amjad0099/gemma3-cidar-ar
The amjad0099/gemma3-cidar-ar is a 0.3 billion parameter Gemma3-based causal language model, fine-tuned by amjad0099. This model was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. With a context length of 32768 tokens, it is suitable for tasks requiring efficient processing of longer sequences.
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Model Overview
The amjad0099/gemma3-cidar-ar is a 0.3 billion parameter language model, fine-tuned by amjad0099. It is based on the Gemma3 architecture, specifically fine-tuned from the unsloth/gemma-3-270m-it model. This model leverages the Unsloth library and Huggingface's TRL library for accelerated training, indicating an optimization for efficient fine-tuning processes.
Key Characteristics
- Architecture: Gemma3-based, a causal language model.
- Parameter Count: 0.3 billion parameters, making it a compact model.
- Context Length: Supports a substantial context window of 32768 tokens.
- Training Efficiency: Fine-tuned using Unsloth, which facilitates 2x faster training.
Potential Use Cases
Given its compact size and efficient fine-tuning, this model is well-suited for:
- Applications requiring a smaller footprint and faster inference.
- Tasks benefiting from a long context window, such as summarization or detailed question answering.
- Further experimentation and fine-tuning on specific downstream tasks where rapid iteration is beneficial.