angelslim
SkillAI & modelsTencent AngelSlim — accessible, comprehensive, and efficient toolkit for large model compression. Quantization (FP8/INT4/NVFP4/1.25-bit), pruning, speculative decoding (Eagle3), and diffusion model compression.
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the angelslim skill
What this skill tells your AI
The instructions your AI receives, as published by mkurman/zorai in skills/scientific-skills/angelslim/SKILL.md and read by ahel’s review.
Overview
AngelSlim integrates mainstream compression algorithms into a unified framework with one-click access. Supports FP8/INT8/INT4/NVFP4/1.25-bit quantization, pruning, Eagle3 speculative decoding, and diffusion model compression for LLMs, VLMs, and audio models.
Installation
uv pip install angelslim
Basic Quantization (PTQ)
import angelslim as slim
# FP8 static quantization
model = slim.quantize(model, dtype="fp8_static", qconfig="default")
# INT4 GPTQ
model = slim.quantize(model, dtype="int4_gptq", dataset="wikitext2")
Compression Strategies
| Method | Precision | Best For |
|---|---|---|
| FP8-Static/Dynamic | 8-bit | General LLM deployment |
| INT4 GPTQ/AWQ/GPTAQ | 4-bit | Memory-constrained serving |
| NVFP4 | 4-bit (NVIDIA) | Blackwell GPUs |
| Sherry | 1.25-bit | Extreme compression |
| STQ1_0 | 1.25-bit | On-device deployment |
Speculative Decoding (Eagle3)
# Train Eagle3 draft model
slim.eagle3.train(model, draft_model_config)
# Inference with Eagle3
output = model.generate_with_eagle3(input_ids, max_new_tokens=256)
References
Signals
- GitHub stars
- 324
- Forks
- 26
- Last commit
- Sep 2026
Advanced
- Catalog kind
- skill
- Gateway key
angelslim- Source
- github.com/mkurman/zorai