optimization-awq
Activation-aware weight quantization for 4-bit LLM compression with 3x speedup and minimal accuracy loss. Use when deploying large models (7B-70B) on limited GPU memory, when you need faster inference than GPTQ with better accuracy preservation, or for instruction-tuned and multimodal models. MLSys 2024 Best Paper Award winner.
| Field | Value |
|---|---|
| Owner | davila7 |
| Curator | Thomas Cherickal |
| Source | davila7/claude-code-templates |
| Repo stars | 33,617 |
| License | MIT |
| Repo last push | 2026-11-14 |
Links
- Roles: local-ai-engineer
- Source repo: davila7/claude-code-templates
- Same repo: eval-genius, evaluation-bigcode-evaluation-harness, evaluation-lm-evaluation-harness, evaluation-nemo-evaluator, prompt-engineering-dspy, distributed-training-deepspeed, distributed-training-megatron-core, distributed-training-pytorch-fsdp
- Registry seed skills for this role: local-ai-engineer seed skills