Instructions to use djdeniro/MiniMax-M2.7-MXFP416 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use djdeniro/MiniMax-M2.7-MXFP416 with Transformers:

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

pipe = pipeline("text-generation", model="djdeniro/MiniMax-M2.7-MXFP416", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("djdeniro/MiniMax-M2.7-MXFP416", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("djdeniro/MiniMax-M2.7-MXFP416", trust_remote_code=True, device_map="auto")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use djdeniro/MiniMax-M2.7-MXFP416 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "djdeniro/MiniMax-M2.7-MXFP416"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djdeniro/MiniMax-M2.7-MXFP416",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/djdeniro/MiniMax-M2.7-MXFP416

SGLang

How to use djdeniro/MiniMax-M2.7-MXFP416 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 "djdeniro/MiniMax-M2.7-MXFP416" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djdeniro/MiniMax-M2.7-MXFP416",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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 "djdeniro/MiniMax-M2.7-MXFP416" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "djdeniro/MiniMax-M2.7-MXFP416",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use djdeniro/MiniMax-M2.7-MXFP416 with Docker Model Runner:
```
docker model run hf.co/djdeniro/MiniMax-M2.7-MXFP416
```

MiniMax-M2.7-MXFP416 / config.json

djdeniro

Upload config.json

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	{
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	"auto_map": {
	"AutoConfig": "configuration_minimax_m2.MiniMaxM2Config",
	"AutoModelForCausalLM": "modeling_minimax_m2.MiniMaxM2ForCausalLM"
	},
	"dtype": "bfloat16",
	"head_dim": 128,
	"hidden_act": "silu",
	"hidden_size": 3072,
	"intermediate_size": 1536,
	"max_position_embeddings": 204800,
	"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,
	"qk_norm_type": "per_layer",
	"quantization_config": {
	"quant_method": "mxfp4_16",
	"format": "mxfp4-16-pack-quantized",
	"quantization_status": "compressed",
	"config_groups": {
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	"symmetric": false,
	"scale_dtype": "torch.float16",
	"dynamic": false,
	"actorder": null,
	"block_structure": null,
	"observer": "minmax",
	"observer_kwargs": {},
	"zp_dtype": null
	},
	"targets": [
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	],
	"input_activations": null,
	"output_activations": null
	}
	},
	"ignore": [
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	"re:.e_score_correction_bias.",
	"re:.lm_head.",
	"re:.embed_tokens.",
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	"kv_cache_scheme": null,
	"sparsity_config": {},
	"transform_config": {}
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	"rms_norm_eps": 1e-06,
	"rope_theta": 5000000,
	"rotary_dim": 64,
	"scoring_func": "sigmoid",
	"shared_intermediate_size": 0,
	"tie_word_embeddings": false,
	"transformers_version": "4.46.1",
	"use_cache": true,
	"use_mtp": true,
	"use_qk_norm": true,
	"use_routing_bias": true,
	"vocab_size": 200064
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