cs-552-2026-MandMP/math_model
The cs-552-2026-MandMP/math_model is a 2 billion parameter language model, fine-tuned from Qwen/Qwen3-1.7B, with a context length of 32768 tokens. This model was trained using the TRL framework, indicating a focus on instruction following or specific task optimization. It is designed for text generation tasks, leveraging its Qwen3 base for general language understanding and generation capabilities.
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Model Overview
The cs-552-2026-MandMP/math_model is a 2 billion parameter language model, fine-tuned from the Qwen/Qwen3-1.7B base model. It was developed using the TRL (Transformers Reinforcement Learning) framework, suggesting an emphasis on instruction-tuned capabilities or specific task optimization through reinforcement learning from human feedback or similar methods. The model supports a substantial context length of 32768 tokens, allowing it to process and generate longer sequences of text.
Key Capabilities
- Text Generation: Capable of generating coherent and contextually relevant text based on given prompts.
- Instruction Following: Fine-tuning with TRL typically enhances the model's ability to follow specific instructions and generate desired outputs.
- Large Context Window: The 32768-token context length enables handling complex queries and maintaining context over extended conversations or documents.
Training Details
The model was trained using SFT (Supervised Fine-Tuning) within the TRL framework. The training utilized specific versions of key libraries:
- TRL: 1.3.0
- Transformers: 5.7.0
- Pytorch: 2.10.0+cu128
- Datasets: 4.8.5
- Tokenizers: 0.22.2
Good For
- Applications requiring robust text generation from a compact model.
- Use cases benefiting from a model with a large context window for detailed input processing.
- Scenarios where instruction-tuned performance is critical for task execution.