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license: apache-2.0 base_model: Qwen/Qwen2.5-Coder-14B-Instruct tags: - bitnet - quantization - ternary - 1.58-bit - qwen - qwen2.5 - code - experimental - 14b-architecture library_name: safetensors pipeline_tag: text-generation language: - en - zh model_name: Qwen2.5-Coder-14B-BitNet-1.58b datasets: [] metrics: []

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Qwen2.5-Coder-14B-Instruct-BitNet-1.58b

Architecture: 14.7 Billion Parameters | BitNet 1.58-bit Ternary Quantization

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IMPORTANT: Parameter Count Display

>

HuggingFace displays a reduced parameter count because it counts packed bytes, not actual parameters.

This model has the full 14.7B parameter Qwen2.5-Coder architecture.

The weights are stored as ternary values ({-1, 0, +1}) packed 4 per byte, which reduces

storage to 4.6 GB but preserves all 14.7 billion parameters.

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Overview

This is an experimental BitNet 1.58-bit quantization of the Qwen2.5-Coder-14B-Instruct model using absmean scaling with group-wise quantization. The model stores weights as ternary values ({-1, 0, +1}) packed 4 values per byte.

This is research/experimental work. Quality and performance have not been formally benchmarked.

Specifications

Property	Value
Base Model	Qwen/Qwen2.5-Coder-14B-Instruct
Architecture	Qwen2 (Qwen2ForCausalLM)
Parameters	14.7B
Quantization	BitNet 1.58-bit ternary
Bits per Weight	~1.58
Group Size	64
Original Size	29.54 GB (BF16)
Quantized Size	4.62 GB (SafeTensors)
GGUF Size	6.52 GB (TQ2_0)
Compression	~6.4x

Formats

Format	File	Description
SafeTensors	model-*.safetensors	Sharded quantized weights + scales
GGUF	qwen-coder-14b-tq2.gguf	llama.cpp compatible

Quantization Method

Algorithm

1. Reshape weights into groups of 64
2. Compute per-group scale: scale = mean(|weights|)
3. Normalize and round to nearest ternary: q = round(w / scale) clamped to {-1, 0, +1}
4. Map to unsigned: {-1, 0, +1} → {0, 1, 2}
5. Pack 4 values per byte: v0 + v13 + v29 + v3*27

Tooling

• Quantization: Custom Rust tool using Candle
• GGUF Conversion: llama.cpp converthfto_gguf.py

Hardware Used

• GPU: NVIDIA RTX 5080 (16GB VRAM)
• Quantization time: ~99 seconds
• Memory: Streaming mode with CPU fallback for large tensors

Usage

With Ollama/llama.cpp

# llama.cpp
./llama-cli -m qwen-coder-14b-tq2.gguf -p "Write a Python function:"

Unpacking Weights (Python)

def unpackternary(packedbyte):
    """Unpack 4 ternary values from byte."""
    values = []
    val = packed_byte
    for _ in range(4):
        values.append((val % 3) - 1)  # {0,1,2} → {-1,0,+1}
        val //= 3
    return values

Limitations

• Quality not benchmarked - May have significant degradation vs original
• Requires custom runtime - Standard transformers doesn't support ternary weights
• Experimental - Not intended for production use without evaluation
• GGUF keeps embeddings/lm_head at F16, hence larger than SafeTensors

License

Apache 2.0 (inherited from Qwen2.5-Coder-14B-Instruct)

Citation

@misc{qwen-coder-bitnet-2025,
  title={Qwen2.5-Coder-14B-BitNet-1.58b: Experimental BitNet Quantization},
  author={Tzervas},
  year={2025},
  url={https://huggingface.co/tzervas/qwen2.5-coder-14b-bitnet-1.58b}
}