slices:
  - sources:
      - model: Qwen/Qwen2.5-32B-Instruct
        layer_range: [0, 64]
      - model: amadeusai/AV-BI-Qwen2.5-32B-PT-BR-Instruct
        layer_range: [0, 64]
merge_method: slerp
base_model: Qwen/Qwen2.5-32B-Instruct
parameters:
  t:
    - filter: self_attn
      value: [0, 0.5, 0.3, 0.7, 1]
    - filter: mlp
      value: [1, 0.5, 0.7, 0.3, 0]
    - value: 0.5
dtype: bfloat16

Usage

You can use Amadeus-Verbo--MI-Qwen2.5-32B-PT-BR-Instruct with the latest HuggingFace Transformers library and we advise you to use the latest version of Transformers.

With transformers<4.37.0, you will encounter the following error:

KeyError: 'qwen2'

Below, we have provided a simple example of how to load the model and generate text:

Quickstart

The following code snippet uses pipeline, AutoTokenizer, AutoModelForCausalLM and apply_chat_template to show how to load the tokenizer, the model, and how to generate content.

Using the pipeline:

python

from transformers import pipeline

messages = [
    {"role": "user", "content": "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"},
]
pipe = pipeline("text-generation", model="amadeusai/AV-MI-Qwen2.5-32B-PT-BR-Instruct")
pipe(messages)

python

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "amadeusai/AV-MI-Qwen2.5-32B-PT-BR-Instruct"

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

prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana."
messages = [
    {"role": "system", "content": "Você é um assistente útil."},
    {"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]

python

from transformers import GenerationConfig, TextGenerationPipeline, AutoTokenizer, AutoModelForCausalLM
import torch

# Specify the model and tokenizer
model_id = "amadeusai/AV-MI-Qwen2.5-32B-PT-BR-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id)

# Specify the generation parameters as you like
generation_config = GenerationConfig(
    **{
    "do_sample": True,
    "max_new_tokens": 512,
    "renormalize_logits": True,
    "repetition_penalty": 1.2,
    "temperature": 0.1,
    "top_k": 50,
    "top_p": 1.0,
    "use_cache": True, 
  }
)

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
generator = TextGenerationPipeline(model=model, task="text-generation", tokenizer=tokenizer, device=device)

# Generate text
prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"
completion = generator(prompt, generation_config=generation_config)
print(completion[0]['generated_text'])

Citation

If you find our work helpful, feel free to cite it.

@misc{cruzcastañeda2025amadeusverbotechnicalreportpowerful,
      title={Amadeus-Verbo Technical Report: The powerful Qwen2.5 family models trained in Portuguese}, 
      author={William Alberto Cruz-Castañeda and Marcellus Amadeus},
      year={2025},
      eprint={2506.00019},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2506.00019}, 
}

amadeusai/Amadeus-Verbo-MI-Qwen-2.5-32B-PT-BR-Instruct-Experimental

merge_32B

Merge Details

Merge Method

Models Merged

Configuration

Usage

Quickstart

Citation

amadeusai/
Amadeus-Verbo-MI-Qwen-2.5-32B-PT-BR-Instruct-Experimental