex and lengthy inputs.
Good For
- Applications requiring a Gemma 3-based model with optimized training efficiency.
- Tasks that benefit from a large context window for understanding and generating extensive text.
- Developers looking for a finetuned model that was developed with performance-enhancing tools like Unsloth.