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)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B")
model = AutoModelForCausalLM.from_pretrained("HuggingFaceTB/SmolLM3-3B")
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 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
```

XciD HF Staff commited on Jul 9, 2025

Commit

486a602

verified ·

1 Parent(s): b49c771

fix: reassign xml_tools to tools to make it work with vllm (#9)

Browse files

- fix: reassign xml_tools to tools to make it work with vllm (0d650ef7967b8d623bb87fd12d056cbba02e154a)
- Update chat_template.jinja (6407be8f606a717ab9919d422d641dab78ff7cd7)
- Update README.md (410c59a5babcd960e3ecfc763755551eb5b6b690)

Files changed (2) hide show

README.md +2 -2
chat_template.jinja +5 -2

README.md CHANGED Viewed

@@ -211,12 +211,12 @@ python -m sglang.launch_server --model-path HuggingFaceTB/SmolLM3-3B
 #### vLLM
 ```bash
-vllm serve HuggingFaceTB/SmolLM3-3B
 ```
 #### Setting `chat_template_kwargs`
-You can specify `chat_template_kwargs` such as `enable_thinking` and `xml_tools` to a deployed model by passing the `chat_template_kwargs` parameter in the API request.
 ```bash
 curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{

 #### vLLM
 ```bash
+vllm serve HuggingFaceTB/SmolLM3-3B --enable-auto-tool-choice --tool-call-parser=hermes
 ```
 #### Setting `chat_template_kwargs`
+You can specify `chat_template_kwargs` such as `enable_thinking` to a deployed model by passing the `chat_template_kwargs` parameter in the API request.
 ```bash
 curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{

chat_template.jinja CHANGED Viewed

@@ -42,9 +42,12 @@
     {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
   {%- endif -%}
-  {%- if xml_tools or python_tools -%}
     {{- "### Tools\n\n" -}}
-    {%- if xml_tools -%}
       {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
       {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
         {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}

     {{- "You are a helpful AI assistant named SmolLM, trained by Hugging Face.\n\n" -}}
   {%- endif -%}
+  {%- if xml_tools or python_tools or tools -%}
     {{- "### Tools\n\n" -}}
+    {%- if xml_tools or tools -%}
+      {%- if tools -%}
+        {%- set xml_tools = tools -%}
+      {%- endif -%}
       {%- set ns = namespace(xml_tool_string="You may call one or more functions to assist with the user query.\nYou are provided with function signatures within <tools></tools> XML tags:\n\n<tools>\n") -%}
       {%- for tool in xml_tools[:] -%} {# The slicing makes sure that xml_tools is a list #}
         {%- set ns.xml_tool_string = ns.xml_tool_string ~ (tool | string) ~ "\n" -%}