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---
language:
- pt
license: apache-2.0
library_name: transformers
tags:
- text-generation-inference
datasets:
- Polygl0t/gigaverbo-v2-sft
- Polygl0t/gigaverbo-v2-preferences
metrics:
- perplexity
pipeline_tag: text-generation
widget:
- text: "<|im_start|>user\nQual é a capital de Portugal?<|im_end|><|im_start|>assistant\n"
example_title: Exemplo
- text: "<|im_start|>user\nEscreva um poema sobre a floresta amazônica.<|im_end|><|im_start|>assistant\n"
example_title: Exemplo
- text: "<|im_start|>user\nListe três benefícios da energia solar.<|im_end|><|im_start|>assistant\n"
example_title: Exemplo
inference:
parameters:
repetition_penalty: 1.2
temperature: 0.1
top_k: 50
top_p: 1.0
max_new_tokens: 150
co2_eq_emissions:
emissions: 65000
source: CodeCarbon
training_type: post-training
geographical_location: Germany
hardware_used: NVIDIA A100-SXM4-80GB
model-index:
- name: Tucano2-qwen-1.5B-Instruct
results:
- task:
type: text-generation
name: Text Generation
dataset:
name: ARC Challenge
type: Polygl0t/ARC-poly
split: test
args:
num_few_shot: 5
metrics:
- type: acc_norm
value: 38.63
name: Acc-norm
source:
url: https://github.com/Polygl0t/lm-evaluation-harness/tree/polyglot_harness_portuguese
name: arc_challenge_poly_pt
- task:
type: text-generation
name: Text Generation
dataset:
name: MMLU
type: Polygl0t/MMLU-poly
split: test
args:
num_few_shot: 5
metrics:
- type: acc
value: 41.46
name: Acc
source:
url: https://github.com/Polygl0t/lm-evaluation-harness/tree/polyglot_harness_portuguese
name: mmlu_poly_pt
- task:
type: text-generation
name: Text Generation
dataset:
name: BELEBELE
type: facebook/belebele
split: test
args:
num_few_shot: 5
metrics:
- type: acc_norm
value: 62.33
name: Acc-norm
source:
url: https://github.com/Polygl0t/lm-evaluation-harness/tree/polyglot_harness_portuguese
name: belebele_por_Latn
- task:
type: text-generation
name: Text Generation
dataset:
name: BLUEX
type: eduagarcia-temp/BLUEX_without_images
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 40.33
name: Acc
source:
url: https://github.com/eduagarcia/lm-evaluation-harness-pt
name: bluex
- task:
type: text-generation
name: Text Generation
dataset:
name: ENEM Challenge
type: eduagarcia/enem_challenge
split: train
args:
num_few_shot: 3
metrics:
- type: acc
value: 53.6
name: Acc
source:
url: https://github.com/eduagarcia/lm-evaluation-harness-pt
name: enem_challenge
- 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: 40.73
name: Acc
source:
url: https://github.com/eduagarcia/lm-evaluation-harness-pt
name: oab_exams
- task:
type: text-generation
name: Text Generation
dataset:
name: IFEval
type: Polygl0t/IFEval-PT
split: train
args:
num_few_shot: 0
metrics:
- type: ifeval_pt_inst_level_loose_acc
value: 41.86
name: Acc-loose
- type: ifeval_pt_prompt_level_loose_acc
value: 30.0
name: Acc-loose
source:
url: https://github.com/Polygl0t/lm-evaluation-harness/tree/polyglot_harness_portuguese
name: ifeval_pt
- task:
type: text-generation
name: Text Generation
dataset:
name: GSM8K
type: Polygl0t/gsm8k-pt
split: test
args:
num_few_shot: 0
metrics:
- type: flexible-extract
value: 18.49
name: Acc-flex
source:
url: https://github.com/Polygl0t/lm-evaluation-harness/tree/polyglot_harness_portuguese
name: gsm8k_pt
- task:
type: text-generation
name: Text Generation
dataset:
name: HumanEval
type: openai/openai_humaneval
split: test
args:
num_few_shot: 0
metrics:
- type: pass@1
value: 26.21
name: pass@1
source:
url: https://github.com/Polygl0t/lm-evaluation-harness
base_model: Polygl0t/Tucano2-qwen-1.5B-Base
---
# Tucano2-qwen-1.5B-Instruct
<img src="./logo.png" alt="An illustration of a Tucano bird showing vibrant colors like yellow, orange, blue, green, and black." height="200">
## Model Summary
**[Tucano2-qwen-1.5B-Instruct](https://huggingface.co/Polygl0t/Tucano2-qwen-1.5B-Instruct)** is an instruction-tuned Portuguese language model on top of **Tucano2-qwen-0.5B-Base**. It has been trained using a combination of one round of supervised fine-tuning (SFT) and one round of Anchored Preference Optimization (APO).
Despite its compact size, the model delivers strong performance across a wide range of Portuguese benchmarks and supports tasks such as **retrieval-augmented generation**, **function calling and tool use**, **summarization**, and **structured output generation**, among others.
**All datasets, source code, and training recipes used to develop the Tucano2 series are fully open and reproducible.**
## Details
- **Architecture:** a Transformer-based model ([`qwen3`](https://huggingface.co/docs/transformers/main/en/model_doc/qwen3))
- **Size:** 1,510,073,344 parameters
- **Context length:** 4,096 tokens
- **Dataset(s):**
- [Polygl0t/gigaverbo-v2-sft](https://huggingface.co/datasets/Polygl0t/gigaverbo-v2-sft)
- [Polygl0t/gigaverbo-v2-preferences](https://huggingface.co/datasets/Polygl0t/gigaverbo-v2-preferences)
- **Training time**: ~ 82 hours
- **Emissions:** 65 KgCO2 (Germany)
- **Total energy consumption:** 170 kWh
This repository has the [source code](https://github.com/Polygl0t/llm-foundry) used to train this model. The full configuration used for training is available in the following config files:
- Single stage Supervised Fine-Tuning (linear warmup with cosine decay): [training_config_sft.yaml](training_config_sft.yaml)
- Single stage Anchored Preference Optimization (linear warmup with cosine decay): [training_config_apo.yaml](training_config_apo.yaml)
- Training Logs (loss, lr, rewards, etc.): [train_logs_apo.parquet](train_logs_apo.parquet), [train_logs_sft.parquet](train_logs_sft.parquet)
<details>
<summary><b>SFT Loss Curve</b></summary>
![SFT Loss Curve](./.plots/sft_loss.png)
</details>
<details>
<summary><b>APO Rewards</b></summary>
![APO Rewards](./.plots/apo_reward.png)
</details>
## Intended Uses
The primary intended use Tucano2-qwen-1.5B-Instruct is to serve as foundations for research and development involving Portuguese language modeling. You may also fine-tune and adapt Tucano2-qwen-1.5B-Instruct for deployment if your use follows the Apache 2.0 license. If you decide to use Tucano2-qwen-1.5B-Instruct as a basis for your fine-tuned model, please conduct your own risk and bias assessment.
## Basic usage
```python
from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
import torch
# Load model and tokenizer
model_id = "Polygl0t/Tucano2-qwen-1.5B-Instruct"
tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="auto"
)
# Configure generation parameters
generation_config = GenerationConfig(
do_sample=True,
temperature=0.1,
top_k=50,
top_p=1.0,
repetition_penalty=1.2,
max_new_tokens=150,
pad_token_id=tokenizer.eos_token_id,
)
# Prepare chat messages
messages = [
{"role": "user", "content": "Qual é a capital de Portugal?"}
]
# Apply chat template and generate
prompt = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
with torch.no_grad():
outputs = model.generate(**inputs, generation_config=generation_config)
# Decode and print response
response = tokenizer.decode(outputs[0][len(inputs.input_ids[0]):], skip_special_tokens=True)
print(f"🤖 {response}")
```
## Limitations
Like almost all other language models trained on large text datasets scraped from the web, the Tucano2-qwen-1.5B-Instruct shows behavior that does not make it an out-of-the-box solution to many real-world applications, especially those requiring factual, reliable, and nontoxic text generation. Tucano2-qwen-1.5B-Instruct is subject to the following:
- **Hallucinations:** Tucano2-qwen-1.5B-Instruct can produce content that can be mistaken as true facts, but are misleading or entirely false, i.e., hallucination.
- **Biases and Toxicity:** Tucano2-qwen-1.5B-Instruct inherits the social and historical stereotypes from the data used to train it. Given these biases, the model can produce toxic content, i.e., harmful, offensive, or detrimental to individuals, groups, or communities.
- **Language Limitations:** Tucano2-qwen-1.5B-Instruct is primarily designed to interact with Portuguese. Other languages might challenge its comprehension, leading to potential misinterpretations or errors in response.
- **Repetition and Verbosity:** Tucano2-qwen-1.5B-Instruct may get stuck on repetition loops (especially if the repetition penalty during generations is set to a meager value) or produce verbose responses unrelated to the prompt it was given.
Hence, even though Tucano2-qwen-1.5B-Instruct is released with a permissive license, we urge users to perform their risk analysis on them if they intend to use them for real-world applications.
## Evaluations
The table below compares the Tucano2 (Instruct variant) series against other chat models of similar size. We divide our evaluations into three sets:
- **Knowledge & Reasoning:** ARC-Challenge, ENEM, BLUEX, OAB Exams, BELEBELE, MMLU, GSM8K-PT
- **Instruction Following:** IFEval-PT
- **Coding:** HumanEval
The NPM (Normalized Performance Metric) provides a balanced view of model performance across tasks, accounting for each task's inherent difficulty by normalizing its evaluation score relative to its random baseline.
| | Total Avg. | Knowledge & Reasoning (NPM) | Instruction Following | Coding |
| ------------------------------ | ---------- | --------------------------- | --------------------- | ------ |
| **Tucano2-qwen-3.7B-Instruct** | 53.64 | 56.22 | 41.67 | 47.56 |
| Jurema-7B | 53.03 | 50.66 | 47 | 75.61 |
| Qwen2.5-3B-Instruct | 51.71 | 47.34 | 63.33 | 70.73 |
| Qwen3-4B | 51.36 | 42.33 | 79.33 | 86.59 |
| Gemma-3-Gaia-PT-BR-4b-it | 49.93 | 45 | 70.33 | 64.02 |
| SmolLM3-3B | 49.54 | 43.99 | 69.67 | 68.29 |
| Llama-3.2-3B-Instruct | 45.82 | 43.08 | 62.67 | 48.17 |
| Qwen2.5-1.5B-Instruct | 41.39 | 40.25 | 42 | 48.78 |
| **Tucano2-qwen-1.5B-Instruct** | 37.54 | 39.61 | 34.33 | 26.22 |
| Qwen3-1.7B | 36.3 | 28.24 | 65 | 64.02 |
| **Tucano2-qwen-0.5B-Instruct** | 26.08 | 27.77 | 30 | 10.37 |
| Qwen3-0.6B | 22.21 | 15.13 | 55 | 39.02 |
| Llama-3.2-1B-Instruct | 20.14 | 15.37 | 44.33 | 29.27 |
| Qwen2.5-0.5B-Instruct | 17.8 | 14.98 | 31 | 24.39 |
| Tucano-2b4-Instruct | 3.78 | 2.71 | 15 | 0 |
| Tucano-1b1-Instruct | 2.59 | 1.42 | 13.33 | 0 |
| TeenyTinyLlama-460m-Chat | 0.07 | -1.68 | 12.33 | 0 |
<details>
<summary><b>Evaluation Suite</b></summary>
| **Benchmark** | **n-shot** | **Type** | **Baseline** | **Metric** |
| ------------------------- | ---------- | ------------- | ------------ | ------------------------ |
| **Knowledge & Reasoning** | | | | |
| ARC-Challenge | 5-shot | MC-Q&A | 25 | `acc_norm` |
| ENEM | 3-shot | MC-Q&A | 20 | `acc` |
| BLUEX | 3-shot | MC-Q&A | 22.5 | `acc` |
| OAB Exams | 3-shot | MC-Q&A | 25 | `acc` |
| BELEBELE | 5-shot | MC-Q&A | 25 | `acc_norm` |
| MMLU | 5-shot | MC-Q&A | 25 | `acc` |
| GSM8K-PT | 0-shot | Math Problems | 0 | `flexible-extract` |
| **Instruction Following** | | | | |
| IFEval-PT | 0-shot | Instruction | 0 | `prompt_level_loose_acc` |
| **Coding** | | | | |
| HumanEval | 0-shot | Coding | 0 | `pass@1` |
</details>
<details>
<summary><b>Individual Benchmarks</b></summary>
| | BLUEX | ENEM | OAB | ARC Challenge | BELEBELE | MMLU | IFEval-PT | GSM8K-PT | HumanEval |
| ------------------------------ | ----- | ----- | ----- | ------------- | -------- | ----- | --------- | -------- | --------- |
| **Tucano2-qwen-3.7B-Instruct** | 64.53 | 72.92 | 54.31 | 60.34 | 85.22 | 64.64 | 41.67 | 53.81 | 47.56 |
| Jurema-7B | 63.42 | 70.96 | 64.97 | 52.56 | 88.44 | 49.91 | 47 | 30.29 | 75.61 |
| Qwen2.5-3B-Instruct | 56.88 | 68.65 | 46.79 | 41.71 | 84 | 58.22 | 63.33 | 51.9 | 70.73 |
| Qwen3-4B | 63.28 | 72.15 | 50.3 | 43.08 | 83.67 | 26.93 | 79.33 | 39.88 | 86.59 |
| Gemma-3-Gaia-PT-BR-4b-it | 50.9 | 64.52 | 43.46 | 54.7 | 78.89 | 51.49 | 70.33 | 51.29 | 64.02 |
| SmolLM3-3B | 53.55 | 64.73 | 41 | 52.74 | 78.67 | 53.23 | 69.67 | 44.44 | 68.29 |
| Llama-3.2-3B-Instruct | 52.02 | 59.13 | 44.97 | 43.93 | 78.56 | 52.14 | 62.67 | 55.1 | 48.17 |
| Qwen2.5-1.5B-Instruct | 52.02 | 61.79 | 44.28 | 39.74 | 76 | 51.91 | 42 | 42.16 | 48.78 |
| **Tucano2-qwen-1.5B-Instruct** | 52.85 | 62.7 | 43.42 | 50.26 | 77.56 | 52.54 | 34.33 | 19.71 | 26.22 |
| Qwen3-1.7B | 50.49 | 58.64 | 32.62 | 36.67 | 64.89 | 30.56 | 65 | 26.94 | 64.02 |
| **Tucano2-qwen-0.5B-Instruct** | 40.33 | 53.6 | 40.73 | 38.63 | 62.33 | 41.46 | 30 | 18.49 | 10.37 |
| Qwen3-0.6B | 34.91 | 41.15 | 29.75 | 30.51 | 37.11 | 26.48 | 55 | 31.66 | 39.02 |
| Llama-3.2-1B-Instruct | 30.04 | 34.01 | 30.84 | 32.82 | 41.56 | 35.15 | 44.33 | 26.56 | 29.27 |
| Qwen2.5-0.5B-Instruct | 30.18 | 34.08 | 29.34 | 27.44 | 50.67 | 39.54 | 31 | 14.69 | 24.39 |
| Tucano-2b4-Instruct | 25.87 | 20.01 | 26.74 | 31.97 | 24 | 26.72 | 15 | 2.05 | 0 |
| Tucano-1b1-Instruct | 22.95 | 19.94 | 25.33 | 30 | 24.89 | 25.59 | 13.33 | 1.67 | 0 |
| TeenyTinyLlama-460m-Chat | 17.25 | 18.19 | 19.73 | 26.84 | 22.89 | 26.97 | 12.33 | 2.05 | 0 |
</details>
<details>
<summary><b>Inference Samples on Various Tasks</b></summary>
### Math
**Prompt:**
<|im_start|>user
Como eu posso resolver o seguinte problema: 2x + 3 = 11?<|im_end|>
<|im_start|>assistant
**Response:**
Para encontrar a solução para esta equação, você precisa isolar x. Primeiro subtraia 3 de ambos os lados da equação e depois divida por 2. A resposta é x=4. Para verificar sua resposta substitua 4 na equação original (2 * 4) + 3 = 11, que simplifica para 8 + 3 = 11. Portanto, a solução está correta .<|im_end|>
---
### Structured Output
**Prompt:**
<|im_start|>system
Sempre responda em formato JSON estruturado conforme solicitado.<|im_end|>
<|im_start|>user
Extraia e resuma as informações principais do e-mail abaixo. Escreva sua resposta (em formato JSON) contendo as seguintes chaves: 'assunto', 'remetente', 'destinatário', 'resumo'. Aqui está o e-mail:
Assunto: RE: Atualização sobre o Projeto de Conservação de Bacias Hidrográfica
Carlos,
Obrigado por enviar o currículo atualizado. Tive a oportunidade de revisá-lo e achei que ficou ótimo! Você fez um excelente trabalho ao tornar as informações acessíveis e envolventes para uma ampla audiência.
Em relação à possível resistência dos membros da comunidade, concordo que devemos estar preparados para abordar suas preocupações. Uma ideia que tive foi incluir alguns exemplos práticos de como o uso indevido das águas no rio Amazonas ou na Baía de Guanabara e o descarte incorreto de resíduos sólidos têm prejudicado essas áreas. Ouvi-los falar diretamente sobre as consequências pode ser muito mais persuasivo do que simplesmente mostrar dados estatísticos.
Além disso, também considero importante destacarmos os ganhos econômicos trazidos pela adoção de práticas mais sustentáveis, como tarifas de água menores e gastos diminutos relacionados à limpeza urbana. No final das contas, as pessoas costumam responder bem quando veem vantagens tangíveis além dos aspectos puramente ambientais.
Diga-me o que você pensa dessas sugestões! Ficarei contente em ajudar a desenvolver ainda mais estratégias para vencer essa resistência e engajar as pessoas nessa nossa jornada.
Atenciosamente,
Helena<|im_end|>
<|im_start|>assistant
**Response:**
```json
{
"assunto": "RE: Atualização sobre o Projeto de Conservação de Bacias Hidrográficas",
"remetente": "Helena",
"destinatario": "Carlos",
"resumo": "A remetente elogia o novo currículo enviado pelo destinatário, sugerindo adicionar casos reais impactantes nas bacias hidrográficas brasileiras para aumentar a eficácia educacional. Ela enfatiza a importância de evidenciar benefícios financeiros associados às práticas sustentáveis."
}
```<|im_end|>
---
### Function Call / Tool Use
**Prompt:**
<|im_start|>system
Utilize as ferramentas para resolver as tarefas que o usuário lhe solicitar. Se as ferramentas forem insuficientes, explique ao usuário que não é possível completar a tarefa.
\# Tools / Ferramentas
Você pode chamar uma ou mais funções para auxiliar na consulta do usuário.
Você recebe assinaturas de funções dentro de tags XML <tools></tools>:
<tools>
{"type": "function", "function": {"name": "create_todo", "description": "Cria uma nova tarefa no aplicativo de todo.", "parameters": {"type": "object", "properties": {"task_description": {"type": "string", "description": "Descrição da tarefa a ser criada."}, "due_date": {"type": "string", "description": "Data de entrega da tarefa no formato YYYY-MM-DD."}}, "required": ["task_description", "due_date"]}}}
</tools>
Para cada chamada de função, retorne um objeto json com o nome da função e os argumentos dentro das tags XML <tool_call></tool_call>:
<tool_call>
{"name": <function-name>, "arguments": <args-json-object>}
</tool_call><|im_end|>
<|im_start|>user
Eu preciso criar uma nova tarefa para o meu projeto.<|im_end|>
<|im_start|>assistant
Claro, eu posso ajudar com isso. Você poderia, por favor, me fornecer a descrição da tarefa e a data de entrega?<|im_end|>
<|im_start|>user
A tarefa é finalizar o relatório do projeto e a data de entrega é 2022-03-15.<|im_end|>
<|im_start|>assistant
**Response:**
<tool_call>
{"name": "create_todo", "arguments": {"task_description": "Finalizing the project report", "due_date": "2022-03-15"}}
</tool_call><|im_end|>
---
### Similarity Scoring
**Prompt:**
<|im_start|>user
Frase 1: O artista pintou um quadro inspirado nos campos floridos da primavera.
Frase 2: O criador produziu uma obra que representava paisagens urbanas cinzentas.
Pergunta: Qu\u00e3o similares s\u00e3o as duas frases? D\u00ea uma pontua\u00e7\u00e3o entre 1,0 a 5,0.
Resposta:<|im_end|>
<|im_start|>assistant
**Response:**
2.5. As duas frases apresentam situações diferentes quanto à temática e estilo artístico, resultando numa pontuação média de similaridade de 2.5, indicando pouca relação entre elas.<|im_end|>
---
### Classification
**Prompt:**
<|im_start|>user
Classifique o sentimento da seguinte resenha de filme como 'Positiva', 'Negativa' ou 'Neutra':
"O filme foi uma experiência cinematográfica incrível, com atuações excepcionais e uma trama envolvente que me manteve na ponta da cadeira do começo ao fim."<|im_end|>
<|im_start|>assistant
**Response:**
Sentimento: Positivo. A crítica destaca aspectos positivos como performances excelentes e um enredo cativante, indicando uma opinião favorável sobre o filme.<|im_end|>
---
</details>
Below, we compare the performance of Tucano2-qwen-1.5B-Instruct with Qwen3-1.7B, a strong baseline in the 1.5B parameter range. The percentages represent the absolute difference in performance between the two models on each benchmark. All other plots can be found in the [.plots](https://huggingface.co/Polygl0t/Tucano2-qwen-1.5B-Instruct/tree/main/.plots/) folder.
**Tucano2-qwen-1.5B-Instruct vs Qwen3-1.7B**
![Performance Comparison](./.plots/model_comparison.png)
## Cite as 🤗
```latex
@misc{correa2026tucano2cool,
title={{Tucano 2 Cool: Better Open Source LLMs for Portuguese}},
author={Nicholas Kluge Corr{\^e}a and Aniket Sen and Shiza Fatimah and Sophia Falk and Lennard Landgraf and Julia Kastner and Lucie Flek},
year={2026},
eprint={2603.03543},
archivePrefix={arXiv},
primaryClass={cs.CL},
url={https://arxiv.org/abs/2603.03543},
}
```
## Aknowlegments
Polyglot is a project funded by the Federal Ministry of Education and Research (BMBF) and the Ministry of Culture and Science of the State of North Rhine-Westphalia (MWK) as part of TRA Sustainable Futures (University of Bonn) and the Excellence Strategy of the federal and state governments.
We also gratefully acknowledge the granted access to the [Marvin cluster](https://www.hpc.uni-bonn.de/en/systems/marvin) hosted by [University of Bonn](https://www.uni-bonn.de/en) along with the support provided by its High Performance Computing & Analytics Lab.
## License
Tucano2-qwen-1.5B-Instruct is licensed under the Apache License, Version 2.0. For more details, see the [LICENSE](LICENSE) file.

83
chat_template.jinja Normal file
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@@ -0,0 +1,83 @@
{#- Handle tool/function calling setup #}
{%- if tools %}
{{- '<|im_start|>system\n' }}
{#- Include system message if present #}
{%- if messages[0].role == 'system' %}
{{- messages[0].content + '\n\n' }}
{%- endif %}
{#- Add tool calling instructions in Portuguese #}
{{- "# Tools / Ferramentas\n\nVocê pode chamar uma ou mais funções para auxiliar na consulta do usuário.\n\nVocê recebe assinaturas de funções dentro de tags XML <tools></tools>:\n<tools>" }}
{%- for tool in tools %}
{{- "\n" }}
{{- tool | tojson }}
{%- endfor %}
{{- "\n</tools>\n\nPara cada chamada de função, retorne um objeto json com o nome da função e os argumentos dentro das tags XML <tool_call></tool_call>:\n<tool_call>\n{\"name\": <function-name>, \"arguments\": <args-json-object>}\n</tool_call><|im_end|>\n" }}
{%- else %}
{#- Standard system message without tools #}
{%- if messages[0].role == 'system' %}
{{- '<|im_start|>system\n' + messages[0].content + '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{#- Process each message in the conversation #}
{%- for message in messages %}
{#- Normalize content to string #}
{%- if message.content is string %}
{%- set content = message.content %}
{%- else %}
{%- set content = '' %}
{%- endif %}
{#- Handle user messages and non-first system messages #}
{%- if (message.role == "user") or (message.role == "system" and not loop.first) %}
{{- '<|im_start|>' + message.role + '\n' + content + '<|im_end|>' + '\n' }}
{#- Handle assistant messages without reasoning #}
{%- elif message.role == "assistant" %}
{{- '<|im_start|>' + message.role }}
{% generation %}
{{- content }}
{#- Add tool calls if present #}
{%- if message.tool_calls %}
{%- for tool_call in message.tool_calls %}
{%- if (loop.first and content) or (not loop.first) %}
{{- '\n' }}
{%- endif %}
{#- Normalize tool call format #}
{%- if tool_call.function %}
{%- set tool_call = tool_call.function %}
{%- endif %}
{{- '<tool_call>\n{"name": "' }}
{{- tool_call.name }}
{{- '", "arguments": ' }}
{%- if tool_call.arguments is string %}
{{- tool_call.arguments }}
{%- else %}
{{- tool_call.arguments | tojson }}
{%- endif %}
{{- '}\n</tool_call>' }}
{%- endfor %}
{%- endif %}
{{- '<|im_end|>' }}
{% endgeneration %}
{#- Handle tool response messages #}
{%- elif message.role == "tool" %}
{#- Group consecutive tool responses under one user message #}
{%- if loop.first or (messages[loop.index0 - 1].role != "tool") %}
{{- '<|im_start|>user' }}
{%- endif %}
{{- '\n<tool_response>\n' }}
{{- content }}
{{- '\n</tool_response>' }}
{%- if loop.last or (messages[loop.index0 + 1].role != "tool") %}
{{- '<|im_end|>\n' }}
{%- endif %}
{%- endif %}
{%- endfor %}
{#- Add generation prompt if requested #}
{%- if add_generation_prompt %}
{{- '<|im_start|>assistant\n' }}
{%- endif %}

61
config.json Normal file
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@@ -0,0 +1,61 @@
{
"architectures": [
"Qwen3ForCausalLM"
],
"attention_bias": false,
"attention_dropout": 0.0,
"bos_token_id": 1,
"dtype": "bfloat16",
"eos_token_id": 2,
"head_dim": 128,
"hidden_act": "silu",
"hidden_size": 2048,
"initializer_range": 0.02,
"intermediate_size": 6144,
"layer_types": [
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention",
"full_attention"
],
"max_position_embeddings": 4096,
"max_window_layers": 28,
"model_type": "qwen3",
"num_attention_heads": 16,
"num_hidden_layers": 28,
"num_key_value_heads": 8,
"pad_token_id": 49109,
"rms_norm_eps": 1e-06,
"rope_scaling": null,
"rope_theta": 1000000,
"sliding_window": null,
"tie_word_embeddings": true,
"transformers_version": "4.57.3",
"use_cache": false,
"use_sliding_window": false,
"vocab_size": 49152
}

207
evals.yaml Normal file
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@@ -0,0 +1,207 @@
evaluations:
arc_challenge_poly_pt_acc: 0.47094017094017093
arc_challenge_poly_pt_acc_norm: 0.5025641025641026
arc_challenge_poly_pt_acc_norm_stderr: 0.014623692220064299
arc_challenge_poly_pt_acc_stderr: 0.014599164650716437
arc_challenge_poly_pt_alias: arc_challenge_poly_pt
assin2_rte_acc,all: 0.9178921568627451
assin2_rte_acc_stderr,all: 0.0039410227425137
assin2_rte_alias: assin2_rte
assin2_rte_f1_macro,all: 0.9176110196232412
assin2_rte_f1_macro_stderr,all: 0.003955066154708788
assin2_sts_alias: assin2_sts
assin2_sts_mse,all: 0.8036356209150326
assin2_sts_mse_stderr,all: N/A
assin2_sts_pearson,all: 0.7335010773330974
assin2_sts_pearson_stderr,all: 0.006813537226494434
assin_entailment_acc: 0.6955
assin_entailment_acc_stderr: 0.007277236909999638
assin_entailment_alias: assin_entailment
assin_paraphrase_acc: 0.677
assin_paraphrase_acc_stderr: 0.007394688658428343
assin_paraphrase_alias: assin_paraphrase
belebele_por_Latn_acc: 0.7755555555555556
belebele_por_Latn_acc_norm: 0.7755555555555556
belebele_por_Latn_acc_norm_stderr: 0.013914930474237296
belebele_por_Latn_acc_stderr: 0.013914930474237296
belebele_por_Latn_alias: belebele_por_Latn
bluex_acc,all: 0.5285118219749653
bluex_acc,exam_id__UNICAMP_2018: 0.5185185185185185
bluex_acc,exam_id__UNICAMP_2019: 0.44
bluex_acc,exam_id__UNICAMP_2020: 0.509090909090909
bluex_acc,exam_id__UNICAMP_2021_1: 0.4782608695652174
bluex_acc,exam_id__UNICAMP_2021_2: 0.47058823529411764
bluex_acc,exam_id__UNICAMP_2022: 0.6410256410256411
bluex_acc,exam_id__UNICAMP_2023: 0.6046511627906976
bluex_acc,exam_id__UNICAMP_2024: 0.5333333333333333
bluex_acc,exam_id__USP_2018: 0.4074074074074074
bluex_acc,exam_id__USP_2019: 0.5
bluex_acc,exam_id__USP_2020: 0.6071428571428571
bluex_acc,exam_id__USP_2021: 0.5384615384615384
bluex_acc,exam_id__USP_2022: 0.42857142857142855
bluex_acc,exam_id__USP_2023: 0.6818181818181818
bluex_acc,exam_id__USP_2024: 0.6341463414634146
bluex_acc_stderr,all: 0.010703918286665611
bluex_acc_stderr,exam_id__UNICAMP_2018: 0.03937226593136202
bluex_acc_stderr,exam_id__UNICAMP_2019: 0.0404309855509269
bluex_acc_stderr,exam_id__UNICAMP_2020: 0.0389358045204499
bluex_acc_stderr,exam_id__UNICAMP_2021_1: 0.04241243284078559
bluex_acc_stderr,exam_id__UNICAMP_2021_2: 0.04027645736401125
bluex_acc_stderr,exam_id__UNICAMP_2022: 0.044230892510041545
bluex_acc_stderr,exam_id__UNICAMP_2023: 0.04301638178039838
bluex_acc_stderr,exam_id__UNICAMP_2024: 0.04292586445321273
bluex_acc_stderr,exam_id__USP_2018: 0.0385530737645887
bluex_acc_stderr,exam_id__USP_2019: 0.04559218480567802
bluex_acc_stderr,exam_id__USP_2020: 0.03761017438804557
bluex_acc_stderr,exam_id__USP_2021: 0.04001597061911072
bluex_acc_stderr,exam_id__USP_2022: 0.04070596417180048
bluex_acc_stderr,exam_id__USP_2023: 0.040428408321166086
bluex_acc_stderr,exam_id__USP_2024: 0.04340314503826957
bluex_alias: bluex
calame_pt_acc: 0.5438342967244701
calame_pt_acc_stderr: 0.010934163426897668
calame_pt_alias: calame_pt
calame_pt_perplexity: 7.8689968011124725
calame_pt_perplexity_stderr: 0.41766365172631903
enem_challenge_acc,all: 0.6270118964310707
enem_challenge_acc,exam_id__2009: 0.6347826086956522
enem_challenge_acc,exam_id__2010: 0.6752136752136753
enem_challenge_acc,exam_id__2011: 0.6923076923076923
enem_challenge_acc,exam_id__2012: 0.6379310344827587
enem_challenge_acc,exam_id__2013: 0.6111111111111112
enem_challenge_acc,exam_id__2014: 0.6330275229357798
enem_challenge_acc,exam_id__2015: 0.5462184873949579
enem_challenge_acc,exam_id__2016: 0.6198347107438017
enem_challenge_acc,exam_id__2016_2: 0.6097560975609756
enem_challenge_acc,exam_id__2017: 0.6120689655172413
enem_challenge_acc,exam_id__2022: 0.5939849624060151
enem_challenge_acc,exam_id__2023: 0.6592592592592592
enem_challenge_acc_stderr,all: 0.0073957026155519546
enem_challenge_acc_stderr,exam_id__2009: 0.025965719340461144
enem_challenge_acc_stderr,exam_id__2010: 0.02502188604536942
enem_challenge_acc_stderr,exam_id__2011: 0.02467006930960307
enem_challenge_acc_stderr,exam_id__2012: 0.025714191658261398
enem_challenge_acc_stderr,exam_id__2013: 0.02702908679710427
enem_challenge_acc_stderr,exam_id__2014: 0.026672592927520465
enem_challenge_acc_stderr,exam_id__2015: 0.02626461947663519
enem_challenge_acc_stderr,exam_id__2016: 0.025564903994126094
enem_challenge_acc_stderr,exam_id__2016_2: 0.025424569351359966
enem_challenge_acc_stderr,exam_id__2017: 0.026108373622566433
enem_challenge_acc_stderr,exam_id__2022: 0.024621347603181173
enem_challenge_acc_stderr,exam_id__2023: 0.023527643017571674
enem_challenge_alias: enem
faquad_nli_acc,all: 0.8584615384615385
faquad_nli_acc_stderr,all: 0.009630496129889232
faquad_nli_alias: faquad_nli
faquad_nli_f1_macro,all: 0.7762511973180077
faquad_nli_f1_macro_stderr,all: 0.014629139687819745
global_piqa_completions_por_latn_braz_acc: 0.75
global_piqa_completions_por_latn_braz_acc_bytes: 0.7
global_piqa_completions_por_latn_braz_acc_bytes_stderr: 0.04605661864718383
global_piqa_completions_por_latn_braz_acc_norm: 0.71
global_piqa_completions_por_latn_braz_acc_norm_stderr: 0.045604802157206865
global_piqa_completions_por_latn_braz_acc_stderr: 0.04351941398892446
global_piqa_completions_por_latn_braz_alias: global_piqa_completions_por_latn_braz
gsm8k_pt_alias: gsm8k_pt
gsm8k_pt_exact_match,flexible-extract: 0.19710806697108066
gsm8k_pt_exact_match,strict-match: 0.0
gsm8k_pt_exact_match_stderr,flexible-extract: 0.010978635935133022
gsm8k_pt_exact_match_stderr,strict-match: 0.0
hatebr_offensive_acc,all: 0.805
hatebr_offensive_acc_stderr,all: 0.00750545853190643
hatebr_offensive_alias: hatebr_offensive_binary
hatebr_offensive_f1_macro,all: 0.7986300308904273
hatebr_offensive_f1_macro_stderr,all: 0.007718372444189955
hellaswag_poly_pt_acc: 0.43222450969769205
hellaswag_poly_pt_acc_norm: 0.5524975620327229
hellaswag_poly_pt_acc_norm_stderr: 0.00517617669289931
hellaswag_poly_pt_acc_stderr: 0.005156906089879843
hellaswag_poly_pt_alias: hellaswag_poly_pt
humaneval_instruct_alias: humaneval_instruct
humaneval_instruct_pass@1,create_test: 0.2621951219512195
humaneval_instruct_pass@1_stderr,create_test: 0.03445000289173463
ifeval_pt_alias: ifeval_pt
ifeval_pt_inst_level_loose_acc: 0.46511627906976744
ifeval_pt_inst_level_loose_acc_stderr: N/A
ifeval_pt_inst_level_strict_acc: 0.4232558139534884
ifeval_pt_inst_level_strict_acc_stderr: N/A
ifeval_pt_prompt_level_loose_acc: 0.3433333333333333
ifeval_pt_prompt_level_loose_acc_stderr: 0.027459642357098978
ifeval_pt_prompt_level_strict_acc: 0.30333333333333334
ifeval_pt_prompt_level_strict_acc_stderr: 0.026585019936500975
lambada_poly_pt_acc: 0.5905297884727343
lambada_poly_pt_acc_stderr: 0.006850844880897535
lambada_poly_pt_alias: lambada_poly_pt
lambada_poly_pt_perplexity: 9.837299391644223
lambada_poly_pt_perplexity_stderr: 0.3695848037190668
mmlu_poly_pt_acc: 0.5254428099669769
mmlu_poly_pt_acc_stderr: 0.0043261940110685525
mmlu_poly_pt_alias: mmlu_poly_pt
oab_exams_acc,all: 0.43416856492027334
oab_exams_acc,exam_id__2010-01: 0.3411764705882353
oab_exams_acc,exam_id__2010-02: 0.46
oab_exams_acc,exam_id__2011-03: 0.3838383838383838
oab_exams_acc,exam_id__2011-04: 0.4
oab_exams_acc,exam_id__2011-05: 0.5
oab_exams_acc,exam_id__2012-06: 0.4125
oab_exams_acc,exam_id__2012-06a: 0.425
oab_exams_acc,exam_id__2012-07: 0.425
oab_exams_acc,exam_id__2012-08: 0.4625
oab_exams_acc,exam_id__2012-09: 0.38961038961038963
oab_exams_acc,exam_id__2013-10: 0.5
oab_exams_acc,exam_id__2013-11: 0.575
oab_exams_acc,exam_id__2013-12: 0.475
oab_exams_acc,exam_id__2014-13: 0.425
oab_exams_acc,exam_id__2014-14: 0.475
oab_exams_acc,exam_id__2014-15: 0.5
oab_exams_acc,exam_id__2015-16: 0.425
oab_exams_acc,exam_id__2015-17: 0.4230769230769231
oab_exams_acc,exam_id__2015-18: 0.4
oab_exams_acc,exam_id__2016-19: 0.47435897435897434
oab_exams_acc,exam_id__2016-20: 0.4625
oab_exams_acc,exam_id__2016-20a: 0.375
oab_exams_acc,exam_id__2016-21: 0.4125
oab_exams_acc,exam_id__2017-22: 0.4375
oab_exams_acc,exam_id__2017-23: 0.3375
oab_exams_acc,exam_id__2017-24: 0.425
oab_exams_acc,exam_id__2018-25: 0.4125
oab_exams_acc_stderr,all: 0.006102155835634948
oab_exams_acc_stderr,exam_id__2010-01: 0.029640791842627794
oab_exams_acc_stderr,exam_id__2010-02: 0.02872289890111472
oab_exams_acc_stderr,exam_id__2011-03: 0.02813693132118459
oab_exams_acc_stderr,exam_id__2011-04: 0.03154240021693644
oab_exams_acc_stderr,exam_id__2011-05: 0.03224120715021378
oab_exams_acc_stderr,exam_id__2012-06: 0.031634579715402525
oab_exams_acc_stderr,exam_id__2012-06a: 0.031920386806255
oab_exams_acc_stderr,exam_id__2012-07: 0.03196335345646489
oab_exams_acc_stderr,exam_id__2012-08: 0.03218895689579674
oab_exams_acc_stderr,exam_id__2012-09: 0.0320702088032968
oab_exams_acc_stderr,exam_id__2013-10: 0.032306001943973275
oab_exams_acc_stderr,exam_id__2013-11: 0.03198130854191419
oab_exams_acc_stderr,exam_id__2013-12: 0.03227420874356609
oab_exams_acc_stderr,exam_id__2014-13: 0.03197697369553699
oab_exams_acc_stderr,exam_id__2014-14: 0.03231427645661526
oab_exams_acc_stderr,exam_id__2014-15: 0.03281148582087051
oab_exams_acc_stderr,exam_id__2015-16: 0.03193036659905079
oab_exams_acc_stderr,exam_id__2015-17: 0.03231882364931395
oab_exams_acc_stderr,exam_id__2015-18: 0.031606839641845616
oab_exams_acc_stderr,exam_id__2016-19: 0.03261356291633692
oab_exams_acc_stderr,exam_id__2016-20: 0.03217012301961201
oab_exams_acc_stderr,exam_id__2016-20a: 0.03124902402233319
oab_exams_acc_stderr,exam_id__2016-21: 0.03172689133529826
oab_exams_acc_stderr,exam_id__2017-22: 0.03202936221429109
oab_exams_acc_stderr,exam_id__2017-23: 0.03052924311679464
oab_exams_acc_stderr,exam_id__2017-24: 0.031892680920180556
oab_exams_acc_stderr,exam_id__2018-25: 0.03181646004259634
oab_exams_alias: oab_exams
portuguese_hate_speech_acc,all: 0.7708578143360753
portuguese_hate_speech_acc_stderr,all: 0.010179285936061107
portuguese_hate_speech_alias: portuguese_hate_speech_binary
portuguese_hate_speech_f1_macro,all: 0.7383400530746562
portuguese_hate_speech_f1_macro_stderr,all: 0.011391910083076176
tweetsentbr_acc,all: 0.663681592039801
tweetsentbr_acc_stderr,all: 0.007468862153058396
tweetsentbr_alias: tweetsentbr
tweetsentbr_f1_macro,all: 0.57818681435807
tweetsentbr_f1_macro_stderr,all: 0.007796495757272563
step: 69750

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generation_config.json Normal file
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2
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model_name: Tucano2-qwen-1.5B-Instruct
results:
niah_pt_multikey_1_1024: 0.556
niah_pt_multikey_1_1024_stderr: 0.022242244375731048
niah_pt_multikey_1_2048: 0.572
niah_pt_multikey_1_2048_stderr: 0.022149790663861947
niah_pt_multikey_1_4096: 0.504
niah_pt_multikey_1_4096_stderr: N/A
niah_pt_multikey_1_alias: " - niah_pt_multikey_1"
niah_pt_multikey_2_1024: 0.582
niah_pt_multikey_2_1024_stderr: 0.022080014812228193
niah_pt_multikey_2_2048: 0.372
niah_pt_multikey_2_2048_stderr: 0.021637197985722334
niah_pt_multikey_2_4096: 0.132
niah_pt_multikey_2_4096_stderr: N/A
niah_pt_multikey_2_alias: " - niah_pt_multikey_2"
niah_pt_multikey_3_1024: 0.688
niah_pt_multikey_3_1024_stderr: 0.020740596536488017
niah_pt_multikey_3_2048: 0.496
niah_pt_multikey_3_2048_stderr: 0.022382357781962105
niah_pt_multikey_3_4096: 0.208
niah_pt_multikey_3_4096_stderr: N/A
niah_pt_multikey_3_alias: " - niah_pt_multikey_3"
niah_pt_multiquery_1024: 0.5075
niah_pt_multiquery_1024_stderr: 0.014319483062959385
niah_pt_multiquery_2048: 0.445
niah_pt_multiquery_2048_stderr: 0.014312921415241465
niah_pt_multiquery_4096: 0.3605
niah_pt_multiquery_4096_stderr: N/A
niah_pt_multiquery_alias: " - niah_pt_multiquery"
niah_pt_multivalue_1024: 0.3765
niah_pt_multivalue_1024_stderr: 0.015089761489288265
niah_pt_multivalue_2048: 0.352
niah_pt_multivalue_2048_stderr: 0.014443310508220534
niah_pt_multivalue_4096: 0.3525
niah_pt_multivalue_4096_stderr: N/A
niah_pt_multivalue_alias: " - niah_pt_multivalue"
niah_pt_single_1_1024: 0.658
niah_pt_single_1_1024_stderr: 0.021236147199899316
niah_pt_single_1_2048: 0.578
niah_pt_single_1_2048_stderr: 0.022109039310618563
niah_pt_single_1_4096: 0.476
niah_pt_single_1_4096_stderr: N/A
niah_pt_single_1_alias: " - niah_pt_single_1"
niah_pt_single_2_1024: 0.452
niah_pt_single_2_1024_stderr: 0.022279694107843542
niah_pt_single_2_2048: 0.496
niah_pt_single_2_2048_stderr: 0.022382357781962105
niah_pt_single_2_4096: 0.478
niah_pt_single_2_4096_stderr: N/A
niah_pt_single_2_alias: " - niah_pt_single_2"
niah_pt_single_3_1024: 0.464
niah_pt_single_3_1024_stderr: 0.022324981738385333
niah_pt_single_3_2048: 0.488
niah_pt_single_3_2048_stderr: 0.022376626797927058
niah_pt_single_3_4096: 0.502
niah_pt_single_3_4096_stderr: N/A
niah_pt_single_3_alias: " - niah_pt_single_3"
ruler_pt_4096: 0.4209333333333333
ruler_pt_4096_stderr: N/A
ruler_pt_alias: ruler_pt
ruler_pt_cwe_1024: 0.2336
ruler_pt_cwe_1024_stderr: 0.007601160126621533
ruler_pt_cwe_2048: 0.0674
ruler_pt_cwe_2048_stderr: 0.0035916960563391557
ruler_pt_cwe_4096: 0.24180000000000001
ruler_pt_cwe_4096_stderr: N/A
ruler_pt_cwe_alias: " - ruler_pt_cwe"
ruler_pt_fwe_1024: 0.8326666666666667
ruler_pt_fwe_1024_stderr: 0.010739688265042091
ruler_pt_fwe_2048: 0.6426666666666666
ruler_pt_fwe_2048_stderr: 0.010748185494645086
ruler_pt_fwe_4096: 0.5346666666666666
ruler_pt_fwe_4096_stderr: N/A
ruler_pt_fwe_alias: " - ruler_pt_fwe"
ruler_pt_vt_1024: 0.9568
ruler_pt_vt_1024_stderr: 0.0061337399301781715
ruler_pt_vt_2048: 0.9288
ruler_pt_vt_2048_stderr: 0.007954096762714245
ruler_pt_vt_4096: 0.8408
ruler_pt_vt_4096_stderr: N/A
ruler_pt_vt_alias: " - ruler_pt_vt"

30
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# Directory settings
checkpoint_dir: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct"
train_dataset_dir:
# Total: 28,437 samples (x5 epochs)
# Harmfull samples (without reasoning): 4,267 samples
- /polyglot/portuguese/gigaverbo-v2-dpo/harmfull-no-reasoning
# Harmfull samples (with reasoning, stripped): 4,008 samples
- /polyglot/portuguese/gigaverbo-v2-dpo/harmfull-reasoning-stripped
# Harmless samples (without reasoning): 10,521 samples
- /polyglot/portuguese/gigaverbo-v2-dpo/harmless-no-reasoning
# Harmless samples (with reasoning, stripped): 9,641 samples
- /polyglot/portuguese/gigaverbo-v2-dpo/harmless-reasoning-stripped
val_dataset_dir: null
dataset_type: "jsonl"
cache_dir: "/lustre/mlnvme/data/polyglot/.cache"
# Data loading settings
pin_memory: true
num_workers_for_dataloader: 16
shuffle_dataset: true
mask_eos_token: false
mask_pad_token: false
# Model architecture settings
vocab_size: 49152
num_hidden_layers: 28
num_attention_heads: 16
num_key_value_heads: 8
head_dim: 128
hidden_size: 2048
intermediate_size: 6144
max_position_embeddings: 4096
tie_word_embeddings: true
hidden_act: "silu"
output_hidden_states: false
attn_implementation: "flash_attention_2"
use_cache: false
no_rope_layer_interval: null
rope_theta: 1000000.0
rope_scale_factor: null
rms_norm_eps: 0.000001
# Training settings
total_batch_size: 524288
micro_batch_size: 4
gradient_accumulation_steps: 4
eval_micro_batch_size: null
num_train_epochs: 5
warmup_ratio: 0.1
max_learning_rate: 0.000005
min_learning_rate: 0.0
muon_learning_rate: null
weight_decay: 0.0
beta1: 0.9
beta2: 0.95
eps: 0.00000001
lr_decay_type: "cosine"
use_sqrt: false
lr_decay_iters_coef: 1.
seed: 42
max_steps: 1115
max_grad_norm: 1.0
# APO settings
loss_type: "apo_zero"
dpo_beta: 0.5
precompute_ref_log_probs: true
truncation_mode: "keep_end"
# Precision and optimization settings
torch_compile: false
mat_mul_precision: "highest"
tf32: true
bf16: true
gradient_checkpointing: true
use_liger_kernel: false
static_graph: false
# Hub settings
push_to_hub: false
hub_token: null
hub_model_id: null
# Tokenizer and Reference model
tokenizer_name_or_path: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct-SFT"
chat_template_path: null
reference_model: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct-SFT"
continual_pretraining: true
# Checkpoint settings
resume_from_checkpoint: null
checkpointing_steps: 1000
begin_new_stage: true
stage_name: "single_cosine"
# Miscellaneous settings
sanity_check: false
sanity_check_num_samples: 100000
wandb_token: null
wandb_id: "tucano2-qwen-1.5b-instruct-apo"
wandb_project: "Polyglot"
wandb_desc: "Developing LLMs for low-resource languages"

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# Directory settings
checkpoint_dir: "/polyglot/portuguese/checkpoints/models/Tucano2-qwen-1.5B-Instruct-SFT"
train_dataset_dir:
# Total: ~874 million tokens (x5 epochs)
# Coding: ~2.3 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/code
# Function Calling: ~17.5 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/function_call
# General Instruction Following: ~700 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/general
# Math and CoT: ~27 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/math_cot
# Retrieval Augmented Generation: ~2.2 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/retrieval
# Structured Outputs: ~35 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/structured
# Summarization: ~290 thousand tokens
- /polyglot/portuguese/gigaverbo-v2-sft/summarization
# Translation: ~5.7 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/translation
# Chosen Data from Preference Dataset: ~14 million tokens
- /polyglot/portuguese/gigaverbo-v2-sft/dpo
val_dataset_dir: null
dataset_type: "jsonl"
cache_dir: "/lustre/mlnvme/data/polyglot/.cache"
# Data loading settings
pin_memory: true
num_workers_for_dataloader: 16
shuffle_dataset: true
mask_eos_token: false
mask_pad_token: true
# Model architecture settings
vocab_size: 49152
num_hidden_layers: 28
num_attention_heads: 16
num_key_value_heads: 8
head_dim: 128
hidden_size: 2048
intermediate_size: 6144
max_position_embeddings: 4096
tie_word_embeddings: true
hidden_act: "silu"
output_hidden_states: false
attn_implementation: "flash_attention_2"
use_cache: false
no_rope_layer_interval: null
rope_theta: 1000000.0
rope_scale_factor: null
rms_norm_eps: 0.000001
# Training settings
total_batch_size: 524288
micro_batch_size: 4
gradient_accumulation_steps: 4
eval_micro_batch_size: null
num_train_epochs: 5
warmup_ratio: 0.1
max_learning_rate: 0.000075
min_learning_rate: 0.0
muon_learning_rate: null
weight_decay: 0.0
beta1: 0.9
beta2: 0.95
eps: 0.00000001
lr_decay_type: "cosine"
use_sqrt: false
lr_decay_iters_coef: 1.
seed: 42
max_steps: 68635
max_grad_norm: 1.0
# SFT settings
packing: false
assistant_only_loss: true
# Precision and optimization settings
torch_compile: false
mat_mul_precision: "highest"
tf32: true
bf16: true
gradient_checkpointing: true
use_liger_kernel: true
static_graph: false
# Hub settings
push_to_hub: false
hub_token: null
hub_model_id: null
# Tokenizer and Reference model
tokenizer_name_or_path: "Polygl0t/Tucano2-qwen-1.5B-Base"
chat_template_path: null
reference_model: "Polygl0t/Tucano2-qwen-1.5B-Base"
continual_pretraining: true
# Checkpoint settings
resume_from_checkpoint: null
checkpointing_steps: 1000
begin_new_stage: true
stage_name: "single_cosine"
# Miscellaneous settings
sanity_check: false
sanity_check_num_samples: 100000
wandb_token: null
wandb_id: "tucano2-qwen-1.5b-instruct-sft"
wandb_project: "Polyglot"
wandb_desc: "Developing LLMs for low-resource languages"