anna-ssi/Qwen2.5-1.5B-Open-R1-Distill
anna-ssi/Qwen2.5-1.5B-Open-R1-Distill is a 1.5 billion parameter causal language model, fine-tuned from Qwen/Qwen2.5-1.5B-Instruct. This model has been trained using the TRL framework, focusing on instruction-following capabilities. With a context length of 131072 tokens, it is designed for general text generation tasks, particularly those requiring adherence to given instructions.
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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.