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Model: amadeusai/Amadeus-Verbo-FI-Qwen2.5-1.5B-PT-BR-Instruct Source: Original Platform
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README.md
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README.md
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---
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language:
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- pt
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metrics:
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- accuracy
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- f1
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- pearsonr
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base_model:
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- Qwen/Qwen2.5-1.5B-Instruct
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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license: apache-2.0
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---
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### Amadeus-Verbo-FI-Qwen2.5-1.5B-PT-BR-Instruct
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#### Introduction
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Amadeus-Verbo-FI-Qwen2.5-1.5B-PT-BR-Instruct is a Brazilian-Portuguese language model (PT-BR-LLM) developed from the base model Qwen2.5-1.5B-Instruct through fine-tuning, for 2 epochs, with 600k instructions dataset.
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Read our article [here](https://arxiv.org/abs/2506.00019).
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## Details
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- **Architecture:** a Transformer-based model with RoPE, SwiGLU, RMSNorm, and Attention QKV bias pre-trained via Causal Language Modeling
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- **Parameters:** 1.54B parameters
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- **Number of Parameters (Non-Embedding):** 1.31B
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- **Number of Layers:** 28
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- **Number of Attention Heads (GQA):** 12 for Q and 2 for KV
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- **Context length:** 32,768 tokens
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- **Number of steps:** 78838
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- **Language:** Brazilian Portuguese
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#### Usage
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You can use Amadeus-Verbo-FI-Qwen2.5-1.5B-PT-BR-Instruct with the latest HuggingFace Transformers library and we advise you to use the latest version of Transformers.
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With transformers<4.37.0, you will encounter the following error:
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KeyError: 'qwen2'
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Below, we have provided a simple example of how to load the model and generate text:
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#### Quickstart
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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.
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Using the pipeline:
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```python
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from transformers import pipeline
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messages = [
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{"role": "user", "content": "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"},
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]
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pipe = pipeline("text-generation", model="amadeusai/AV-FI-Qwen2.5-1.5B-PT-BR-Instruct")
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pipe(messages)
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```
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OR
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```python
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from transformers import AutoModelForCausalLM, AutoTokenizer
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model_name = "amadeusai/AV-FI-Qwen2.5-1.5B-PT-BR-Instruct"
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model = AutoModelForCausalLM.from_pretrained(
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model_name,
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torch_dtype="auto",
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device_map="auto"
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)
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tokenizer = AutoTokenizer.from_pretrained(model_name)
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prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana."
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messages = [
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{"role": "system", "content": "Você é um assistente útil."},
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{"role": "user", "content": prompt}
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]
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text = tokenizer.apply_chat_template(
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messages,
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tokenize=False,
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add_generation_prompt=True
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)
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model_inputs = tokenizer([text], return_tensors="pt").to(model.device)
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generated_ids = model.generate(
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**model_inputs,
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max_new_tokens=512
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)
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generated_ids = [
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output_ids[len(input_ids):] for input_ids, output_ids in zip(model_inputs.input_ids, generated_ids)
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]
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response = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)[0]
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```
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OR
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```python
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from transformers import GenerationConfig, TextGenerationPipeline, AutoTokenizer, AutoModelForCausalLM
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import torch
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# Specify the model and tokenizer
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model_id = "amadeusai/AV-FI-Qwen2.5-1.5B-PT-BR-Instruct"
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tokenizer = AutoTokenizer.from_pretrained(model_id)
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model = AutoModelForCausalLM.from_pretrained(model_id)
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# Specify the generation parameters as you like
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generation_config = GenerationConfig(
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**{
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"do_sample": True,
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"max_new_tokens": 512,
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"renormalize_logits": True,
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"repetition_penalty": 1.2,
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"temperature": 0.1,
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"top_k": 50,
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"top_p": 1.0,
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"use_cache": True,
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}
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)
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device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
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generator = TextGenerationPipeline(model=model, task="text-generation", tokenizer=tokenizer, device=device)
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# Generate text
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prompt = "Faça uma planilha nutricional para uma alimentação fitness e mediterrânea com todos os dias da semana"
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completion = generator(prompt, generation_config=generation_config)
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print(completion[0]['generated_text'])
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```
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#### Citation
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If you find our work helpful, feel free to cite it.
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```
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@misc{cruzcastañeda2025amadeusverbotechnicalreportpowerful,
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title={Amadeus-Verbo Technical Report: The powerful Qwen2.5 family models trained in Portuguese},
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author={William Alberto Cruz-Castañeda and Marcellus Amadeus},
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year={2025},
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eprint={2506.00019},
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archivePrefix={arXiv},
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primaryClass={cs.CL},
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url={https://arxiv.org/abs/2506.00019},
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}
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```
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