轻松训练推测解码模型,并顺利移植到SGLang服务。
eagle3 开源项目推荐(13 个)
eagle3 话题在 GitHub 上没有填写简介,这里按 Star 数汇总最相关的热门仓库。
热门项目 全部 13
GitHub 共 13 个仓库,已全部展示A PyTorch native library for training speculative decoding models
A high-throughput training framework for Speculative decoding models.
AMD ROCm (gfx1030) inference fork with RotorQuant/TurboQuant KV compression, PHANTOM-X zero-copy draft speculation, EAGLE3 speculative decoding, 12 RDNA2 crash fixes, and PrismML Bonsai Q1_0_G128 1-bit GGUF support.
This is a research project to efficiently accelerate speculative decoding with token-level cross-paradigm routing, accepted at EMNLP 2026.
Train and adapt speculative decoding drafts for LLM inference: DSpark, DFlash, DFlash2, EAGLE3, and official EAGLE4 checkpoints.
JAX/TPU-native training framework for EAGLE3 speculative decoding draft models
Deployment guide: Qwen3-VL-8B on Jetson AGX Thor with NVIDIA TensorRT-Edge-LLM — NVFP4 quantization and ONNX export on x86, native TensorRT engine build on Thor, EAGLE-3 speculative decoding (67–92 tok/s)
Some benchmarks on running speculative decoding with Qwen-3.6-35B-A3B with EAGLE3, DFlash and MTP.
Controlled EAGLE-3 draft adaptation for frozen Qwen3-8B: Rust latency, replication, scaling controls, and serving trade-offs.
Retrained EAGLE-3 draft head for Spark-X2.6-4B: draft acceptance on Chinese prompts. Rebuilt 48K draft vocabulary, trained on the deployed Q6_K weights.
Mistral Small 4 119B NVFP4 + EAGLE-3 on NVIDIA Jetson AGX Thor with vLLM — TRITON_MLA, chat/reasoning_effort patches for the Jetson image, 33.7 tok/s (1.93x)
Воспроизведение EAGLE-3 на Qwen3: где спекулятивное декодирование ускоряет, а где мешает. Два ноутбука, полный прогон на Kaggle T4.
关于这个话题
- 清单取自 GitHub 话题页并按 Star 数排序,每次展示前 13 个项目;原始页面见 https://github.com/topics/eagle3。
- 该话题在 GitHub 上共有 13 个仓库, 本站收录的是其中最热门的 13 个,不是全量列表。
- Star / Fork 每日自动刷新一次,实时数据以 GitHub 为准。
- 当前 13 个项目累计 1618 Star,覆盖 3 种主要语言,数据更新于 2026-11-08。
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