Qwen2.5 72 B

Introduction

Qwen2.5-72B is a large language model from the Qwen series, featuring advanced capabilities in coding, mathematics, and multilingual support. This model includes various improvements over its predecessor, Qwen2, such as enhanced instruction following, the ability to generate and understand structured data, long-context support, and multilingual capabilities across 29 languages.

Architecture

Qwen2.5-72B is a causal language model built with transformer architecture and incorporates features like RoPE, SwiGLU, RMSNorm, and Attention QKV bias. It comprises 72.7 billion parameters, with 70 billion being non-embedding parameters, spread across 80 layers and 64 attention heads for Q and 8 for KV. It supports a context length of up to 131,072 tokens.

Training

The model is pretrained with a focus on improving knowledge acquisition and capabilities in coding and mathematics. It is not recommended for conversational use without further post-training processes such as Supervised Fine-Tuning (SFT) or Reinforcement Learning from Human Feedback (RLHF).

Guide: Running Locally

To run Qwen2.5-72B locally, follow these steps:

1. Install Requirements: Ensure you have the latest version of the Hugging Face Transformers library. Older versions (below 4.37.0) may cause compatibility issues.
2. Download the Model: Obtain the Qwen2.5-72B model from the Hugging Face model hub.
3. Set Up the Environment: Prepare a Python environment with necessary dependencies.
4. Run Inference: Use the Transformers library to load the model and perform inference.

For optimal performance, consider using cloud GPU services such as AWS, Google Cloud, or Azure, as the model requires significant computational resources.

License

Qwen2.5-72B is released under the Qwen license. More details can be found here.