Model Overview

anna-ssi/Qwen2.5-1.5B-Open-R1-Distill is a 1.5 billion parameter language model, derived from the Qwen2.5-1.5B-Instruct base model. It has undergone further fine-tuning using the TRL (Transformer Reinforcement Learning) framework, specifically through Supervised Fine-Tuning (SFT).

Key Characteristics

• Base Model: Fine-tuned from Qwen/Qwen2.5-1.5B-Instruct.
• Training Framework: Utilizes Hugging Face's TRL library for fine-tuning.
• Training Method: Employs Supervised Fine-Tuning (SFT).
• Parameter Count: 1.5 billion parameters, offering a balance between performance and computational efficiency.
• Context Length: Supports a substantial context window of 131072 tokens, enabling processing of longer inputs.

Use Cases

This model is suitable for various text generation tasks where instruction adherence is important. Its fine-tuned nature suggests improved performance on tasks requiring specific output formats or responses based on given prompts. Developers can integrate it using the Hugging Face transformers pipeline for quick deployment.