This model was released on 2025-07-08 and added to Hugging Face Transformers on 2025-06-25.

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SmolLM3

SmolLM3 is a fully open, compact language model designed for efficient deployment while maintaining strong performance. It uses a Transformer decoder architecture with Grouped Query Attention (GQA) to reduce the kv cache, and no RoPE, enabling improved performance on long-context tasks. It is trained using a multi-stage training approach on high-quality public datasets across web, code, and math domains. The model is multilingual and supports very large context lengths. The instruct variant is optimized for reasoning and tool use.

[!TIP] Click on the SmolLM3 models in the right sidebar for more examples of how to apply SmolLM3 to different language tasks.

The example below demonstrates how to generate text with [Pipeline], [AutoModel], and from the command line using the instruction-tuned models.

python

from transformers import pipeline


pipe = pipeline(
    task="text-generation",
    model="HuggingFaceTB/SmolLM3-3B",
    device_map=0
)

messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": "Tell me about yourself."},
]
outputs = pipe(messages, max_new_tokens=256, do_sample=True, temperature=0.7, top_k=50, top_p=0.95)
print(outputs[0]["generated_text"][-1]['content'])

</hfoption> <hfoption id="AutoModel">

python

from transformers import AutoModelForCausalLM, AutoTokenizer


model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceTB/SmolLM3-3B",
    device_map="auto",
    attn_implementation="sdpa"
)
tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B")

prompt = "Give me a short introduction to large language models."
messages = [
    {"role": "system", "content": "You are a helpful assistant."},
    {"role": "user", "content": prompt}
]
text = tokenizer.apply_chat_template(
    messages,
    tokenize=False,
    add_generation_prompt=True
)
model_inputs = tokenizer([text], return_tensors="pt").to(model.device)

generated_ids = model.generate(
    model_inputs.input_ids,
    cache_implementation="static",
    max_new_tokens=512,
    do_sample=True,
    temperature=0.7,
    top_k=50,
    top_p=0.95
)
generated_ids = [
    output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
]

response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
print(response)

</hfoption> <hfoption id="transformers CLI">

bash

# pip install -U flash-attn --no-build-isolation
transformers chat HuggingFaceTB/SmolLM3-3B --dtype auto --attn_implementation flash_attention_2 --device 0

</hfoption> </hfoptions>

Quantization reduces the memory burden of large models by representing the weights in a lower precision. Refer to the Quantization overview for more available quantization backends.

The example below uses bitsandbytes to quantize the weights to 4-bits.

python

# pip install -U flash-attn --no-build-isolation
from transformers import AutoModelForCausalLM, AutoTokenizer, BitsAndBytesConfig


quantization_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_use_double_quant=True,
)

tokenizer = AutoTokenizer.from_pretrained("HuggingFaceTB/SmolLM3-3B")
model = AutoModelForCausalLM.from_pretrained(
    "HuggingFaceTB/SmolLM3-3B",
    device_map="auto",
    quantization_config=quantization_config,
    attn_implementation="flash_attention_2"
)

inputs = tokenizer("Gravity is the force", return_tensors="pt").to(model.device)
outputs = model.generate(**inputs, max_new_tokens=100)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Notes

Ensure your Transformers library version is up-to-date. SmolLM3 requires Transformers>=4.53.0 for full support.

SmolLM3Config

[[autodoc]] SmolLM3Config

SmolLM3Model

[[autodoc]] SmolLM3Model - forward

SmolLM3ForCausalLM

[[autodoc]] SmolLM3ForCausalLM - forward

SmolLM3ForSequenceClassification

[[autodoc]] SmolLM3ForSequenceClassification - forward

SmolLM3ForTokenClassification

[[autodoc]] SmolLM3ForTokenClassification - forward

SmolLM3ForQuestionAnswering

[[autodoc]] SmolLM3ForQuestionAnswering - forward