optimization-gptq
Post-training 4-bit quantization for LLMs with minimal accuracy loss. Use for deploying large models (70B, 405B) on consumer GPUs, when you need 4× memory reduction with <2% perplexity degradation, or for faster inference (3-4× speedup) vs FP16. Integrates with transformers and PEFT for QLoRA fine-tuning.
| 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