Instructions to use MiniMaxAI/MiniMax-M2.5 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/MiniMax-M2.5 with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/MiniMax-M2.5", 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("MiniMaxAI/MiniMax-M2.5", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/MiniMax-M2.5", 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]:]))

Inference
HuggingChat
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MiniMaxAI/MiniMax-M2.5 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/MiniMax-M2.5"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "MiniMaxAI/MiniMax-M2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/MiniMax-M2.5

SGLang

How to use MiniMaxAI/MiniMax-M2.5 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-M2.5" \
    --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": "MiniMaxAI/MiniMax-M2.5",
		"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 "MiniMaxAI/MiniMax-M2.5" \
        --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": "MiniMaxAI/MiniMax-M2.5",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/MiniMax-M2.5 with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/MiniMax-M2.5
```

MiniMax-M2.5 MLX q3/q4 uploaded

by ahoybrotherbear - opened Feb 13

Discussion

ahoybrotherbear

Feb 13

MiniMax just dropped M2.5 today (229B MoE, 10B active params, 80.2% SWE-Bench Verified) and I quantized it to MLX within a couple hours of release. First time doing this!

My quants:

4-bit MLX -- ~129GB, needs 256GB+ RAM variant
3-bit MLX -- ~86GB, runs tight on 96GB machines

Other MLX quants out there:

mlx-community/MiniMax-M2.5-4bit -- the bot got there a few minutes before me lol
inferencerlabs 6.5-bit and 9-bit -- higher quality quants using modified MLX

Performance on M3 Ultra 512GB:

~53 tokens/sec generation (4-bit)
~54 tokens/sec generation (3-bit)
~128GB peak memory (4-bit), ~100GB peak memory (3-bit)

Quality note on the 3-bit: inferencerlabs' testing shows significant quality degradation below 4 bits for this model (43% token accuracy at q3.5 vs 91%+ at q4.5). I tested my 3-bit on coding and reasoning tasks and it produced coherent, correct output, but it's definitely not as sharp as 4-bit. Think of it as the smallest viable quant for people who can't fit the 4-bit version. 2-bit was completely unusable (infinite repetition loops).

Converted with mlx-lm v0.30.7. Happy to answer questions if anyone else wants to try running this locally - cheers!

ubergarm

Feb 13

@ahoybrotherbear

I don't see your actual quants in your repo, are they in different branches or something?

Also, what is the exact recipe you used? I'm not as familiar with mlx-lm, but in my own testing with GGUFs using mainline and ik_llama.cpp the smol-IQ3_KS 87.237 GiB (3.277 BPW) seems to be working with opencode okay at least anecdotally. Do you have a link for inferencerlabs' testing as I'm curious if it is possible to reproduce for a given quant.

Cheers!

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