Instructions to use HuggingFaceTB/SmolLM3-3B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceTB/SmolLM3-3B with Transformers:

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

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

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use HuggingFaceTB/SmolLM3-3B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/HuggingFaceTB/SmolLM3-3B

SGLang

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

Docker Model Runner
How to use HuggingFaceTB/SmolLM3-3B with Docker Model Runner:
```
docker model run hf.co/HuggingFaceTB/SmolLM3-3B
```

eliebak HF Staff commited on Jul 8, 2025

Commit

9d66358

verified ·

1 Parent(s): 2593281

Update README.md

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@@ -218,9 +218,9 @@ In this section, we report the evaluation results of SmolLM3 model. All evaluati
 We highlight the best score in bold and underline the second-best score.
-## Instruction Model
-### No Extended Thinking
 Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold.
 | Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B |
 |---------|--------|------------|------------|-------------|------------|----------|
@@ -235,7 +235,7 @@ Evaluation results of non reasoning models and reasoning models in no thinking m
 (*): this is a tool calling finetune
-### Extended Thinking
 Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
 | Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B |
 |---------|--------|------------|------------|----------|
@@ -249,10 +249,10 @@ Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
 | Multilingual Q&A | Global MMLU | <u>64.1</u> | 62.3 | **73.3** |
-## Base Pre-Trained Model
 For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length.
-### English benchmarks
 Note: All evaluations are zero-shot unless stated otherwise.
 | Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base |
@@ -276,7 +276,7 @@ Note: All evaluations are zero-shot unless stated otherwise.
 | | Ruler 32k context | 76.35 | 75.93 | <u>77.58</u> | 70.63 | **83.98** |
 | | Ruler 64k context | 67.85 | 64.90 | **72.93** | 57.18 | 60.29 |
-### Multilingual benchmarks
 | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base |

 We highlight the best score in bold and underline the second-best score.
+### Instruction Model
+#### No Extended Thinking
 Evaluation results of non reasoning models and reasoning models in no thinking mode. We highlight the best and second-best scores in bold.
 | Category | Metric | SmoLLM3-3B | Qwen2.5-3B | Llama3.1-3B | Qwen3-1.7B | Qwen3-4B |
 |---------|--------|------------|------------|-------------|------------|----------|
 (*): this is a tool calling finetune
+#### Extended Thinking
 Evaluation results in reasoning mode for SmolLM3 and Qwen3 models:
 | Category | Metric | SmoLLM3-3B | Qwen3-1.7B | Qwen3-4B |
 |---------|--------|------------|------------|----------|
 | Multilingual Q&A | Global MMLU | <u>64.1</u> | 62.3 | **73.3** |
+### Base Pre-Trained Model
 For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length.
+#### English benchmarks
 Note: All evaluations are zero-shot unless stated otherwise.
 | Category | Metric | SmolLM3-3B | Qwen2.5-3B | Llama3-3.2B | Qwen3-1.7B-Base | Qwen3-4B-Base |
 | | Ruler 32k context | 76.35 | 75.93 | <u>77.58</u> | 70.63 | **83.98** |
 | | Ruler 64k context | 67.85 | 64.90 | **72.93** | 57.18 | 60.29 |
+#### Multilingual benchmarks
 | Category | Metric | SmolLM3 3B Base | Qwen2.5-3B | Llama3.2 3B | Qwen3 1.7B Base | Qwen3 4B Base |