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简体中文

MiniMax-M1 Model

I. Environment Preparation

1.1 Support Requirements

MiniMax-M1 support in FastDeploy uses a hybrid decoder stack. Details:

  • Standard full-attention layers run through the existing FastDeploy attention backend.
  • Linear-attention layers use the Lightning Attention Triton kernels in fastdeploy/model_executor/ops/triton_ops/lightning_attn.py.
  • Current first-pass support targets BF16 inference.

1.2 Installing FastDeploy

Installation process reference document FastDeploy GPU Installation

II. How to Use

2.1 Basics: Starting the Service

MODEL_PATH=/models/MiniMax-Text-01

python -m fastdeploy.entrypoints.openai.api_server \
    --model "$MODEL_PATH" \
    --port 8180 \
    --metrics-port 8181 \
    --engine-worker-queue-port 8182 \
    --max-model-len 32768 \
    --max-num-seqs 32

2.2 Model Notes

  • HuggingFace architecture: MiniMaxText01ForCausalLM
  • Hybrid layer layout: 70 linear-attention layers and 10 full-attention layers
  • MoE routing: 32 experts, top-2 experts per token

III. Known Limitations

  • This initial integration is focused on model structure and backend wiring.
  • Linear attention KV history uses instance variables, which needs migration to slot-based cache for proper multi-request isolation (TODO already noted in code).
  • Low-bit quantization support still requires follow-up validation against MiniMax-M1 weights.
  • Production validation should include GPU runtime checks for Lightning Attention decode/prefill paths.