Individual skill
hqq-quantization
Half-Quadratic Quantization for LLMs without calibration data. Use when quantizing models to 4/3/2-bit precision without needing calibration datasets, for fast quantization workflows, or when deploying with vLLM or HuggingFace Transformers.
10-optimization/hqq/SKILL.md
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- ai Repository topic
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- claude Repository topic
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- codex Repository topic
- gemini Repository topic
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- quantization Declared skill tag
- hqq Declared skill tag
- optimization Declared skill tag
- memory-efficiency Declared skill tag
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# HQQ - Half-Quadratic Quantization
Fast, calibration-free weight quantization supporting 8/4/3/2/1-bit precision with multiple optimized backends.
## When to use HQQ
**Use HQQ when:**
- Quantizing models without calibration data (no dataset needed)
- Need fast quantization (minutes vs hours for GPTQ/AWQ)
- Deploying with vLLM or HuggingFace Transformers
- Fine-tuning quantized models with LoRA/PEFT
- Experimenting with extreme quantization (2-bit, 1-bit)
**Key advantages:**