Tencent/AngelSlim

Health v2 · maintenance only

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1547 stars · 172 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

AngelSlim is a Python toolkit from Tencent for compressing large language models and related architectures (VLMs, diffusion, audio models) via quantization, distillation, and speculative decoding. It provides PTQ/QAT pipelines, quantization-aware distillation on Megatron-Core, and acceleration techniques like sparse attention and draft-model-based decoding.

Use cases

• quantize an LLM to FP8 or FP4 for faster inference
• compress a large model with post-training quantization
• run quantization-aware distillation for Qwen or Hunyuan models
• speed up LLM generation with speculative decoding
• train a draft model for speculative decoding
• accelerate long-context prefill with sparse attention
• distill a full-precision model into a smaller one

When to choose

• you need to compress or quantize LLMs, VLMs, or diffusion models for deployment
• you want speculative decoding with MTP, EAGLE, or DFly drafters
• you work with Tencent Hunyuan, DeepSeek, or Qwen model families
• you need distributed quantization-aware distillation on Megatron-Core

When to avoid

• you need compression for small classical ML models rather than large neural networks
• you want a one-click GUI tool with no Python scripting
• your target hardware does not support low-precision formats like FP8/FP4

Facets

library · maturity active

machine-learning llm-inference llm-training deep-learning large-language-models machine-learning deep-learning developer-tools python model-compression quantization speculative-decoding distillation ptq qat sparse-attention vlm diffusion-models gpu linux

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markdown · JSON · MCP: product_card(name="Tencent/AngelSlim")