Instructions to use cyankiwi/MiniMax-M2.1-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use cyankiwi/MiniMax-M2.1-AWQ-4bit with Transformers:

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

pipe = pipeline("text-generation", model="cyankiwi/MiniMax-M2.1-AWQ-4bit", 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("cyankiwi/MiniMax-M2.1-AWQ-4bit", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("cyankiwi/MiniMax-M2.1-AWQ-4bit", 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 cyankiwi/MiniMax-M2.1-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "cyankiwi/MiniMax-M2.1-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "cyankiwi/MiniMax-M2.1-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/cyankiwi/MiniMax-M2.1-AWQ-4bit

SGLang

How to use cyankiwi/MiniMax-M2.1-AWQ-4bit 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 "cyankiwi/MiniMax-M2.1-AWQ-4bit" \
    --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": "cyankiwi/MiniMax-M2.1-AWQ-4bit",
		"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 "cyankiwi/MiniMax-M2.1-AWQ-4bit" \
        --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": "cyankiwi/MiniMax-M2.1-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use cyankiwi/MiniMax-M2.1-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/cyankiwi/MiniMax-M2.1-AWQ-4bit
```

PPL test

by bullerwins - opened Jan 27

Discussion

bullerwins

Jan 27

Hi!

How do you run the PPL test to test quality? Have you found PPL test to be the better test instead of regular benchmarks for example?

Thanks :)

cpatonn

cyankiwi org Jan 27

Thank you for your interest in my model!

My PPL test measures byte perplexity on the wikitext dataset, derived from EleutherAI/lm-evaluation-harness.

I use the PPL test to measure how the quantized model differs from the original model facing general text i.e., the wikitext dataset. Using benchmarks of specific domains e.g., GPQA Diamond, AIME25, livecodebench, etc also works, but it takes significantly more time, and especially when following Artificial Analysis standards i.e., evaluating GPQA Diamond 5 times, AIME25 10 times, etc.

In the future, the evaluations for my models would be more inclusive, so stay tuned :)

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