| name | angelslim |
| description | Tencent 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. |
| tags | ["angelslim","model-compression","quantization","pruning","speculative-decoding","tencent","zorai"] |
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
model = slim.quantize(model, dtype="fp8_static", qconfig="default")
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)
slim.eagle3.train(model, draft_model_config)
output = model.generate_with_eagle3(input_ids, max_new_tokens=256)
References
