reaperdoesntknow/Qwen3.5-2B-Opus-Distil
reaperdoesntknow/Qwen3.5-2B-Opus-Distil is a 2.3 billion parameter Qwen3.5-based language model, finetuned by reaperdoesntknow. This model was optimized for faster training using Unsloth and Huggingface's TRL library, making it suitable for efficient deployment in applications requiring a compact yet capable model. It offers a 32K context length, providing a balance of performance and resource efficiency for various NLP tasks.
Loading preview...
Model Overview
reaperdoesntknow/Qwen3.5-2B-Opus-Distil is a 2.3 billion parameter language model, finetuned by reaperdoesntknow. It is based on the Qwen3.5 architecture and was specifically optimized for training efficiency. The model leverages Unsloth and Huggingface's TRL library, resulting in a 2x faster training process compared to standard methods.
Key Characteristics
- Base Model: Finetuned from
unsloth/Qwen3.5-2B. - Training Optimization: Achieves 2x faster training through the use of Unsloth and Huggingface's TRL library.
- Parameter Count: Features 2.3 billion parameters, offering a compact size for efficient inference.
- Context Length: Supports a context window of 32,768 tokens.
Use Cases
This model is particularly well-suited for applications where rapid fine-tuning and efficient deployment are critical. Its optimized training process makes it an excellent choice for developers looking to quickly adapt a capable language model to specific tasks without extensive computational resources. It can be applied to various natural language processing tasks, benefiting from its balanced size and context handling.