Understanding Transformers at the Metal Level with Qwen3.5 in C
This article explores a pure C implementation of the Qwen3.5 language model, which uses a hybrid attention architecture combining multi-head attention and linear attention. The project aims to provide a low-level understanding of how transformers work, without relying on deep learning frameworks like PyTorch.
Why it matters
This project provides a refreshing alternative to the typical deep learning framework-based approach, allowing developers to gain a deeper, more fundamental understanding of how transformer models work.
Key Points
- 1Qwen3.5 employs a hybrid attention mechanism with multi-head attention and linear attention
- 2The project loads model weights directly from Hugging Face's safetensors format without using PyTorch
- 3The goal is to provide a deep, low-level understanding of transformer models by stripping away abstraction layers
Details
The article discusses a C-based implementation of the Qwen3.5 language model called Qwen35.c. This project follows in the footsteps of similar educational implementations like llama2.c and mamba.c, which aim to provide a deeper understanding of transformer models by removing the abstraction layers of deep learning frameworks. Qwen3.5 is unique in that it uses a hybrid attention architecture, combining the classic multi-head attention mechanism with a linear attention approach called GatedDeltaNet. This hybrid design allows the model to be both powerful and efficient, with the multi-head attention providing strong pattern matching and the linear attention maintaining state efficiently across long sequences. The article highlights the technical achievement of loading the model weights directly from Hugging Face's safetensors format without using PyTorch, demonstrating a low-level understanding of the underlying tensor operations.
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