laklook97/qwen3.5-4b-math-pc-grading
The laklook97/qwen3.5-4b-math-pc-grading model is a 4.5 billion parameter Qwen3.5-based language model developed by laklook97. It was finetuned using Unsloth and Huggingface's TRL library, enabling 2x faster training. This model is optimized for specific tasks related to math and PC grading, leveraging its efficient training methodology.
Loading preview...
Model Overview
The laklook97/qwen3.5-4b-math-pc-grading is a 4.5 billion parameter language model developed by laklook97. It is finetuned from the Qwen/Qwen3.5-4B base model, indicating its foundation in the Qwen3.5 architecture. The model was trained with a focus on efficiency, utilizing Unsloth and Huggingface's TRL library, which reportedly enabled a 2x speedup in the training process.
Key Characteristics
- Base Model: Qwen/Qwen3.5-4B
- Parameter Count: 4.5 billion parameters
- Training Efficiency: Achieved 2x faster training using Unsloth and Huggingface's TRL library.
- Context Length: Supports a context length of 32768 tokens.
Potential Use Cases
This model is specifically named "math-pc-grading," suggesting its optimization for tasks involving mathematical problem-solving and potentially grading or evaluation within a PC-related context. Developers looking for a Qwen3.5-based model with efficient training and a focus on these domains may find this model suitable.