Model Overview

This model, OpenMath-Nemotron-1.5B-PruneAware-2, is a 1.5 billion parameter language model developed by anujjamwal. It is a fine-tuned version of an existing base model, specifically trained using the TRL (Transformers Reinforcement Learning) framework. The training process involved Supervised Fine-Tuning (SFT), leveraging TRL version 0.29.0, Transformers 5.0.0, Pytorch 2.10.0+cu128, Datasets 4.0.0, and Tokenizers 0.22.2.

Key Characteristics

• Parameter Count: 1.5 billion parameters.
• Context Length: Supports a substantial context window of 32768 tokens, enabling processing of longer inputs.
• Training Method: Fine-tuned using the TRL library with a Supervised Fine-Tuning (SFT) approach.
• Origin: Fine-tuned from an existing OpenMath-Nemotron-1.5B-PruneAware-2 base model, suggesting a specialization in mathematical or reasoning tasks.

Intended Use Cases

This model is suitable for applications that benefit from a compact yet capable language model with a large context window, especially where fine-tuning for specific tasks has been applied. Its training with TRL implies potential for improved instruction following or task-specific performance. Developers can integrate it using the Hugging Face transformers pipeline for text generation tasks.