---
title: MiniMax-M3-EAGLE3
canonical_url: "https://www.modelscope.cn/models/Inferact/MiniMax-M3-EAGLE3"
md_url: "https://www.modelscope.cn/models/Inferact/MiniMax-M3-EAGLE3.md"
repository: Inferact/MiniMax-M3-EAGLE3
last_updated: 2026-06-13
license: mit
pipeline_tag: text-generation
tasks:
  - text-generation
model_type:
  - llama
architectures:
  - LlamaForCausalLMEagle3
base_model:
  - MiniMaxAI/Minimax-M3-preview
base_model_relation: finetune
parameters: 3.3B
tensor_type:
  - BF16
library_name:
  - safetensors
  - pytorch
frameworks:
  - pytorch
downloads: 507
stars: 3
tags:
  - eagle3
  - speculative-decoding
  - draft-model
  - vllm
  - torchspec
  - minimax
---

# MiniMax-M3-EAGLE3

> MiniMax-M3-EAGLE3 - Inferact 在 ModelScope 开源的模型。Inferact/MiniMax-M3-EAGLE3 is an EAGLE3 draft model for accelerating inference of MiniMax-M3. It is served end-to-end with vLLM and was trained using TorchSpec — a torch-native online speculative-decoding…

Inferact/MiniMax-M3-EAGLE3 是 ModelScope 魔搭社区上的 3.3B 参数text-generation模型，采用 mit 许可，基于 MiniMaxAI/Minimax-M3-preview 构建。

- **Repository**: Inferact/MiniMax-M3-EAGLE3
- **License**: mit
- **Tasks**: text-generation
- **Parameters**: 3.3B
- **Base model**: MiniMaxAI/Minimax-M3-preview
- **Tags**: eagle3, speculative-decoding, draft-model, vllm, torchspec, minimax
- **Downloads**: 507
- **Stars**: 3
- **Last updated**: 2026-06-13

Source: https://www.modelscope.cn/models/Inferact/MiniMax-M3-EAGLE3

---

## Model Overview

**Inferact/MiniMax-M3-EAGLE3** is an EAGLE3 draft model for accelerating inference of [MiniMax-M3](https://huggingface.co/MiniMaxAI/MiniMax-M3). It is served end-to-end with **[vLLM](https://github.com/vllm-project/vllm)** and was trained using **[TorchSpec](https://github.com/lightseekorg/TorchSpec)** — a torch-native online speculative-decoding training framework that runs FSDP training and vLLM-based target inference concurrently, learning from **MiniMax-M3-regenerated responses and live vLLM-generated hidden states** to match the base model's exact token distribution.

The draft is a **1-layer** dense Llama (`LlamaForCausalLMEagle3`, ~3.3 B params) operating 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

All numbers are measured end-to-end against `MiniMaxAI/MiniMax-M3-MXFP8` served with vLLM at `tensor-parallel-size=4`, `num_speculative_tokens=3`, and `--enforce-eager`. Greedy draft sampling (`topk=1`).

| Category | Dataset | n | Mean Accept Length | Draft Accept Rate | Per-pos Accept Rate |
|---|---|---:|---:|---:|---|
| Dialogue | [MT-Bench](https://github.com/lm-sys/FastChat/tree/main/fastchat/llm_judge) | 80 | 2.698 | 56.60% | 0.749, 0.547, 0.402 |
| Math | [GSM8K](https://github.com/openai/grade-school-math) | 200 | 3.518 | 83.93% | 0.923, 0.839, 0.756 |
| Code | [HumanEval](https://huggingface.co/datasets/openai/openai_humaneval) | 164 | 3.499 | 83.29% | 0.922, 0.832, 0.744 |
| Math | [MATH500](https://huggingface.co/datasets/HuggingFaceH4/MATH-500) | 500 | 3.517 | 83.90% | 0.929, 0.841, 0.747 |
| Math | [AIME](https://huggingface.co/datasets/Maxwell-Jia/AIME_2024) | 30 | 3.291 | 76.36% | 0.889, 0.763, 0.638 |
| Synthetic | speed-bench (16k, low-entropy) | 64 | 2.776 | 59.21% | 0.747, 0.576, 0.453 |

---

## Training

**Data:** ~456,881 training conversations (the `mix2` dataset: SWE-bench-Pro, SWE-bench, OpenCodeInstruct, kimi-mtp), with **all responses regenerated by MiniMax-M3** — preserving the target's reasoning traces and MiniMax-M3 chat formatting.

**Method:** EAGLE3 TTT, `ttt_length=7`, `max_seq_length=32 768`, AdamW at `lr=1 × 10⁻⁴` (cosine decay to 0, 2 % warmup, `max_grad_norm=1.0`), bf16 + gradient checkpointing, FlexAttention, 1 epoch (~14,277 steps). Trained on **5 × GB300 nodes** (2 nodes FSDP2 draft training, dp=8, global batch 32 + 3 nodes vLLM TP=4 target inference). EAGLE3 aux hidden states from target layers (2, 30, 57) + the final layer. Embedding / LM head / final norm are shared from the target (M3 is a VL model, so these live under the `language_model.*` prefix).

**Core training command** — `torchspec.train_entry` spawns the FSDP2 trainer and vLLM inference engines as decoupled Ray actors, streaming hidden states through Mooncake:

```bash
python3 -m torchspec.train_entry \
  --config configs/vllm_minimax_m3_mix2.yaml \
  model.draft_model_config=configs/draft_models/minimax_m3_eagle3.json \
  training.training_num_nodes=2 \
  training.training_num_gpus_per_node=4 \
  inference.inference_num_gpus=12 \
  inference.inference_num_gpus_per_engine=4 \
  inference.vllm.tp_size=4
```

Draft architecture, TTT depth, sequence length, cluster layout, and optimizer are all YAML-configurable — retargeting or scaling is a config change. See the [TorchSpec repo](https://github.com/lightseekorg/TorchSpec) for full customization instructions.

---

## Quick Start

### Requirements

- vLLM nightly with MiniMax-M3 support
- Docker image `vllm/vllm-openai:minimax-m3`


### Launch Server (vLLM)

```bash
vllm serve MiniMaxAI/MiniMax-M3-MXFP8 \
  --tensor-parallel-size 4 \
  --gpu-memory-utilization 0.90 \
  --block-size 128 \
  --speculative-config '{"method": "eagle3", "model": "Inferact/MiniMax-M3-EAGLE3", "num_speculative_tokens": 3, "attention_backend": "FLASH_ATTN"}'
```