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Model: corre-social/Drummond-1b1-Instruct
Source: Original Platform

.gitattributes

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README.md

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+
+---
+library_name: transformers
+base_model: TucanoBR/Tucano-1b1-Instruct
+tags:
+- trl
+- sft
+- portuguese
+- pt-br
+- reasoning
+- chain-of-thought
+license: apache-2.0
+language:
+- pt
+datasets:
+- corre-social/s1_dataset_ptbr_1k_tokenized
+---
+
+# Drummond-1b1-Instruct
+
+<img src="https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/transformers_logo_name.png" alt="Transformers" width="200"/>
+
+## Resumo do Modelo
+
+O **Drummond-1b1-Instruct** é um modelo de linguagem focado em seguir instruções e raciocínio em Português (PT-BR). Ele é um *fine-tune* do modelo [Tucano-1b1-Instruct](https://huggingface.co/TucanoBR/Tucano-1b1-Instruct), treinado especificamente para gerar cadeias de pensamento ("thinking process") antes de fornecer a resposta final.
+
+Este modelo utiliza a arquitetura herdada do Tucano e foi otimizado para tarefas que exigem raciocínio estruturado com baixo custo computacional.
+
+- **Desenvolvido por:** Corre Social
+- **Modelo Base:** TucanoBR/Tucano-1b1-Instruct
+- **Idioma:** Português (PT-BR)
+- **Tamanho do Contexto:** 2048 tokens
+- **Licença:** Apache 2.0 (Verificar modelo base)
+
+## Detalhes de Treinamento
+
+O modelo foi treinado utilizando técnicas modernas de *Supervised Fine-Tuning* (SFT) focadas em eficiência e qualidade de instrução.
+
+### Tecnologias Utilizadas
+
+O treinamento foi realizado utilizando o ecossistema Hugging Face e PyTorch:
+
+* **Biblioteca de Treino:** [TRL (Transformer Reinforcement Learning)](https://github.com/huggingface/trl) versão 0.12.0.
+* **Otimização de Memória:** `bitsandbytes` para otimizadores de 8-bit.
+* **Monitoramento:** Weights & Biases (WandB).
+* **Hardware:** Treinado em GPU com suporte a `bfloat16`.
+
+### Técnicas de Treinamento
+
+1.  **Supervised Fine-Tuning (SFT):** O modelo foi ajustado em um dataset de instruções para alinhar o comportamento de resposta.
+2.  **Completion Only Loss:** Utilizamos o `DataCollatorForCompletionOnlyLM`. Esta técnica é crucial: o modelo **não** aprende a prever a instrução do usuário, apenas a resposta e o raciocínio. Isso evita que o modelo "alucine" instruções e foca a perda (loss) apenas na geração útil.
+    * *Instruction Template:* `<instruction>`
+    * *Response Template:* `<|im_start|>think`
+3.  **Special Tokens & ChatML:** Foram adicionados tokens especiais (`<|im_start|>`, `<|im_end|>`) e o token de gatilho de pensamento `think` para estruturar o formato de *Chain of Thought*.
+4.  **Otimização de Precisão:**
+    * Uso de **BF16 (BFloat16)** para estabilidade numérica durante o treino.
+    * Otimizador **AdamW 8-bit** para reduzir o consumo de VRAM.
+    * **Gradient Checkpointing** ativado para permitir *batch sizes* maiores ou modelos maiores em GPUs limitadas.
+
+### Hiperparâmetros
+
+| Parâmetro | Valor |
+| :--- | :--- |
+| **Epochs** | 5 |
+| **Learning Rate** | 1e-5 |
+| **Batch Size (Efetivo)** | 4 (1 per device * 4 accumulation steps) |
+| **Context Window** | 2048 tokens |
+| **Optimizer** | adamw_8bit |
+| **Precision** | bf16 |
+| **LRScheduler** | Linear (com 10 warmup steps) |
+
+### Dados de Treinamento
+
+* **Dataset:** `corre-social/s1_dataset_ptbr_1k_tokenized`
+* **Tamanho:** ~1.000 exemplos de alta qualidade.
+* **Foco:** O dataset contém exemplos estruturados para estimular o modelo a "pensar" (`think`) antes de responder.
+
+## Como Usar
+
+Para utilizar o modelo, é recomendável usar a formatação de prompt correta para ativar o modo de raciocínio:
+
+```python
+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+model_id = "corre-social/Drummond-1b1-Instruct"
+
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    model_id,
+    torch_dtype=torch.bfloat16,
+    device_map="auto"
+)
+
+# Template específico usado no treino
+prompt = """<instruction>Explique como funciona a gravidade de forma simples.</instruction>
+<|im_start|>think"""
+
+inputs = tokenizer(prompt, return_tensors="pt").to(model.device)
+
+outputs = model.generate(
+    **inputs,
+    max_new_tokens=512,
+    do_sample=True,
+    temperature=0.7,
+    top_p=0.9,
+    eos_token_id=tokenizer.eos_token_id
+)
+
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)

chat_template.jinja

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+{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<instruction>' + message['content'].strip() + '</instruction>'}}{% elif message['role'] == 'assistant' %}{{ message['content'].strip() + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}

checkpoint-1200/chat_template.jinja

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+{% for message in messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if message['role'] == 'user' %}{{ '<instruction>' + message['content'].strip() + '</instruction>'}}{% elif message['role'] == 'assistant' %}{{ message['content'].strip() + eos_token}}{% else %}{{ raise_exception('Only user and assistant roles are supported!') }}{% endif %}{% endfor %}

checkpoint-1200/config.json

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+  "vocab_size": 32004
+}

checkpoint-1200/generation_config.json

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checkpoint-1250/chat_template.jinja

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checkpoint-1250/config.json

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+}

training_args.bin

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