anujjamwal/OpenMath-Nemotron-1.5B-hcot-archive
anujjamwal/OpenMath-Nemotron-1.5B-hcot-archive is a 1.5 billion parameter language model fine-tuned from nvidia/OpenMath-Nemotron-1.5B, featuring a 32768 token context length. This model was trained using Supervised Fine-Tuning (SFT) with the TRL framework. It is designed for text generation tasks, building upon the mathematical reasoning capabilities of its base model.
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Model Overview
anujjamwal/OpenMath-Nemotron-1.5B-hcot-archive is a 1.5 billion parameter language model, fine-tuned from the nvidia/OpenMath-Nemotron-1.5B base model. It leverages a substantial 32768 token context window, making it suitable for processing longer inputs and generating coherent, extended responses. The model was developed using the TRL (Transformers Reinforcement Learning) framework, specifically through a Supervised Fine-Tuning (SFT) procedure.
Key Capabilities
- Text Generation: Excels at generating human-like text based on given prompts.
- Extended Context Handling: Benefits from a 32768 token context length, allowing for more complex and detailed interactions.
- Fine-tuned Performance: Built upon
nvidia/OpenMath-Nemotron-1.5B, suggesting potential strengths in areas related to the base model's focus, such as mathematical reasoning.
Training Details
The model underwent Supervised Fine-Tuning (SFT) using the TRL library (version 0.29.0). The training environment included Transformers 5.0.0, Pytorch 2.10.0+cu128, Datasets 4.0.0, and Tokenizers 0.22.2.
Good For
- Applications requiring robust text generation.
- Scenarios where a large context window is beneficial for understanding and generating detailed content.
- Further research and development in fine-tuning language models for specific tasks.