Instructions to use Inferact/MiniMax-M3-EAGLE3-GQA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Inferact/MiniMax-M3-EAGLE3-GQA with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="Inferact/MiniMax-M3-EAGLE3-GQA")

# Load model directly
from transformers import AutoTokenizer, LlamaForCausalLMEagle3

tokenizer = AutoTokenizer.from_pretrained("Inferact/MiniMax-M3-EAGLE3-GQA")
model = LlamaForCausalLMEagle3.from_pretrained("Inferact/MiniMax-M3-EAGLE3-GQA")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Inferact/MiniMax-M3-EAGLE3-GQA with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Inferact/MiniMax-M3-EAGLE3-GQA"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inferact/MiniMax-M3-EAGLE3-GQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Inferact/MiniMax-M3-EAGLE3-GQA

SGLang

How to use Inferact/MiniMax-M3-EAGLE3-GQA with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "Inferact/MiniMax-M3-EAGLE3-GQA" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inferact/MiniMax-M3-EAGLE3-GQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "Inferact/MiniMax-M3-EAGLE3-GQA" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Inferact/MiniMax-M3-EAGLE3-GQA",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Inferact/MiniMax-M3-EAGLE3-GQA with Docker Model Runner:
```
docker model run hf.co/Inferact/MiniMax-M3-EAGLE3-GQA
```

MiniMax-M3-EAGLE3-GQA / README.md

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Add per-position accept rate to Performance table

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	---
	license: mit
	library_name: transformers
	base_model: MiniMaxAI/Minimax-M3-preview
	pipeline_tag: text-generation
	tags:
	- eagle3
	- speculative-decoding
	- draft-model
	- gqa
	- vllm
	- torchspec
	- minimax
	---

	## Model Overview

	Inferact/MiniMax-M3-EAGLE3-GQA is a grouped-query-attention (GQA) EAGLE3 draft model for accelerating inference of [MiniMax-M3](https://huggingface.co/MiniMaxAI/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](https://huggingface.co/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`.

	\| Dataset \| n \| Mean accepted length \| Draft accept rate \| Per-position accept rate (pos 1 / 2 / 3) \|
	\|---\|---:\|---:\|---:\|---:\|
	\| MT-Bench \| 64 \| 2.668 \| 55.62% \| 0.745 / 0.537 / 0.387 \|
	\| SPEED-Bench (qualitative) \| 64 \| 2.561 \| 52.04% \| 0.719 / 0.500 / 0.342 \|