Q R W K V6 32 B Instruct Preview v0.1

Introduction

QRWKV6-32B-Instruct-Preview-v0.1 is a powerful iteration of the RWKV model, showcasing significant improvements in computational cost efficiency and performance. It leverages linear attention mechanisms to optimize inference cost while maintaining competitive accuracy across various benchmarks.

Architecture

The QRWKV6 model utilizes a linear attention mechanism that allows for substantial reductions in computational overhead, especially for models with large context lengths. This architecture can transform existing QKV Attention-based models into RWKV variants without starting from scratch, thus providing a cost-effective and scalable solution. However, it inherits the knowledge and dataset limitations of its parent models, supporting around 30 languages compared to over 100 in previous RWKV iterations.

Training

The model was trained with a context length of up to 16K tokens, constrained by compute resources. While stable beyond this length, further training could enhance performance for longer contexts. Future developments include training larger models like Q-RWKV-6 72B and LLaMA-RWKV-7 70B, with plans to publish detailed conversion methodologies and a comprehensive paper.

Guide: Running Locally

1. Setup Environment: Install necessary libraries like transformers and safetensors.
2. Download Model: Access the model files via the Hugging Face model card or use the provided links.
3. Load Model: Use the transformers library to load and run the model locally.
4. Inference: Implement custom code to perform inference tasks using the model.

For optimal performance, consider utilizing cloud GPU services, such as TensorWave, which offers access to advanced hardware like the MI300X.

License

This model is licensed under the Apache-2.0 License, allowing for broad use and modification with proper attribution.