LucasjsBatista/qwen2.5-3b-irpf2026
The LucasjsBatista/qwen2.5-3b-irpf2026 is a 3.1 billion parameter Qwen2.5-based causal language model, fine-tuned by LucasjsBatista. This model was efficiently trained using Unsloth and Huggingface's TRL library, building upon the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base. Its primary differentiation lies in its optimized training process, making it suitable for applications requiring a compact yet capable instruction-tuned model.
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Model Overview
The LucasjsBatista/qwen2.5-3b-irpf2026 is a 3.1 billion parameter instruction-tuned language model, developed by LucasjsBatista. It is fine-tuned from the unsloth/Qwen2.5-3B-Instruct-bnb-4bit base model, leveraging the Unsloth library and Huggingface's TRL for accelerated training.
Key Characteristics
- Base Model: Qwen2.5-3B-Instruct architecture.
- Parameter Count: 3.1 billion parameters.
- Training Efficiency: Utilizes Unsloth for a 2x faster fine-tuning process, making it an efficient choice for deployment.
- License: Distributed under the Apache-2.0 license.
Use Cases
This model is particularly well-suited for:
- Applications requiring a compact, instruction-following language model.
- Scenarios where efficient training and deployment of a Qwen2.5-based model are critical.
- Tasks benefiting from a fine-tuned model with a smaller footprint.