Alelcv27/Qwen2.5-3B-EvilMisaligned
Alelcv27/Qwen2.5-3B-EvilMisaligned is a 3.1 billion parameter language model developed by Alelcv27, fine-tuned from unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit. This model leverages Unsloth for accelerated training, achieving 2x faster finetuning. With a 32768 token context length, it is designed for efficient deployment in applications requiring a compact yet capable model.
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Model Overview
Alelcv27/Qwen2.5-3B-EvilMisaligned is a 3.1 billion parameter language model developed by Alelcv27. It is a finetuned variant of the unsloth/qwen2.5-3b-instruct-unsloth-bnb-4bit base model, utilizing the Qwen2.5 architecture. This model was specifically trained using the Unsloth library in conjunction with Hugging Face's TRL library, which enabled a 2x faster finetuning process.
Key Characteristics
- Architecture: Based on the Qwen2.5 model family.
- Parameter Count: 3.1 billion parameters, offering a balance between performance and computational efficiency.
- Training Efficiency: Finetuned with Unsloth, resulting in significantly reduced training times.
- Context Length: Supports a substantial context window of 32768 tokens.
Use Cases
This model is suitable for applications where a compact, efficiently trained language model is beneficial. Its faster finetuning process makes it an interesting candidate for developers looking to quickly adapt a Qwen2.5-based model to specific tasks without extensive computational overhead.