Instructions to use MiniMaxAI/SynLogic-Mix-3-32B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MiniMaxAI/SynLogic-Mix-3-32B with Transformers:

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

pipe = pipeline("text-generation", model="MiniMaxAI/SynLogic-Mix-3-32B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("MiniMaxAI/SynLogic-Mix-3-32B")
model = AutoModelForCausalLM.from_pretrained("MiniMaxAI/SynLogic-Mix-3-32B")
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 MiniMaxAI/SynLogic-Mix-3-32B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "MiniMaxAI/SynLogic-Mix-3-32B"
# 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/SynLogic-Mix-3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/MiniMaxAI/SynLogic-Mix-3-32B

SGLang

How to use MiniMaxAI/SynLogic-Mix-3-32B 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/SynLogic-Mix-3-32B" \
    --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/SynLogic-Mix-3-32B",
		"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/SynLogic-Mix-3-32B" \
        --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/SynLogic-Mix-3-32B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use MiniMaxAI/SynLogic-Mix-3-32B with Docker Model Runner:
```
docker model run hf.co/MiniMaxAI/SynLogic-Mix-3-32B
```

nielsr HF Staff commited on Jun 9, 2025

Commit

f0178c7

verified ·

1 Parent(s): ee8bf5d

Add text-generation pipeline tag and usage example

Browse files

This PR adds the missing `pipeline_tag`, ensuring people can find your model at https://huggingface.co/models?pipeline_tag=text-generation. It also adds a usage example.

Files changed (1) hide show

README.md +24 -6

README.md CHANGED Viewed

@@ -1,15 +1,17 @@
 ---
-license: mit
-language:
-- en
-tags:
-- LLM
-library_name: transformers
 base_model:
 - Qwen/Qwen2.5-32B
 datasets:
 - MiniMaxAI/SynLogic
 ---
 # SynLogic Zero-Mix-3: Large-Scale Multi-Domain Reasoning Model
 * 🐙 **GitHub Repo:** [https://github.com/MiniMax-AI/SynLogic](https://github.com/MiniMax-AI/SynLogic)
@@ -20,6 +22,22 @@ datasets:
 **Zero-Mix-3** is an advanced multi-domain reasoning model trained using Zero-RL (reinforcement learning from scratch) on a diverse mixture of logical reasoning, mathematical, and coding data. Built on Qwen2.5-32B-Base, this model demonstrates the power of combining diverse verifiable reasoning tasks in a unified training framework.
 ## Key Features
 * **Multi-Domain Training:** Jointly trained on logical reasoning (SynLogic), mathematics, and coding tasks

 ---
 base_model:
 - Qwen/Qwen2.5-32B
 datasets:
 - MiniMaxAI/SynLogic
+language:
+- en
+library_name: transformers
+license: mit
+tags:
+- LLM
+pipeline_tag: text-generation
 ---
 # SynLogic Zero-Mix-3: Large-Scale Multi-Domain Reasoning Model
 * 🐙 **GitHub Repo:** [https://github.com/MiniMax-AI/SynLogic](https://github.com/MiniMax-AI/SynLogic)
 **Zero-Mix-3** is an advanced multi-domain reasoning model trained using Zero-RL (reinforcement learning from scratch) on a diverse mixture of logical reasoning, mathematical, and coding data. Built on Qwen2.5-32B-Base, this model demonstrates the power of combining diverse verifiable reasoning tasks in a unified training framework.
+## Usage
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+model_name = "MiniMaxAI/SynLogic-Mix-3-32B"
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
+prompt = "What is 2 + 2?"
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+outputs = model.generate(**inputs, max_new_tokens=20)
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+```
 ## Key Features
 * **Multi-Domain Training:** Jointly trained on logical reasoning (SynLogic), mathematics, and coding tasks