Instructions to use Lasimeri/MiniMax-M2.7-int4-AutoRound with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Lasimeri/MiniMax-M2.7-int4-AutoRound with Transformers:

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

pipe = pipeline("text-generation", model="Lasimeri/MiniMax-M2.7-int4-AutoRound", trust_remote_code=True)
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Lasimeri/MiniMax-M2.7-int4-AutoRound", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("Lasimeri/MiniMax-M2.7-int4-AutoRound", trust_remote_code=True)
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

vLLM

How to use Lasimeri/MiniMax-M2.7-int4-AutoRound with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Lasimeri/MiniMax-M2.7-int4-AutoRound"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Lasimeri/MiniMax-M2.7-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Lasimeri/MiniMax-M2.7-int4-AutoRound

SGLang

How to use Lasimeri/MiniMax-M2.7-int4-AutoRound 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 "Lasimeri/MiniMax-M2.7-int4-AutoRound" \
    --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": "Lasimeri/MiniMax-M2.7-int4-AutoRound",
		"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 "Lasimeri/MiniMax-M2.7-int4-AutoRound" \
        --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": "Lasimeri/MiniMax-M2.7-int4-AutoRound",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Lasimeri/MiniMax-M2.7-int4-AutoRound with Docker Model Runner:
```
docker model run hf.co/Lasimeri/MiniMax-M2.7-int4-AutoRound
```

MiniMax-M2.7 INT4 AutoRound

4-bit quantized version of MiniMaxAI/MiniMax-M2.7 using Intel AutoRound.

Quantization Config

Setting	Value
Scheme	W4A16 (INT4 weights, FP16 activations)
Group size	128
Ignored layers	MoE `gate` layers (kept at full precision)
Method	RTN (iters=0)

Usage

vLLM

vllm serve Lasimeri/MiniMax-M2.7-int4-AutoRound \
  --trust-remote-code \
  --tensor-parallel-size 8 \
  --enable-auto-tool-choice \
  --tool-call-parser minimax_m2 \
  --reasoning-parser minimax_m2_append_think

SGLang

python -m sglang.launch_server \
  --model-path Lasimeri/MiniMax-M2.7-int4-AutoRound \
  --trust-remote-code \
  --tp 8 \
  --reasoning-parser minimax-append-think \
  --tool-call-parser minimax-m2

Quantization Hardware

Quantized on a single-node rig:

Component	Spec
CPU	AMD EPYC 7742 (64C / 128T)
RAM	251 GB DDR4
GPUs	8× RTX 3080 (20 GB modded)

Peak resource usage during quantization: ~25.6 GB RAM, ~5 GB VRAM on GPU 0, ~1.3 GB on each remaining GPU.

Downloads last month: 2,551

Safetensors

Model size

32B params

Tensor type

I32

F16

BF16

Model tree for Lasimeri/MiniMax-M2.7-int4-AutoRound

Base model

MiniMaxAI/MiniMax-M2.7

Quantized

(104)

this model