This model is a 7.45% smaller version of HuggingFaceTB/SmolLM3-3B optimized for Portuguese language via vocabulary size reduction using the trimming method.
This trimmed model should perform similarly to the original model with only 16,384 tokens and a much smaller memory footprint. However, it may not perform well for other languages as tokens not commonly used in the selected languages were removed from the vocabulary.

Model Statistics

Metric	Original	Trimmed	Reduction
Vocabulary size	128,256 tokens	16,384 tokens	87.23%
Model size	3,075,098,624 params	2,845,984,768 params	7.45%

Mining Dataset Statistics

Number of texts used for mining: 200,000 texts
Dataset: lbourdois/fineweb-2-trimming

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "alphaedge-ai/SmolLM3-3B-por-16384"
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 = "Your prompt in Portuguese."
messages = [
    {"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)

# 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))

To enable/disable thinking mode, use the /think or /no_think flag in the system prompt:

python

messages = [
    {"role": "system", "content": "/no_think"},
    {"role": "user", "content": prompt}
]

Citations

SmolLM3-3B

@misc{bakouch2025smollm3,
      title={SmolLM3: smol, multilingual, long-context reasoner},
      author={akouch, Elie and Ben Allal, Loubna and Lozhkov, Anton and Tazi, Nouamane
               and Tunstall, Lewis and Patiño, Carlos Miguel and Beeching, Edward
               and Roucher, Aymeric and others},
      year={2025},
      howpublished={https://huggingface.co/blog/smollm3}
}

Trimming blog post

@misc{hf_blogpost_trimming,
      title={Introduction to Trimming}, 
      author={Loïck BOURDOIS and Tom AARSEN and Bram VANROY and Christopher AKIKI and Woojun JUNG and Manuel ROMERO and Prithiv SAKTHI},
      year={2026},
      url={https://huggingface.co/blog/lbourdois/introduction-to-trimming}, 
}