minjaechoi/qwen36-35b-a3b-2p02bit-r3
The minjaechoi/qwen36-35b-a3b-2p02bit-r3 is a 35.1 billion parameter language model based on the Qwen3.6-35B-A3B architecture. This model features an experimental quantization approach, utilizing routed experts with an average of 2.02 bits per weight, while other weights remain in BF16 format. It is designed for efficient inference by storing weights dequantized in BF16 tensors, compatible with standard transformers and vLLM libraries. This configuration aims to optimize performance and resource usage for large-scale language processing tasks.
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Overview
The minjaechoi/qwen36-35b-a3b-2p02bit-r3 is an internal research checkpoint derived from the Qwen3.6-35B-A3B base model. This 35.1 billion parameter model explores an advanced quantization technique to enhance efficiency.
Key Characteristics
- Base Model: Built upon the robust Qwen/Qwen3.6-35B-A3B architecture.
- Quantization: Implements a novel "routed experts" approach, achieving an average of 2.02 bits per weight for these specialized components.
- Mixed Precision: While routed experts are highly quantized, all other weights are maintained in BF16 (Brain Floating Point) format.
- Compatibility: Weights are stored dequantized in BF16 tensors, ensuring seamless loading and operation with standard libraries like
transformersandvLLM. - Context Length: Supports a substantial context window of 32768 tokens.
What Makes This Model Different?
This model stands out due to its experimental 2.02-bit routed experts quantization strategy. Unlike typical uniform quantization, this approach selectively quantizes parts of the model (the "routed experts") to a very low bit-depth, while retaining higher precision (BF16) for other parameters. This hybrid method aims to strike a balance between model size reduction, inference speed, and maintaining performance, making it a valuable exploration in efficient large language model deployment.
Potential Use Cases
Given its experimental quantization and compatibility with standard inference frameworks, this model is particularly suited for:
- Research and Development: Ideal for researchers exploring advanced quantization techniques and their impact on LLM performance.
- Efficient Deployment: Potentially useful for applications requiring a large language model with reduced memory footprint and faster inference, without sacrificing too much accuracy compared to full-precision models.
- Benchmarking: Can serve as a benchmark for evaluating the effectiveness of mixed-precision and routed-expert quantization strategies in real-world scenarios.