Instructions to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT with Transformers:

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

pipe = pipeline("text-generation", model="BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT")
model = AutoModelForCausalLM.from_pretrained("BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT", device_map="auto")
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 Settings

vLLM

How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT

SGLang

How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT 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 "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT" \
    --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": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT",
		"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 "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT" \
        --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": "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT with Docker Model Runner:
```
docker model run hf.co/BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT
```

Open-MOPD-SmolLM3-3B-MixSFT

This model is the mixed-domain supervised fine-tuning initialization used by the Open-MOPD pipeline. All three domain-specific RL teachers and the final multi-teacher distilled student start from this checkpoint.

The model is derived from HuggingFaceTB/SmolLM3-3B-Base through four epochs of supervised fine-tuning on math, code, and instruction-following data. The training setup uses global batch size 128, learning rate 4e-5, cosine decay with 3% warmup, a maximum sequence length of 32,768, and 30,116 optimization steps.

Domains are balanced by response-token count rather than example count. This prevents the 820K shorter instruction-following responses from overwhelming the smaller but longer math and code corpora. After balancing, math, code, and instruction following contribute approximately 37.3%, 28.1%, and 34.6% of training response tokens.

Results

AIME24	AIME25	Math	LCBv5	LCBv6	Code	IFEval	IFBench_test	IF	Overall
15.63	20.26	17.95	15.99	19.20	17.60	66.91	16.00	41.46	25.67

Evaluation protocol

Math: AIME24 and AIME25, avg@64, temperature 0.6.
Code: LiveCodeBench v5 and v6, avg@10, temperature 1.0.
Instruction following: IFEval and IFBench_test, n=1, temperature 1.0, with enable_thinking=true.

All evaluations use max_model_len=32768, top_p=0.95, top_k=-1, and stop_token_ids=[128012]. Scores are averaged per dataset, then per domain, followed by a macro-average across domains.

Usage

from transformers import AutoModelForCausalLM, AutoTokenizer

model_id = "BytedTsinghua-SIA/Open-MOPD-SmolLM3-3B-MixSFT"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    dtype="bfloat16",
    device_map="auto",
)

messages = [{"role": "user", "content": "Write a Python function that merges two sorted lists."}]
inputs = tokenizer.apply_chat_template(
    messages,
    return_tensors="pt",
    add_generation_prompt=True,
    enable_thinking=True,
).to(model.device)
outputs = model.generate(inputs, max_new_tokens=4096, temperature=0.6, top_p=0.95, do_sample=True)
print(tokenizer.decode(outputs[0][inputs.shape[-1]:], skip_special_tokens=True))

Role in the pipeline

SmolLM3-3B-Base -> MixSFT initialization -> Math/Code/IF RL teachers -> Open-MOPD Final.

Model specifications

Architecture: SmolLM3ForCausalLM
Parameters: approximately 3B
Layers: 36
Vocabulary size: 128,256
Weights: BF16, approximately 6.2 GB
Includes tokenizer and chat template

The published tokenizer metadata is compatible with Transformers 4.x and produces the same tokenization and chat-template sequences as the original training artifact.