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"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"provenance": [],
"gpuType": "T4"
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# 🏛️ IVRIUS — Fine Tuning Jurídico Bilíngue\n",
"**Modelo:** Llama-3.1-8B + QLoRA (4-bit)\n",
"**Dataset:** STJ + TJMG + Pares BR↔US + CIJ\n",
"**Tempo estimado:** 1-2h no T4 Colab\n",
"\n",
"### ✅ Checklist antes de rodar:\n",
"- [ ] Runtime → Change runtime type → **T4 GPU**\n",
"- [ ] Conectado ao Colab\n",
"- [ ] Token HuggingFace disponível"
]
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 1: Instalar dependências ──────────────────────────────\n",
"!pip install -q \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
"!pip install -q --no-deps xformers trl peft accelerate bitsandbytes\n",
"!pip install -q datasets huggingface_hub\n",
"print('✅ Dependências instaladas!')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 2: Configuração ────────────────────────────────────────\n",
"HF_TOKEN = 'SEU_HF_TOKEN_AQUI'\n",
"HF_REPO_OUT = 'MMR408/ivrius-llama-juridico-v1' # onde salvar o modelo\n",
"MODEL_BASE = 'unsloth/llama-3.1-8b-bnb-4bit'\n",
"\n",
"import os\n",
"os.environ['HF_TOKEN'] = HF_TOKEN\n",
"\n",
"from huggingface_hub import login\n",
"login(token=HF_TOKEN)\n",
"print('✅ HuggingFace autenticado!')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 3: Carregar modelo base ───────────────────────────────\n",
"from unsloth import FastLanguageModel\n",
"import torch\n",
"\n",
"max_seq_length = 2048\n",
"dtype = None\n",
"load_in_4bit = True\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name=MODEL_BASE,\n",
" max_seq_length=max_seq_length,\n",
" dtype=dtype,\n",
" load_in_4bit=load_in_4bit,\n",
" token=HF_TOKEN,\n",
")\n",
"print('✅ Modelo base carregado!')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 4: Configurar LoRA ─────────────────────────────────────\n",
"model = FastLanguageModel.get_peft_model(\n",
" model,\n",
" r=16,\n",
" target_modules=['q_proj','k_proj','v_proj','o_proj',\n",
" 'gate_proj','up_proj','down_proj'],\n",
" lora_alpha=16,\n",
" lora_dropout=0,\n",
" bias='none',\n",
" use_gradient_checkpointing='unsloth',\n",
" random_state=42,\n",
")\n",
"print('✅ LoRA configurado!')\n",
"model.print_trainable_parameters()"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 5: Preparar datasets ───────────────────────────────────\n",
"from datasets import load_dataset, Dataset, concatenate_datasets\n",
"import json\n",
"\n",
"ALPACA_PROMPT = \"\"\"Abaixo está uma instrução jurídica. Responda de forma precisa e fundamentada.\n",
"\n",
"### Instrução:\n",
"{instruction}\n",
"\n",
"### Resposta:\n",
"{response}\"\"\"\n",
"\n",
"EOS = tokenizer.eos_token\n",
"\n",
"def format_alpaca(examples):\n",
" texts = []\n",
" for inst, resp in zip(examples['instruction'], examples['response']):\n",
" texts.append(ALPACA_PROMPT.format(instruction=inst, response=resp) + EOS)\n",
" return {'text': texts}\n",
"\n",
"# ── Dataset 1: STJ ementas (resumo) ──────────────────────────────\n",
"print('📥 Carregando STJ...')\n",
"stj_raw = load_dataset('celsowm/jurisprudencias_stj', split='train[:2000]', token=HF_TOKEN)\n",
"\n",
"def stj_to_alpaca(row):\n",
" ementa = str(row.get('ementa_texto') or '').strip()[:500]\n",
" acordao = str(row.get('acordao') or '').strip()[:1000]\n",
" if not ementa or len(ementa) < 50:\n",
" return None\n",
" return {\n",
" 'instruction': f'Resuma a seguinte decisão jurídica do STJ ({row.get(\"identificacao\",\"\")}):\\n{acordao[:600] if acordao else ementa}',\n",
" 'response': ementa\n",
" }\n",
"\n",
"stj_items = [stj_to_alpaca(r) for r in stj_raw if stj_to_alpaca(r)]\n",
"ds_stj = Dataset.from_list(stj_items)\n",
"print(f'✅ STJ: {len(ds_stj)} exemplos')\n",
"\n",
"# ── Dataset 2: TJMG ementas ───────────────────────────────────────\n",
"print('📥 Carregando TJMG...')\n",
"tjmg_raw = load_dataset('celsowm/jurisprudencias_tjmg', split='train[:2000]', token=HF_TOKEN)\n",
"\n",
"def tjmg_to_alpaca(row):\n",
" ementa = str(row.get('ementa') or '').strip()[:500]\n",
" if not ementa or len(ementa) < 50:\n",
" return None\n",
" return {\n",
" 'instruction': f'Resuma a seguinte decisão jurídica do TJMG ({row.get(\"numero_processo\",\"\")}):\\n{ementa[:300]}',\n",
" 'response': ementa\n",
" }\n",
"\n",
"tjmg_items = [tjmg_to_alpaca(r) for r in tjmg_raw if tjmg_to_alpaca(r)]\n",
"ds_tjmg = Dataset.from_list(tjmg_items)\n",
"print(f'✅ TJMG: {len(ds_tjmg)} exemplos')\n",
"\n",
"# ── Dataset 3: Pares bilíngues BR↔US (do HuggingFace) ────────────\n",
"print('📥 Carregando pares bilíngues...')\n",
"ds_pairs_raw = load_dataset('MMR408/proejto2', split='train', token=HF_TOKEN)\n",
"\n",
"# Fallback: usar pares hardcoded se não encontrar\n",
"# (você pode subir o JSONL manualmente no Colab também)\n",
"pairs_items = []\n",
"for row in ds_pairs_raw:\n",
" prompt = str(row.get('prompt') or '')\n",
" response = str(row.get('response') or '')\n",
" if prompt and response and len(response) > 100:\n",
" pairs_items.append({'instruction': prompt, 'response': response})\n",
"\n",
"ds_pairs = Dataset.from_list(pairs_items) if pairs_items else Dataset.from_list([])\n",
"print(f'✅ Pares BR↔US: {len(ds_pairs)} exemplos')\n",
"\n",
"# ── Combinar todos ────────────────────────────────────────────────\n",
"datasets_list = [ds for ds in [ds_stj, ds_tjmg, ds_pairs] if len(ds) > 0]\n",
"ds_combined = concatenate_datasets(datasets_list).shuffle(seed=42)\n",
"print(f'\\n✅ Dataset combinado: {len(ds_combined)} exemplos totais')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 6: Tokenizar ───────────────────────────────────────────\n",
"ds_formatted = ds_combined.map(format_alpaca, batched=True,\n",
" remove_columns=ds_combined.column_names)\n",
"print(f'✅ Tokenizado: {len(ds_formatted)} exemplos')\n",
"print(f'\\nAmostra:\\n{ds_formatted[0][\"text\"][:300]}...')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 7: Treinar ─────────────────────────────────────────────\n",
"from trl import SFTTrainer\n",
"from transformers import TrainingArguments\n",
"from unsloth import is_bfloat16_supported\n",
"\n",
"trainer = SFTTrainer(\n",
" model=model,\n",
" tokenizer=tokenizer,\n",
" train_dataset=ds_formatted,\n",
" dataset_text_field='text',\n",
" max_seq_length=max_seq_length,\n",
" dataset_num_proc=2,\n",
" packing=False,\n",
" args=TrainingArguments(\n",
" per_device_train_batch_size=2,\n",
" gradient_accumulation_steps=4,\n",
" warmup_steps=10,\n",
" max_steps=120, # ~1.5h no T4 — aumente para 300 se quiser mais qualidade\n",
" learning_rate=2e-4,\n",
" fp16=not is_bfloat16_supported(),\n",
" bf16=is_bfloat16_supported(),\n",
" logging_steps=10,\n",
" optim='adamw_8bit',\n",
" weight_decay=0.01,\n",
" lr_scheduler_type='linear',\n",
" seed=42,\n",
" output_dir='ivrius_output',\n",
" report_to='none',\n",
" ),\n",
")\n",
"\n",
"print('🚀 Iniciando treino...')\n",
"trainer_stats = trainer.train()\n",
"print(f'\\n✅ Treino concluído!')\n",
"print(f' Loss final: {trainer_stats.training_loss:.4f}')\n",
"print(f' Tempo: {trainer_stats.metrics[\"train_runtime\"]/60:.1f} minutos')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 8: Testar o modelo ─────────────────────────────────────\n",
"FastLanguageModel.for_inference(model)\n",
"\n",
"testes = [\n",
" 'O que é dano moral no direito brasileiro? Compare com o conceito americano.',\n",
" 'Explique o habeas corpus no Brasil e seu equivalente nos EUA.',\n",
" 'Qual a diferença entre prescrição e decadência no direito civil brasileiro?',\n",
"]\n",
"\n",
"for pergunta in testes:\n",
" prompt = ALPACA_PROMPT.format(instruction=pergunta, response='') \n",
" inputs = tokenizer([prompt], return_tensors='pt').to('cuda')\n",
" outputs = model.generate(**inputs, max_new_tokens=300,\n",
" temperature=0.3, do_sample=True,\n",
" pad_token_id=tokenizer.eos_token_id)\n",
" resposta = tokenizer.batch_decode(outputs)[0]\n",
" resposta_limpa = resposta.split('### Resposta:')[-1].replace(tokenizer.eos_token,'').strip()\n",
" print(f'\\n❓ {pergunta}')\n",
" print(f'💬 {resposta_limpa[:400]}')\n",
" print('─'*60)"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 9: Salvar LoRA no HuggingFace ─────────────────────────\n",
"print(f'💾 Salvando LoRA em {HF_REPO_OUT}...')\n",
"model.save_pretrained('ivrius_lora')\n",
"tokenizer.save_pretrained('ivrius_lora')\n",
"\n",
"model.push_to_hub(HF_REPO_OUT, token=HF_TOKEN)\n",
"tokenizer.push_to_hub(HF_REPO_OUT, token=HF_TOKEN)\n",
"print(f'\\n✅ Modelo salvo em: https://huggingface.co/{HF_REPO_OUT}')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {},
"source": [
"# ── Célula 10 (opcional): Merge + GGUF para LM Studio ────────────\n",
"# Descomente se quiser rodar localmente no LM Studio\n",
"\n",
"# print('🔧 Fazendo merge do modelo...')\n",
"# model.save_pretrained_merged('ivrius_merged', tokenizer, save_method='merged_16bit')\n",
"# model.push_to_hub_merged(HF_REPO_OUT + '-merged', tokenizer,\n",
"# save_method='merged_16bit', token=HF_TOKEN)\n",
"\n",
"# # Converter para GGUF (para LM Studio / Ollama)\n",
"# model.save_pretrained_gguf('ivrius_gguf', tokenizer, quantization_method='q4_k_m')\n",
"# model.push_to_hub_gguf(HF_REPO_OUT + '-gguf', tokenizer,\n",
"# quantization_method='q4_k_m', token=HF_TOKEN)\n",
"# print('✅ GGUF salvo!')\n",
"\n",
"print('Célula 10 pronta — descomente para gerar GGUF')"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## ✅ Concluído!\n",
"\n",
"Seu modelo IVRIUS está treinado e salvo no HuggingFace.\n",
"\n",
"**Próximos passos:**\n",
"1. Descomentar célula 10 para gerar GGUF (LM Studio / Ollama)\n",
"2. Integrar no Lambda AWS via Bedrock Custom Model\n",
"3. Adicionar disclaimer OAB nas respostas\n",
"4. Integrar RAG com Qdrant + voyage-law-2\n",
"\n",
"**Modelo salvo em:** `MMR408/ivrius-llama-juridico-v1`\n",
"\n",
"---\n",
"_IVRIUS — Intelligent Virtual Research and Information System_ \n",
"_Dra. Miriam Mesquita — OAB_"
]
}
]
}
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