chaibi-mustapha/gemma-2-2b-fire-detection

Hugging Face
TEXT GENERATIONConcurrency Cost:1Model Size:2.6BQuant:BF16Ctx Length:8kPublished:May 15, 2026License:apache-2.0Architecture:Transformer Open Weights Warm

The chaibi-mustapha/gemma-2-2b-fire-detection model is a 2.6 billion parameter Gemma 2 causal language model, finetuned by chaibi-mustapha. It was trained using Unsloth and Huggingface's TRL library, enabling faster fine-tuning. This model is specifically adapted for fire detection tasks, leveraging its base architecture for specialized performance in this domain. Its 8192-token context length supports processing detailed input for its intended application.

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Model Overview

This model, chaibi-mustapha/gemma-2-2b-fire-detection, is a specialized Gemma 2 variant with 2.6 billion parameters and an 8192-token context length. It was developed by chaibi-mustapha and is licensed under Apache-2.0. The model is a finetuned version of unsloth/gemma-2-2b-it-bnb-4bit.

Key Characteristics

  • Base Architecture: Built upon the Gemma 2 family, known for its efficiency and performance.
  • Fine-tuning: The model was fine-tuned using Unsloth and Huggingface's TRL library, which facilitated a 2x faster training process.
  • Specialization: While the README does not explicitly detail the dataset or specific fire detection capabilities, the naming convention strongly suggests its primary application is in fire detection tasks.

Intended Use

This model is designed for applications requiring a compact yet capable language model, particularly where its specialized fine-tuning for fire detection can be leveraged. Developers looking for a Gemma 2-based model with optimized training and a focus on this specific domain may find it suitable.