Instructions to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder with Transformers:

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

pipe = pipeline("text-generation", model="TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder")
model = AutoModelForCausalLM.from_pretrained("TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder")
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
Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder

SGLang

How to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder 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 "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder" \
    --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": "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder",
		"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 "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder" \
        --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": "TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Unsloth Studio new

How to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder to start chatting

Load model with FastModel

pip install unsloth
from unsloth import FastModel
model, tokenizer = FastModel.from_pretrained(
    model_name="TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder",
    max_seq_length=2048,
)

Docker Model Runner
How to use TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder with Docker Model Runner:
```
docker model run hf.co/TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Qwen3 4B Thinking x MiniMax M2.1 Code SFT

This model was trained on over 1,300 agentic "vibe coding" examples generated by MiniMax M2.1 with a large majority focused on extracting UI/UX design capabilities across different tech stacks.

For more info on how and what the model was trained on, please view the dataset card

How to run

Personally I use vllm on windows via wsl. Here is my command:

vllm serve TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Code-Distill --reasoning-parser deepseek_r1 --enable-auto-tool-choice --tool-call-parser hermes --max-model-len 65536 --quantization bitsandbytes --override-generation-config '{"temperature": 0.6, "top_p": 0.95, "top_k": 20}'

Demo

Prompt: Make me a landing page for my bakery. we make cakes, cookies, brownies and everything else a normal bakery makes. I want it to look really nice

Needless to say I was impressed what this 4B model was capable of (especially at 4bit quant)

The site is overall very choppy and needs polishing, but please feel free to download the html file (in the repo) and give it a look with all it's wonky animations :)

This qwen3 model was trained 2x faster with Unsloth and Huggingface's TRL library.

Downloads last month: 19

Safetensors

Model size

4B params

Tensor type

BF16

Model tree for TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder

Base model

Qwen/Qwen3-4B-Thinking-2507

Finetuned

unsloth/Qwen3-4B-Thinking-2507

Finetuned

(104)

this model

Quantizations

1 model

TeichAI
/

Qwen3-4B-Thinking-MiniMax-M2.1-Coder

Qwen3 4B Thinking x MiniMax M2.1 Code SFT

How to run

Demo

Model tree for TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder

Dataset used to train TeichAI/Qwen3-4B-Thinking-MiniMax-M2.1-Coder