Instructions to use Can111/m1-32b with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Can111/m1-32b with Transformers:

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

pipe = pipeline("text-generation", model="Can111/m1-32b")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Can111/m1-32b")
model = AutoModelForCausalLM.from_pretrained("Can111/m1-32b")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Can111/m1-32b with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Can111/m1-32b"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Can111/m1-32b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Can111/m1-32b

SGLang

How to use Can111/m1-32b 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 "Can111/m1-32b" \
    --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": "Can111/m1-32b",
		"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 "Can111/m1-32b" \
        --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": "Can111/m1-32b",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use Can111/m1-32b with Docker Model Runner:
```
docker model run hf.co/Can111/m1-32b
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning

M1-32B is a 32B-parameter large language model fine-tuned from Qwen2.5-32B-Instruct on the M500 dataset—an interdisciplinary multi-agent collaborative reasoning dataset. M1-32B is optimized for improved reasoning, discussion, and decision-making in multi-agent systems (MAS), including frameworks such as AgentVerse.

Code: https://github.com/jincan333/MAS-TTS Project page: https://github.com/jincan333/MAS-TTS

How to Use with 🤗 Transformers

You can use this model directly with the transformers library for text generation.

from transformers import AutoModelForCausalLM, AutoTokenizer
import torch

model_id = "Can111/m1-32b"

# Load tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    torch_dtype=torch.bfloat16, # Use bfloat16 for optimal performance if supported
    device_map="auto" # Automatically distribute model across available devices
)
model.eval() # Set model to evaluation mode

# Define your conversation messages
messages = [
    {"role": "user", "content": "Explain multi-agent collaborative reasoning and its benefits."},
]

# Apply chat template and tokenize inputs
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)

model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

# Generate response
generated_ids = model.generate(
    model_inputs.input_ids,
    max_new_tokens=256,
    do_sample=True,
    temperature=0.7,
    top_p=0.9
)

# Decode and print the generated text
decoded_output = tokenizer.batch_decode(generated_ids[:, model_inputs.input_ids.shape[1]:], skip_special_tokens=True)[0]
print(decoded_output)

🚀 Key Features

🧠 Enhanced Collaborative Reasoning
Trained on real multi-agent traces involving diverse roles like Expert Recruiter, Problem Solvers, and Evaluator.
🗣️ Role-Aware Dialogue Generation
Learns to reason and respond from different expert perspectives based on structured prompts.
⚙️ Optimized for Multi-Agent Systems
Performs well as a MAS agent with adaptive collaboration and token budgeting.

🏗️ Model Training

Base Model: Qwen2.5-32B-Instruct
Dataset: M500 (500 curated multi-agent reasoning traces)
Objective: Supervised Fine-Tuning (SFT) on role-conditioned prompts
Training Setup:
- 8 × A100 GPUs
- 5 epochs
- Learning rate: 1e-5
- Frameworks: DeepSpeed, FlashAttention, LLaMA-Factory

📊 Performance

Model	General Understanding		Mathematical Reasoning		Coding
	GPQA	Commongen	AIME2024	MATH-500	HumanEval	MBPP-S
Non-Reasoning Models
Qwen2.5	50.2	96.7	21.1	84.4	89.0	80.2
DeepSeek-V3	58.6	98.6	33.3	88.6	89.6	83.9
GPT-4o	49.2	97.8	7.8	81.3	90.9	85.4
Reasoning Models
s1.1-32B	58.3	94.1	53.3	90.6	82.3	77.4
DeepSeek-R1	75.5	97.2	78.9	96.2	98.2	91.7
o3-mini	71.3	99.1	84.4	95.3	97.0	93.6
M1-32B (Ours)	61.1	96.9	60.0	95.1	92.8	89.1
M1-32B w. CEO (Ours)	62.1	97.4	62.2	95.8	93.9	90.5

Table Caption:
Performance comparison on general understanding, mathematical reasoning, and coding tasks using strong reasoning and non-reasoning models within the AgentVerse framework. Our method achieves substantial improvements over Qwen2.5 and s1.1-32B on all tasks, and attains performance comparable to o3-mini and DeepSeek-R1 on MATH-500 and MBPP-S, demonstrating its effectiveness in enhancing collaborative reasoning in MAS. Note that the results of s1.1-32B are obtained without using budget forcing.

💬 Intended Use

M1-32B is intended for research on Multi-agent reasoning and collaboration in MAS

Citation

If you use this model, please cite the relevant papers:

@article{jin2025two,
  title={Two Heads are Better Than One: Test-time Scaling of Multi-agent Collaborative Reasoning},
  author={Jin, Can and Peng, Hongwu and Zhang, Qixin and Tang, Yujin and Metaxas, Dimitris N and Che, Tong},
  journal={arXiv preprint arXiv:2504.09772},
  year={2025}
}