license: mit language:

• pt base_model:
• Qwen/Qwen2.5-0.5B-Instruct pipeline_tag: text-generation datasets:
• adalbertojunior/openHermes_portuguese
• cnmoro/smoltalk-555k-ptbr
• cnmoro/RagMixPTBR-Legal-Alpaca-2M model-index:
• name: Qwen2.5-0.5B-Portuguese-v1 results:
  • task: type: text-generation name: Text Generation dataset: name: ENEM Challenge (No Images) type: eduagarcia/enem_challenge split: train args: num_few_shot: 3 metrics:
    • type: acc value: 37.86 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: BLUEX (No Images) type: eduagarcia-temp/BLUEX_without_images split: train args: num_few_shot: 3 metrics:
    • type: acc value: 34.63 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: OAB Exams type: eduagarcia/oab_exams split: train args: num_few_shot: 3 metrics:
    • type: acc value: 33.12 name: accuracy source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Assin2 RTE type: assin2 split: test args: num_few_shot: 15 metrics:
    • type: f1_macro value: 86.3 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: Assin2 STS type: eduagarcia/portuguese_benchmark split: test args: num_few_shot: 15 metrics:
    • type: pearson value: 54.3 name: pearson source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: FaQuAD NLI type: ruanchaves/faquad-nli split: test args: num_few_shot: 15 metrics:
    • type: f1_macro value: 65.33 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: HateBR Binary type: ruanchaves/hatebr split: test args: num_few_shot: 25 metrics:
    • type: f1_macro value: 44.06 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: PT Hate Speech Binary type: hate_speech_portuguese split: test args: num_few_shot: 25 metrics:
    • type: f1_macro value: 55.1 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard
  • task: type: text-generation name: Text Generation dataset: name: tweetSentBR type: eduagarcia/tweetsentbr_fewshot split: test args: num_few_shot: 25 metrics:
    • type: f1_macro value: 45.96 name: f1-macro source: url: https://huggingface.co/spaces/eduagarcia/open_pt_llm_leaderboard?query=cnmoro/Qwen2.5-0.5B-Portuguese-v1 name: Open Portuguese LLM Leaderboard

Qwen2.5-0.5B finetuned for proficiency in Portuguese language and increased intelligence.

https://ollama.com/cnmoro/Qwen2.5-0.5B-Portuguese-v1

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "cnmoro/Qwen2.5-0.5B-Portuguese-v1"

model = AutoModelForCausalLM.from_pretrained(
    model_name,
    torch_dtype="auto",
    device_map="auto"
)
tokenizer = AutoTokenizer.from_pretrained(model_name)

prompt = "Escreva uma breve introdução sobre LLMs (Large Language Models) e suas aplicações."

# System prompt is always injected and hardcoded automatically
# for ideal performance in portuguese language.
# No need to write it again.
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)

generated_ids = model.generate(
    **model_inputs,
    max_new_tokens=512
)
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]
response
# LLM significa Large Language Models, que são modelos de linguagem computacional
# projetados para simular a inteligência humana no processamento e geração de texto.
# Esses modelos usam técnicas avançadas de aprendizado de máquina e redes neurais para
# compreender e gerar texto com base em dados de entrada. As aplicações de LLM incluem
# tradução automática, análise de sentimento, modelagem de tópicos e resposta a perguntas
# automatizadas. Eles estão sendo cada vez mais utilizados em diversas áreas, como
# saúde, educação e finanças, para melhorar a comunicação, as experiências dos clientes
# e os resultados da pesquisa.

Overall Results

Detailed Results

assin2_rte

assin2_sts

bluex

enem_challenge

faquad_nli

hatebr_offensive

oab_exams

portuguese_hate_speech

tweetsentbr

Model Meta Information

• Truncated Samples: 3863
• Non-Truncated Samples: 10287
• Padded Samples: 0
• Non-Padded Samples: 14150
• Fewshots Truncated: 3863
• Has Chat Template: True
• Chat Type: system_user_assistant
• Number of GPUs: 1
• Accelerate Number of Processes: N/A
• Model SHA: None
• Model Data Type: torch.bfloat16
• Model Memory Footprint: 988065664 bytes
• Model Number of Parameters: 494032768
• Model is Loaded in 4bit: N/A
• Model is Loaded in 8bit: N/A
• Model is Quantized: N/A
• Model Device: cuda:0
• Batch Size: 1
• Max Length: 512
• Max Context Length 480
• Max Generation Tokens: 32
• Effective Batch Size: 1.0

Open Portuguese LLM Leaderboard Evaluation Results

Detailed results can be found here and on the 🚀 Open Portuguese LLM Leaderboard