dzuladj/gemma-4-e2b-fine-tuned-alpaca
The dzuladj/gemma-4-e2b-fine-tuned-alpaca is a 5.1 billion parameter language model, fine-tuned by dzuladj from the unsloth/gemma-4-e2b-it-unsloth-bnb-4bit base model. This model leverages Unsloth and Huggingface's TRL library for accelerated training, offering a 32768 token context length. It is optimized for tasks typically associated with instruction-tuned models, providing efficient performance for its size.
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Model Overview
The dzuladj/gemma-4-e2b-fine-tuned-alpaca is a 5.1 billion parameter language model developed by dzuladj. It is fine-tuned from the unsloth/gemma-4-e2b-it-unsloth-bnb-4bit base model, utilizing the Unsloth library and Huggingface's TRL for training. This approach enabled the model to be trained approximately two times faster than conventional methods.
Key Characteristics
- Parameter Count: 5.1 billion parameters, offering a balance between performance and computational efficiency.
- Context Length: Supports a substantial context window of 32768 tokens, allowing for processing longer inputs and maintaining conversational coherence.
- Training Efficiency: Fine-tuned with Unsloth, a library known for accelerating the training process of large language models.
- Base Model: Built upon the Gemma-4-e2b architecture, indicating a foundation in Google's Gemma family of models.
Use Cases
This model is suitable for a variety of instruction-following tasks, benefiting from its fine-tuned nature and efficient training. Its substantial context length makes it particularly useful for applications requiring detailed understanding of longer prompts or multi-turn interactions.