MODEL PROFILE

qwen2.5-coder-32b-bitnet-1.58b

tzervas · qwen2.5-coder-32b-bitnet-1.58b

> **IMPORTANT: Parameter Count Display** > > HuggingFace displays "9B params" because it counts packed bytes, not actual parameters. > This model has the **full 32B parameter Qwen2.5-Coder architecture**. > The weights are stored as ternary values ({-1, 0, +1}) packed 4 per byte, which reduces > storage to 9.6 GB but preserves all 32 billion parameters. This is an **experimental** BitNet 1.58-bit quantization of the Qwen2.5-Coder-32B-Instruct model using absmean scaling with group-wise quantization. The model stores weights as ternary values ({-1, 0, +1}) packed 4 values per byte. > **GGUF Compatibility Note**: The GGUF conversion is experimental. Our BitNet quantization uses group size 64, while TQ2_0 uses 256-element blocks. This may cause compatibility issues with some inference engines. The SafeTensors format is the primary supported format.

Text generationcodelicense:apache-2.0
Context windowNot disclosed
Input → OutputText → Text
Model weightsNot verified
Profile updated2026/11/2 19:15:40
OVERVIEW

Model overview

> **IMPORTANT: Parameter Count Display** > > HuggingFace displays "9B params" because it counts packed bytes, not actual parameters. > This model has the **full 32B parameter Qwen2.5-Coder architecture**. > The weights are stored as ternary values ({-1, 0, +1}) packed 4 per byte, which reduces > storage to 9.6 GB but preserves all 32 billion parameters. This is an **experimental** BitNet 1.58-bit quantization of the Qwen2.5-Coder-32B-Instruct model using absmean scaling with group-wise quantization. The model stores weights as ternary values ({-1, 0, +1}) packed 4 values per byte. > **GGUF Compatibility Note**: The GGUF conversion is experimental. Our BitNet quantization uses group size 64, while TQ2_0 uses 256-element blocks. This may cause compatibility issues with some inference engines. The SafeTensors format is the primary supported format.

Key features

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Limitations & considerations

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PRICING & COST

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INDEPENDENT BENCHMARKS

Public benchmarks & run configuration

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Sources & traceability

许可证:apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。