Instructions to use yujiepan/minimax-m2-tiny-random with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use yujiepan/minimax-m2-tiny-random with Transformers:

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

pipe = pipeline("text-generation", model="yujiepan/minimax-m2-tiny-random", 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("yujiepan/minimax-m2-tiny-random", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained("yujiepan/minimax-m2-tiny-random", 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 yujiepan/minimax-m2-tiny-random with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "yujiepan/minimax-m2-tiny-random"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "yujiepan/minimax-m2-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/yujiepan/minimax-m2-tiny-random

SGLang

How to use yujiepan/minimax-m2-tiny-random 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 "yujiepan/minimax-m2-tiny-random" \
    --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": "yujiepan/minimax-m2-tiny-random",
		"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 "yujiepan/minimax-m2-tiny-random" \
        --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": "yujiepan/minimax-m2-tiny-random",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use yujiepan/minimax-m2-tiny-random with Docker Model Runner:
```
docker model run hf.co/yujiepan/minimax-m2-tiny-random
```

yujiepan commited on Nov 22, 2025

Commit

0eaa016

verified ·

1 Parent(s): a686afe

Upload folder using huggingface_hub

Browse files

Files changed (1) hide show

README.md +9 -3

README.md CHANGED Viewed

@@ -8,9 +8,16 @@ This tiny model is intended for debugging. It is randomly initialized using the
 ### Example usage:
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 model_id = "yujiepan/minimax-m2-tiny-random"
@@ -31,9 +38,8 @@ print(pipe('Write an article about Artificial Intelligence.'))
 import json
 from pathlib import Path
-import torch
 import accelerate
 from huggingface_hub import file_exists, hf_hub_download
 from transformers import (
     AutoConfig,

 ### Example usage:
+- vLLM
+```bash
+vllm serve yujiepan/minimax-m2-tiny-random --trust-remote-code
+```
+- Transformers
 ```python
 import torch
 from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
 model_id = "yujiepan/minimax-m2-tiny-random"
 import json
 from pathlib import Path
 import accelerate
+import torch
 from huggingface_hub import file_exists, hf_hub_download
 from transformers import (
     AutoConfig,