local-ai

auto-round

A SOTA quantization toolkit for high-accuracy low-bit LLM inference|简洁且高效的量化工具包
1,687 stars+42★ this week194 forksPythonUpdated 10/23/2026100% free · open source
What it does

Auto‑Round quantizes large language models to low‑bit (e.g., 4‑bit) weights while keeping accuracy comparable to full‑precision inference.

When to use it
  • •You need to run a GPT‑style model on a single GPU or CPU with limited VRAM.
  • •You want to cut inference cost without re‑training the model.
  • •You are deploying a pre‑trained LLM in a production service and need sub‑8‑bit speed‑up.
Ready-to-paste prompt
python -m auto_round.quantize --model_name_or_path meta-llama/Llama-2-7b-hf --output_dir ./llama2-7b-w4a8 --bits 4 --group_size 128 --scale_dtype fp16
Heads up: Auto‑Round requires PyTorch ≥ 2.0 compiled with the same CUDA version you plan to run on; mismatched CUDA/PT versions cause runtime crashes, so install `torch` via the official wheel (e.g., `pip install torch==2.2.0+cu121 -f https://download.pytorch.org/whl/torch_stable.html`).
Saves to your device
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✅ Light setup

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Claude Code
mkdir -p ~/.claude/skills/auto-round && curl -fsSL https://workflowstacks.com/api/skills/auto-round/claude-skill -o ~/.claude/skills/auto-round/SKILL.md
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Details
Creator
intel
Language
Python
Category
local-ai
Published
1/4/2024

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