auto-round
Auto‑Round quantizes large language models to low‑bit (e.g., 4‑bit) weights while keeping accuracy comparable to full‑precision inference.
- •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.
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
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/auto-round && curl -fsSL https://workflowstacks.com/api/skills/auto-round/claude-skill -o ~/.claude/skills/auto-round/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
Skip the builder — one click puts this in Claude, Cursor, Antigravity and more.
Installs with a command or two; your AI agent can do it for you.
mkdir -p ~/.claude/skills/auto-round && curl -fsSL https://workflowstacks.com/api/skills/auto-round/claude-skill -o ~/.claude/skills/auto-round/SKILL.mdOpens the app with this repo with the prompt ready to go — no copy-paste needed.
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