cs-552-2026-aaty/math_model
The cs-552-2026-aaty/math_model is a 2 billion parameter language model, fine-tuned from Qwen/Qwen3-1.7B using the TRL framework. This model is specifically optimized for mathematical reasoning and related tasks, leveraging its fine-tuning process to enhance performance in numerical and logical problem-solving. With a context length of 32768 tokens, it is designed for applications requiring robust mathematical capabilities.
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Overview
The cs-552-2026-aaty/math_model is a 2 billion parameter language model, fine-tuned from the Qwen/Qwen3-1.7B base model. This model was developed using the TRL (Transformers Reinforcement Learning) framework, indicating a focus on instruction-following or specific task optimization through supervised fine-tuning (SFT).
Key Capabilities
- Mathematical Reasoning: The model's name and fine-tuning approach suggest a specialization in mathematical tasks and problem-solving.
- Instruction Following: Fine-tuned with SFT, it is designed to respond effectively to user prompts and instructions.
- Extended Context: Supports a context length of 32768 tokens, allowing for processing longer inputs and maintaining coherence over extended dialogues or complex problems.
Training Details
The model underwent Supervised Fine-Tuning (SFT). The training utilized specific versions of key frameworks:
- TRL: 1.3.0
- Transformers: 5.7.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Good For
This model is particularly suitable for use cases requiring strong mathematical understanding and generation, such as:
- Solving mathematical problems.
- Generating explanations for mathematical concepts.
- Assisting with data analysis or logical reasoning tasks where numerical precision is important.