SmolLM3 is a 3B parameter language model designed to push the boundaries of small models. It supports 6 languages, advanced reasoning and long context. SmolLM3 is a fully open model that offers strong performance at the 3B–4B scale.
The model is a decoder-only transformer using GQA and NoPE (with 3:1 ratio), it was pretrained on 11.2T tokens with a staged curriculum of web, code, math and reasoning data. Post-training included midtraining on 140B reasoning tokens followed by supervised fine-tuning and alignment via Anchored Preference Optimization (APO).
The modeling code for SmolLM3 is available in transformers v4.53.0, so make sure to upgrade your transformers version. You can also load the model with the latest vllm which uses transformers as a backend.
pip install -U transformers
from transformers import AutoModelForCausalLM, AutoTokenizer
model_name = "HuggingFaceTB/SmolLM3-3B"
device = "cuda" # for GPU usage or "cpu" for CPU usage
# load the tokenizer and the model
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
model_name,
).to(device)
# prepare the model input
prompt = "Give me a brief explanation of gravity in simple terms."
messages_think = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages_think,
tokenize=False,
add_generation_prompt=True,
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
# Generate the output
generated_ids = model.generate(**model_inputs, max_new_tokens=32768)
# Get and decode the output
output_ids = generated_ids[0][len(model_inputs.input_ids[0]) :]
print(tokenizer.decode(output_ids, skip_special_tokens=True))
Tip. We recommend setting temperature=0.6 and top_p=0.95 in the sampling parameters.
We enable extended thinking by default, so the example above generates the output with a reasoning trace. For choosing between enabling, you can provide the /think and /no_think flags through the system prompt as shown in the snippet below for extended thinking disabled. The code for generating the response with extended thinking would be the same except that the system prompt should have /think instead of /no_think.
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "system", "content": "/no_think"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
We also provide the option of specifying the whether to use extended thinking through the enable_thinking kwarg as in the example below. You do not need to set the /no_think or /think flags through the system prompt if using the kwarg, but keep in mind that the flag in the system prompt overwrites the setting in the kwarg.
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
enable_thinking=False
)
SmolLM3 supports tool calling! Just pass your list of tools:
Under the argument xml_tools for standard tool-calling: these tools will be called as JSON blobs within XML tags, like <tool_call>{"name": "get_weather", "arguments": {"city": "Copenhagen"}}</tool_call>
Or under python_tools: then the model will call tools like python functions in a <code> snippet, like <code>get_weather(city="Copenhagen")</code>
from transformers import AutoModelForCausalLM, AutoTokenizer
checkpoint = "HuggingFaceTB/SmolLM3-3B"
tokenizer = AutoTokenizer.from_pretrained(checkpoint)
model = AutoModelForCausalLM.from_pretrained(checkpoint)
tools = [
{
"name": "get_weather",
"description": "Get the weather in a city",
"parameters": {"type": "object", "properties": {"city": {"type": "string", "description": "The city to get the weather for"}}}}
]
messages = [
{
"role": "user",
"content": "Hello! How is the weather today in Copenhagen?"
}
]
inputs = tokenizer.apply_chat_template(
messages,
enable_thinking=False, # True works as well, your choice!
xml_tools=tools,
add_generation_prompt=True,
tokenize=True,
return_tensors="pt"
)
outputs = model.generate(inputs)
print(tokenizer.decode(outputs[0]))
You can specify custom instruction through the system prompt while controlling whether to use extended thinking. For example, the snippet below shows how to make the model speak like a pirate while enabling extended thinking.
prompt = "Give me a brief explanation of gravity in simple terms."
messages = [
{"role": "system", "content": "Speak like a pirate./think"},
{"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
messages,
tokenize=False,
add_generation_prompt=True,
)
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.
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
"model": "HuggingFaceTB/SmolLM3-3B",
"messages": [
{"role": "user", "content": "Give me a brief explanation of gravity in simple terms."}
],
"temperature": 0.6,
"top_p": 0.95,
"max_tokens": 16384,
"chat_template_kwargs": {"enable_thinking": false}
}'
In this section, we report the evaluation results of SmolLM3 model. All evaluations are zero-shot unless stated otherwise, and we use lighteval to run them.
We highlight the best score in bold and underline the second-best score.
Note: All evaluations are zero-shot unless stated otherwise. For Ruler 64k evaluation, we apply YaRN to the Qwen models with 32k context to extrapolate the context length.
The model has also been trained on Arabic (standard), Chinese and Russian data, but has seen fewer tokens in these languages compared to the 6 above. We report the performance on these langages for information.
SmolLM3 can produce text on a variety of topics, but the generated content may not always be factually accurate, logically consistent, or free from biases present in the training data. These models should be used as assistive tools rather than definitive sources of information. Users should always verify important information and critically evaluate any generated content.
unsloth <huggingface.co/unsloth>Newsgroups: alt.freeweights.smollmSubject: 
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