Inferact/MiniMax-M3-EAGLE3-GQA

Model Overview

Inferact/MiniMax-M3-EAGLE3-GQA is a grouped-query-attention (GQA) EAGLE3 draft model for accelerating inference of MiniMax-M3, served with [vLLM](https://github.com/vllm-project/vllm) and trained with [TorchSpec](https://github.com/lightseekorg/TorchSpec).

It is retrained on the same datasets as the multi-head-attention version Inferact/MiniMax-M3-EAGLE3 — kimi-mtp, OpenCodeInstruct, SWE-bench, and SWE-bench-Pro — with the draft's attention changed from MHA to GQA (num_key_value_heads: 64 → 4) for inference efficiency (16× smaller draft KV cache) and compatibility with the target model.

The draft is a 1-layer dense Llama (LlamaForCausalLMEagle3) on MiniMax-M3's hidden_size=6144 / vocab_size=200064; at serve time it shares the target's embedding and LM head (EAGLE3). See config.json for the full architecture.

Performance

Mean accepted length and draft accept rate measured end-to-end against MiniMaxAI/MiniMax-M3-MXFP8 served with vLLM at tensor-parallel-size=4, num_speculative_tokens=3, greedy sampling (temperature=0, top_p=1.0), max-concurrency=16.