Instructions to use MiniMaxAI/MiniMax-M2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use MiniMaxAI/MiniMax-M2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2")# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("MiniMaxAI/MiniMax-M2", dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use MiniMaxAI/MiniMax-M2 with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "MiniMaxAI/MiniMax-M2" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "MiniMaxAI/MiniMax-M2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/MiniMaxAI/MiniMax-M2
- SGLang
How to use MiniMaxAI/MiniMax-M2 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 "MiniMaxAI/MiniMax-M2" \ --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": "MiniMaxAI/MiniMax-M2", "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 "MiniMaxAI/MiniMax-M2" \ --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": "MiniMaxAI/MiniMax-M2", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use MiniMaxAI/MiniMax-M2 with Docker Model Runner:
docker model run hf.co/MiniMaxAI/MiniMax-M2
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"architectures": [
"MiniMaxM2ForCausalLM"
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"attention_dropout": 0.0,
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"auto_map": {
"AutoConfig": "configuration_minimax_m2.MiniMaxM2Config",
"AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM"
},
"bos_token_id": null,
"eos_token_id": null,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 3072,
"initializer_range": 0.02,
"intermediate_size": 1536,
"layernorm_full_attention_beta": 1.0,
"layernorm_linear_attention_beta": 1.0,
"layernorm_mlp_beta": 1.0,
"max_position_embeddings": 196608,
"mlp_intermediate_size": 8192,
"model_type": "minimax_m2",
"mtp_transformer_layers": 1,
"num_attention_heads": 48,
"num_experts_per_tok": 8,
"num_hidden_layers": 62,
"num_key_value_heads": 8,
"num_local_experts": 256,
"num_mtp_modules": 3,
"output_router_logits": false,
"qk_norm_type": "per_layer",
"quantization_config": {
"activation_scheme": "dynamic",
"fmt": "float8_e4m3fn",
"quant_method": "fp8",
"weight_block_size": [
128,
128
],
"modules_to_not_convert": [
"gate",
"e_score_correction_bias",
"lm_head"
]
},
"rms_norm_eps": 1e-06,
"rope_theta": 5000000,
"rotary_dim": 64,
"router_aux_loss_coef": 0.001,
"router_jitter_noise": 0.0,
"scoring_func": "sigmoid",
"shared_intermediate_size": 0,
"shared_moe_mode": "sigmoid",
"sliding_window": null,
"tie_word_embeddings": false,
"transformers_version": "4.57.1",
"use_cache": true,
"use_mtp": true,
"use_qk_norm": true,
"use_routing_bias": true,
"vocab_size": 200064
}
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