Initial release of Auto-FineTune-Ops

.gitignore

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+# Python
+__pycache__/
+*.py[cod]
+*$py.class
+*.so
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+
+# Virtual Environment
+venv/
+env/
+.env
+.venv/
+
+# Environment Variables
+.env
+.env.local
+.env.development.local
+.env.test.local
+.env.production.local
+
+# IDE
+.idea/
+.vscode/
+*.swp
+*.swo
+
+# Project Output
+output/
+logs/
+reports/
+models/
+processed_data/
+
+# OS
+.DS_Store
+Thumbs.db

.streamlit/config.toml

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+[theme]
+primaryColor = "#6366f1"
+backgroundColor = "#0f0f23"
+secondaryBackgroundColor = "#1a1a2e"
+textColor = "#e2e8f0"
+font = "sans serif"
+
+[server]
+headless = true
+enableCORS = false
+enableXsrfProtection = true

Auto_FineTune_Ops_Colab.ipynb

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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# 🤖 Auto-FineTune-Ops: One-Click Fine-Tuning Pipeline\n",
+    "\n",
+    "**Run this notebook on Google Colab (with GPU) or Kaggle to fine-tune your LLM!**\n",
+    "\n",
+    "This notebook combines all the agents:\n",
+    "- **DataArchitectAgent**: Cleans and formats your data\n",
+    "- **TrainingPilot**: Fine-tunes with Unsloth (ultra-fast LoRA)\n",
+    "- **TheJudge**: Evaluates base vs fine-tuned with LLM-as-Judge\n",
+    "\n",
+    "---"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 1️⃣ Setup - Install Dependencies"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%%capture\n",
+    "# Install Unsloth (must be first!)\n",
+    "!pip install \"unsloth[colab-new] @ git+https://github.com/unslothai/unsloth.git\"\n",
+    "!pip install --no-deps trl peft accelerate bitsandbytes\n",
+    "\n",
+    "# Install other dependencies\n",
+    "!pip install datasets transformers rich pandas openai anthropic"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2️⃣ Configuration\n",
+    "\n",
+    "Set your training goal and upload your data!"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#@title ⚙️ Configuration\n",
+    "#@markdown ### Training Settings\n",
+    "GOAL = \"medical_assistant\"  #@param {type:\"string\"}\n",
+    "BASE_MODEL = \"unsloth/llama-3-8b-bnb-4bit\"  #@param [\"unsloth/llama-3-8b-bnb-4bit\", \"unsloth/mistral-7b-bnb-4bit\", \"unsloth/gemma-7b-bnb-4bit\"]\n",
+    "MAX_SEQ_LENGTH = 2048  #@param {type:\"integer\"}\n",
+    "\n",
+    "#@markdown ### Evaluation Settings\n",
+    "RUN_EVALUATION = True  #@param {type:\"boolean\"}\n",
+    "JUDGE_MODEL = \"gpt-4o\"  #@param [\"gpt-4o\", \"claude-3-5-sonnet-20241022\"]\n",
+    "NUM_EVAL_SAMPLES = 20  #@param {type:\"integer\"}\n",
+    "\n",
+    "#@markdown ### API Keys (for evaluation only)\n",
+    "OPENAI_API_KEY = \"\"  #@param {type:\"string\"}\n",
+    "ANTHROPIC_API_KEY = \"\"  #@param {type:\"string\"}\n",
+    "\n",
+    "import os\n",
+    "if OPENAI_API_KEY:\n",
+    "    os.environ[\"OPENAI_API_KEY\"] = OPENAI_API_KEY\n",
+    "if ANTHROPIC_API_KEY:\n",
+    "    os.environ[\"ANTHROPIC_API_KEY\"] = ANTHROPIC_API_KEY\n",
+    "\n",
+    "print(f\"✅ Goal: {GOAL}\")\n",
+    "print(f\"✅ Base Model: {BASE_MODEL}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3️⃣ Upload Your Data\n",
+    "\n",
+    "Upload a CSV or JSON file with instruction-response pairs."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from google.colab import files\n",
+    "import pandas as pd\n",
+    "\n",
+    "print(\"📂 Upload your dataset (CSV or JSON):\")\n",
+    "uploaded = files.upload()\n",
+    "\n",
+    "# Get the uploaded file name\n",
+    "DATA_FILE = list(uploaded.keys())[0]\n",
+    "print(f\"\\n✅ Uploaded: {DATA_FILE}\")\n",
+    "\n",
+    "# Preview the data\n",
+    "if DATA_FILE.endswith('.csv'):\n",
+    "    df = pd.read_csv(DATA_FILE)\n",
+    "else:\n",
+    "    df = pd.read_json(DATA_FILE, lines=DATA_FILE.endswith('.jsonl'))\n",
+    "\n",
+    "print(f\"\\n📊 Dataset shape: {df.shape}\")\n",
+    "print(f\"📋 Columns: {list(df.columns)}\")\n",
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4️⃣ Stage 1: Data Preparation (DataArchitectAgent)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import json\n",
+    "import re\n",
+    "from dataclasses import dataclass, field\n",
+    "from typing import Optional, List, Dict, Tuple\n",
+    "import pandas as pd\n",
+    "from rich.console import Console\n",
+    "from rich.table import Table\n",
+    "\n",
+    "console = Console()\n",
+    "\n",
+    "@dataclass\n",
+    "class CleaningConfig:\n",
+    "    min_instruction_length: int = 10\n",
+    "    max_instruction_length: int = 2048\n",
+    "    min_response_length: int = 20\n",
+    "    max_response_length: int = 4096\n",
+    "    remove_duplicates: bool = True\n",
+    "\n",
+    "class DataArchitectAgent:\n",
+    "    \"\"\"Autonomous data preparation agent.\"\"\"\n",
+    "    \n",
+    "    INSTRUCTION_PATTERNS = [r'instruction', r'prompt', r'question', r'query', r'user', r'input_text']\n",
+    "    OUTPUT_PATTERNS = [r'output', r'response', r'answer', r'completion', r'assistant', r'target']\n",
+    "    \n",
+    "    def __init__(self, config=None):\n",
+    "        self.config = config or CleaningConfig()\n",
+    "    \n",
+    "    def _detect_columns(self, df):\n",
+    "        instruction_col, output_col = None, None\n",
+    "        for col in df.columns:\n",
+    "            col_lower = col.lower()\n",
+    "            for pattern in self.INSTRUCTION_PATTERNS:\n",
+    "                if re.search(pattern, col_lower) and not instruction_col:\n",
+    "                    instruction_col = col\n",
+    "            for pattern in self.OUTPUT_PATTERNS:\n",
+    "                if re.search(pattern, col_lower) and not output_col:\n",
+    "                    output_col = col\n",
+    "        return instruction_col, output_col\n",
+    "    \n",
+    "    def process(self, df, goal):\n",
+    "        console.print(\"[bold blue]🏗️ DATA ARCHITECT AGENT[/]\")\n",
+    "        \n",
+    "        # Detect columns\n",
+    "        inst_col, out_col = self._detect_columns(df)\n",
+    "        console.print(f\"📌 Detected: instruction='{inst_col}', output='{out_col}'\")\n",
+    "        \n",
+    "        if not inst_col or not out_col:\n",
+    "            raise ValueError(\"Could not auto-detect columns. Please rename to 'instruction' and 'output'.\")\n",
+    "        \n",
+    "        # Clean\n",
+    "        df_clean = df.dropna(subset=[inst_col, out_col])\n",
+    "        if self.config.remove_duplicates:\n",
+    "            df_clean = df_clean.drop_duplicates(subset=[inst_col])\n",
+    "        \n",
+    "        # Length filters\n",
+    "        df_clean = df_clean[\n",
+    "            (df_clean[inst_col].str.len() >= self.config.min_instruction_length) &\n",
+    "            (df_clean[inst_col].str.len() <= self.config.max_instruction_length) &\n",
+    "            (df_clean[out_col].str.len() >= self.config.min_response_length) &\n",
+    "            (df_clean[out_col].str.len() <= self.config.max_response_length)\n",
+    "        ]\n",
+    "        \n",
+    "        console.print(f\"✅ Cleaned: {len(df_clean)} rows (from {len(df)})\")\n",
+    "        \n",
+    "        # Format for training\n",
+    "        system_prompt = f\"You are a specialized AI assistant for {goal}.\"\n",
+    "        \n",
+    "        formatted = []\n",
+    "        for _, row in df_clean.iterrows():\n",
+    "            formatted.append({\n",
+    "                \"instruction\": str(row[inst_col]),\n",
+    "                \"input\": \"\",\n",
+    "                \"output\": str(row[out_col]),\n",
+    "                \"system\": system_prompt\n",
+    "            })\n",
+    "        \n",
+    "        # Save\n",
+    "        output_path = f\"/content/{goal}_training.jsonl\"\n",
+    "        with open(output_path, 'w') as f:\n",
+    "            for item in formatted:\n",
+    "                f.write(json.dumps(item) + '\\n')\n",
+    "        \n",
+    "        console.print(f\"💾 Saved to: {output_path}\")\n",
+    "        return output_path, len(formatted)\n",
+    "\n",
+    "# Run Data Agent\n",
+    "data_agent = DataArchitectAgent()\n",
+    "TRAINING_DATA_PATH, DATASET_SIZE = data_agent.process(df, GOAL)\n",
+    "print(f\"\\n✅ Dataset ready: {DATASET_SIZE} samples\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5️⃣ Stage 2: Fine-Tuning (TrainingPilot)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from unsloth import FastLanguageModel\n",
+    "from datasets import load_dataset\n",
+    "from trl import SFTTrainer\n",
+    "from transformers import TrainingArguments\n",
+    "import torch\n",
+    "\n",
+    "print(f\"🚀 GPU: {torch.cuda.get_device_name(0)}\")\n",
+    "print(f\"📊 VRAM: {torch.cuda.get_device_properties(0).total_memory / 1e9:.1f} GB\")\n",
+    "\n",
+    "# Auto-configure hyperparameters based on dataset size\n",
+    "if DATASET_SIZE < 1000:\n",
+    "    LORA_RANK, LORA_ALPHA, LR, EPOCHS = 8, 16, 2e-4, 5\n",
+    "elif DATASET_SIZE < 10000:\n",
+    "    LORA_RANK, LORA_ALPHA, LR, EPOCHS = 16, 32, 1e-4, 3\n",
+    "else:\n",
+    "    LORA_RANK, LORA_ALPHA, LR, EPOCHS = 32, 64, 5e-5, 2\n",
+    "\n",
+    "print(f\"\\n⚙️ Auto-configured for {DATASET_SIZE} samples:\")\n",
+    "print(f\"   LoRA Rank: {LORA_RANK}, Alpha: {LORA_ALPHA}\")\n",
+    "print(f\"   Learning Rate: {LR}, Epochs: {EPOCHS}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load model with Unsloth\n",
+    "model, tokenizer = FastLanguageModel.from_pretrained(\n",
+    "    model_name=BASE_MODEL,\n",
+    "    max_seq_length=MAX_SEQ_LENGTH,\n",
+    "    dtype=None,\n",
+    "    load_in_4bit=True,\n",
+    ")\n",
+    "\n",
+    "# Apply LoRA\n",
+    "model = FastLanguageModel.get_peft_model(\n",
+    "    model,\n",
+    "    r=LORA_RANK,\n",
+    "    lora_alpha=LORA_ALPHA,\n",
+    "    lora_dropout=0,\n",
+    "    target_modules=[\"q_proj\", \"k_proj\", \"v_proj\", \"o_proj\", \"gate_proj\", \"up_proj\", \"down_proj\"],\n",
+    "    bias=\"none\",\n",
+    "    use_gradient_checkpointing=\"unsloth\",\n",
+    "    random_state=42,\n",
+    ")\n",
+    "\n",
+    "print(\"✅ Model loaded with LoRA!\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Load dataset\n",
+    "dataset = load_dataset('json', data_files=TRAINING_DATA_PATH, split='train')\n",
+    "\n",
+    "# Format prompts\n",
+    "alpaca_template = \"\"\"Below is an instruction that describes a task. Write a response that appropriately completes the request.\n",
+    "\n",
+    "### Instruction:\n",
+    "{instruction}\n",
+    "\n",
+    "### Response:\n",
+    "{output}\"\"\"\n",
+    "\n",
+    "def format_prompt(example):\n",
+    "    return {\"text\": alpaca_template.format(**example)}\n",
+    "\n",
+    "dataset = dataset.map(format_prompt)\n",
+    "print(f\"✅ Loaded {len(dataset)} training samples\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Train!\n",
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    tokenizer=tokenizer,\n",
+    "    train_dataset=dataset,\n",
+    "    dataset_text_field=\"text\",\n",
+    "    max_seq_length=MAX_SEQ_LENGTH,\n",
+    "    args=TrainingArguments(\n",
+    "        output_dir=f\"/content/{GOAL}_model\",\n",
+    "        num_train_epochs=EPOCHS,\n",
+    "        per_device_train_batch_size=4,\n",
+    "        gradient_accumulation_steps=4,\n",
+    "        learning_rate=LR,\n",
+    "        warmup_ratio=0.03,\n",
+    "        fp16=True,\n",
+    "        logging_steps=10,\n",
+    "        save_strategy=\"epoch\",\n",
+    "        optim=\"adamw_8bit\",\n",
+    "        seed=42,\n",
+    "    ),\n",
+    ")\n",
+    "\n",
+    "print(\"🏋️ Training started...\")\n",
+    "trainer.train()\n",
+    "\n",
+    "# Save\n",
+    "MODEL_PATH = f\"/content/{GOAL}_model_final\"\n",
+    "trainer.save_model(MODEL_PATH)\n",
+    "tokenizer.save_pretrained(MODEL_PATH)\n",
+    "print(f\"\\n✅ Model saved to: {MODEL_PATH}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 6️⃣ Stage 3: Evaluation (TheJudge) - Optional"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "if RUN_EVALUATION and (OPENAI_API_KEY or ANTHROPIC_API_KEY):\n",
+    "    print(\"⚖️ Running Model Arena evaluation...\")\n",
+    "    \n",
+    "    # Simple evaluation - compare responses\n",
+    "    FastLanguageModel.for_inference(model)\n",
+    "    \n",
+    "    # Sample prompts from dataset\n",
+    "    test_prompts = [dataset[i][\"instruction\"] for i in range(min(NUM_EVAL_SAMPLES, len(dataset)))]\n",
+    "    \n",
+    "    print(f\"\\n📊 Evaluating on {len(test_prompts)} samples...\")\n",
+    "    print(\"Note: Full arena evaluation requires loading base model separately.\")\n",
+    "    print(\"For complete evaluation, use the full TheJudge agent locally.\")\n",
+    "    \n",
+    "    # Quick test generation\n",
+    "    test_prompt = test_prompts[0] if test_prompts else \"Hello, how are you?\"\n",
+    "    inputs = tokenizer(f\"### Instruction:\\n{test_prompt}\\n\\n### Response:\\n\", return_tensors=\"pt\").to(\"cuda\")\n",
+    "    outputs = model.generate(**inputs, max_new_tokens=128, temperature=0.7)\n",
+    "    response = tokenizer.decode(outputs[0], skip_special_tokens=True)\n",
+    "    \n",
+    "    print(f\"\\n📝 Sample generation:\")\n",
+    "    print(f\"Prompt: {test_prompt[:100]}...\")\n",
+    "    print(f\"Response: {response.split('### Response:')[-1][:200]}...\")\n",
+    "else:\n",
+    "    print(\"⏭️ Skipping evaluation (no API key or disabled)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 7️⃣ Download Your Model"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 1: Save to Google Drive\n",
+    "from google.colab import drive\n",
+    "drive.mount('/content/drive')\n",
+    "\n",
+    "!cp -r {MODEL_PATH} /content/drive/MyDrive/{GOAL}_finetuned_model\n",
+    "print(f\"✅ Model copied to Google Drive: /MyDrive/{GOAL}_finetuned_model\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Option 2: Push to HuggingFace Hub\n",
+    "# Uncomment and fill in your details:\n",
+    "\n",
+    "# from huggingface_hub import login\n",
+    "# login(token=\"YOUR_HF_TOKEN\")\n",
+    "# \n",
+    "# model.push_to_hub(\"your-username/your-model-name\")\n",
+    "# tokenizer.push_to_hub(\"your-username/your-model-name\")\n",
+    "# print(\"✅ Pushed to HuggingFace Hub!\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 🎉 Done!\n",
+    "\n",
+    "Your fine-tuned model is ready! You can:\n",
+    "1. Download from Google Drive\n",
+    "2. Push to HuggingFace Hub\n",
+    "3. Use the FastAPI deployment script locally"
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "gpuType": "T4",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "venv",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "name": "python",
+   "version": "3.13.1"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

PROJECT_HIGHLIGHTS.md

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+# 🚀 Auto-FineTune-Ops: Project Highlights
+
+**Autonomous Machine Learning Pipeline for Production-Grade LLM Fine-Tuning**
+
+Auto-FineTune-Ops is a comprehensive, no-code/low-code platform that democratizes access to state-of-the-art LLM fine-tuning. It automates the complex lifecycle of data preparation, training, evaluation, and deployment.
+
+---
+
+## 🌟 Key Features
+
+### 1. 🧠 Intelligent Preprocessing Engine
+A modular, production-ready data pipeline with 10+ specialized modules:
+- **Text Cleaning:** Auto-strip HTML, emojis, URLs, and normalize whitespace.
+- **PII Redaction:** Detect and mask emails, phone numbers, and keys for security.
+- **Deduplication:** Remove exact and semantic duplicates (using TF-IDF/Cosine Similarity).
+- **Quality Filtering:** Filter by language, toxicity, and length constraints.
+- **Advanced Formatting:** Auto-convert loose CSV/JSON into strict Chat Templates (ShareGPT/OpenAI).
+
+### 2. ⚡ Hybrid Training Ecosystem
+Flexible training workflows designed for all hardware setups:
+- **Local GPU Power:** Leverages **Unsloth** for 2x faster training and 70% less memory usage (4-bit quantization).
+- **Google Colab Bridge:** Seamless "No-GPU" fallback flow. Generate a ready-to-run Colab notebook to train on free cloud GPUs if local hardware is insufficient.
+- **Custom Model Support:** Fine-tune any HuggingFace model (Llama 3, Mistral, Gemma, Phi-3, etc.).
+
+### 3. ⚖️ Multi-Provider AI Judge Arena
+Production-grade model evaluation using LLM-as-a-Judge:
+- **Provider Agnostic:** Supports OpenAI (GPT-4o), Anthropic (Claude 3.5), Google (Gemini 1.5), and Groq (Llama 3).
+- **Custom Endpoints:** Connect to local LLMs (Ollama/vLLM) as judges.
+- **Comprehensive Metrics:** Automated scoring for Accuracy, Helpfulness, Clarity, and Tone.
+- **Head-to-Head:** Win-rate visualization comparing Base Model vs. Fine-Tuned Model.
+
+### 4. 🖥️ Interactive Streamlit Dashboard
+A premium, dark-mode UI that abstracts away CLI complexity:
+- **Project Management:** Manage datasets, models, and logs visually.
+- **Real-time Monitoring:** Track training loss and progress live.
+- **Visualization:** Interactive Plotly charts for evaluation results.
+
+### 5. 🚀 One-Click Deployment
+- **Instant API:** Export trained models as a production-ready **FastAPI** microservice.
+- **Standardized Interface:** OpenAI-compatible `/generate` endpoints for easy integration into apps.
+
+---
+
+## 🔧 Technical Stack
+- **Frontend:** Streamlit, Plotly
+- **Core ML:** PyTorch, Transformers, PEFT, Unsloth, TRL
+- **Data:** Pandas, NumPy, Scikit-learn
+- **API:** FastAPI, Uvicorn
+- **LLM Clients:** OpenAI SDK, Anthropic SDK
+
+## 🛡️ Production Readiness
+- **Modular Architecture:** Agent-based design (DataArchitect, TrainingPilot, TheJudge) allows easy extensibility.
+- **Error Handling:** Robust fallback mechanisms and detailed logging.
+- **Security:** PII masking and API key management best practices.

README.md

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+# 🤖 Auto-FineTune-Ops
+
+> **Autonomous End-to-End LLM Fine-Tuning Pipeline**
+>
+> From raw data to production API in one click. No ML expertise required.
+
+[![Python 3.10+](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://www.python.org/)
+[![Streamlit](https://img.shields.io/badge/Streamlit-1.32+-red.svg)](https://streamlit.io/)
+[![License: MIT](https://img.shields.io/badge/License-MIT-green.svg)](LICENSE)
+
+---
+
+## 🎯 What Is This?
+
+Auto-FineTune-Ops is a **no-code/low-code platform** that automates the entire lifecycle of fine-tuning Large Language Models (LLMs). It handles:
+
+1.  **Data Ingestion:** Upload CSV, JSON, or JSONL files.
+2.  **Advanced Preprocessing:** 10+ modules for cleaning, PII redaction, deduplication, and formatting.
+3.  **Hybrid Training:** Train locally on GPU (Unsloth/LoRA) or generate a **Google Colab Notebook** for free cloud GPU training.
+4.  **AI Judge Evaluation:** Compare your fine-tuned model against the base model using GPT-4, Claude 3.5, Gemini, or Groq as a judge.
+5.  **One-Click Deployment:** Export your trained model as a production-ready FastAPI endpoint.
+
+**All accessible via a premium, easy-to-use Streamlit Dashboard.**
+
+---
+
+## ✨ Key Features
+
+### 🧠 Intelligent Preprocessing
+- **Text Cleaning:** Remove HTML, URLs, emojis, normalize whitespace.
+- **PII Filter:** Redact emails, phone numbers, API keys.
+- **Deduplication:** Remove exact and semantic (TF-IDF) duplicates.
+- **Quality Filters:** Filter by length, language, toxicity.
+- **Balancing:** Oversample/undersample classes for classification tasks.
+- **Export Formats:** Auto-convert to OpenAI Chat, Completion, or Classification JSONL formats.
+
+### ⚡ Flexible Training Workflows
+- **Local GPU:** Uses **Unsloth** for ultra-fast 4-bit LoRA fine-tuning (2x faster, 70% less memory).
+- **Google Colab Fallback:** Don't have a GPU? The app generates a ready-to-run Colab notebook for you. Download models back to the app for evaluation.
+- **Custom Models:** Fine-tune any HuggingFace model (Llama 3, Mistral, Gemma, Phi-3, etc.).
+
+### ⚖️ Multi-Provider AI Judge
+Evaluate models head-to-head using:
+- **OpenAI** (GPT-4o, GPT-4-turbo)
+- **Anthropic** (Claude 3.5 Sonnet, Opus)
+- **Google** (Gemini 1.5 Pro)
+- **Groq** (Llama 3, Mixtral)
+- **Custom Endpoints** (Ollama, vLLM)
+
+---
+
+## 🚀 Quick Start
+
+### 1. Installation
+
+```bash
+# Clone the repository
+git clone https://github.com/your-username/Auto-FineTune-Ops.git
+cd Auto-FineTune-Ops
+
+# Create a virtual environment
+python -m venv venv
+# Windows:
+.\venv\Scripts\activate
+# Mac/Linux:
+source venv/bin/activate
+
+# Install dependencies
+pip install -r requirements.txt
+```
+
+### 2. Launch the Dashboard
+
+```bash
+streamlit run app.py
+```
+
+Open your browser to the URL shown (usually `http://localhost:8501`).
+
+---
+
+## 🛠️ Workflow Guide
+
+### Step 1: Data Upload
+- Upload your raw `CSV` or `JSON` file containing instruction-response pairs.
+- The app automatically detects columns like `instruction`, `input`, `output`.
+- Preview full dataset with pagination.
+
+### Step 2: Preprocessing
+- Configure cleaning rules (HTML removal, lowercase, etc.).
+- Set PII filters (mask emails/phones).
+- Enable semantic deduplication.
+- Click **Run Pipeline** to clean and format your data.
+
+### Step 3: Training
+- **If you have a GPU:** Select a base model (e.g., Llama-3-8b) and click **Start Training**.
+- **If you have no GPU:**
+    1.  Download the preprocessed data.
+    2.  Download the generated `Colab Notebook`.
+    3.  Run training on Google Colab (Free Tier).
+    4.  Upload the fine-tuned model results back to the app.
+
+### Step 4: Evaluation
+- Compare your fine-tuned model vs. the base model.
+- Select an AI Judge (e.g., GPT-4o).
+- Visualize win rates and quality scores (Accuracy, Helpfulness, Tone).
+
+### Step 5: Deployment
+- Deploy your model locally as a REST API:
+  ```bash
+  python scripts/deploy.py --model ./output/models/your_model --port 8000
+  ```
+- Or push to HuggingFace Hub directly from the dashboard.
+
+---
+
+## 🏗️ Project Structure
+
+```
+ml_oops/
+├── app.py                     # 🚀 Main Streamlit Dashboard
+├── main.py                    # 🧠 CLI Orchestrator (Headless mode)
+├── requirements.txt           # Dependencies
+├── agents/                    # Core Logic Agents
+│   ├── data_architect.py      # Data Analysis & Cleaning
+│   ├── training_pilot.py      # Fine-Tuning Logic
+│   └── the_judge.py           # Evaluation Logic
+├── preprocessing/             # Advanced Preprocessing Modules
+│   ├── text_cleaning.py       # Regex & Normalization
+│   ├── pii_filter.py          # PII Redaction
+│   ├── deduplication.py       # Semantic Dedupe
+│   └── ...
+├── configs/                   # Configuration Files
+└── output/                    # Artifacts (Models, Logs, Reports)
+```
+
+---
+
+## 🤝 Contributing
+
+Contributions are welcome! Please read `CONTRIBUTING.md` for details on our code of conduct and the process for submitting pull requests.
+
+## 📜 License
+
+This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.
+
+---
+
+<div align="center">
+  <b>Built for modern ML teams.</b><br>
+  <i>Replace weeks of manual engineering with minutes of automated ops.</i>
+</div>

agents/__init__.py

@@ -0,0 +1,7 @@
+"""Auto-FineTune-Ops Agents Package"""
+
+from .data_architect import DataArchitectAgent
+from .training_pilot import TrainingPilot
+from .the_judge import TheJudge
+
+__all__ = ["DataArchitectAgent", "TrainingPilot", "TheJudge"]

agents/data_architect.py

@@ -0,0 +1,505 @@
+"""
+DataArchitectAgent - Autonomous Data Preparation Agent
+=======================================================
+Takes raw CSV/JSON datasets and transforms them into high-quality
+HuggingFace-ready JSONL format for fine-tuning.
+"""
+
+import json
+import re
+from pathlib import Path
+from dataclasses import dataclass, field
+from typing import Optional, List, Dict, Any, Tuple
+import pandas as pd
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn
+from rich.table import Table
+
+console = Console()
+
+
+@dataclass
+class DatasetAnalysis:
+    """Analysis results for a dataset."""
+    total_rows: int
+    valid_rows: int
+    invalid_rows: int
+    duplicate_rows: int
+    detected_columns: Dict[str, str]  # column_name -> detected_type
+    instruction_column: Optional[str] = None
+    input_column: Optional[str] = None
+    output_column: Optional[str] = None
+    quality_score: float = 0.0
+    issues: List[str] = field(default_factory=list)
+
+
+@dataclass
+class CleaningConfig:
+    """Configuration for data cleaning."""
+    min_instruction_length: int = 10
+    max_instruction_length: int = 2048
+    min_response_length: int = 20
+    max_response_length: int = 4096
+    remove_duplicates: bool = True
+    remove_empty: bool = True
+    remove_special_chars: bool = False
+    quality_threshold: float = 0.7
+
+
+class DataArchitectAgent:
+    """
+    Autonomous agent for data preparation and cleaning.
+    
+    This agent analyzes raw datasets, identifies instruction-response pairs,
+    cleans the data, and formats it for HuggingFace fine-tuning.
+    """
+    
+    # Common column name patterns for auto-detection
+    INSTRUCTION_PATTERNS = [
+        r'instruction', r'prompt', r'question', r'query', r'input_text',
+        r'human', r'user', r'request', r'ask', r'command'
+    ]
+    INPUT_PATTERNS = [
+        r'context', r'input', r'background', r'reference', r'document'
+    ]
+    OUTPUT_PATTERNS = [
+        r'output', r'response', r'answer', r'completion', r'reply',
+        r'assistant', r'bot', r'generated', r'target'
+    ]
+    
+    def __init__(self, config: Optional[CleaningConfig] = None):
+        """Initialize the DataArchitectAgent."""
+        self.config = config or CleaningConfig()
+        self.analysis: Optional[DatasetAnalysis] = None
+        
+    def load_dataset(self, path: str) -> pd.DataFrame:
+        """
+        Load a dataset from CSV or JSON file.
+        
+        Args:
+            path: Path to the dataset file
+            
+        Returns:
+            Loaded DataFrame
+        """
+        path = Path(path)
+        
+        if not path.exists():
+            raise FileNotFoundError(f"Dataset not found: {path}")
+        
+        console.print(f"[bold blue]📂 Loading dataset:[/] {path}")
+        
+        if path.suffix.lower() == '.csv':
+            df = pd.read_csv(path)
+        elif path.suffix.lower() in ['.json', '.jsonl']:
+            if path.suffix.lower() == '.jsonl':
+                df = pd.read_json(path, lines=True)
+            else:
+                df = pd.read_json(path)
+        else:
+            raise ValueError(f"Unsupported file format: {path.suffix}")
+        
+        console.print(f"[green]✓ Loaded {len(df)} rows with {len(df.columns)} columns[/]")
+        return df
+    
+    def _match_column_pattern(self, column: str, patterns: List[str]) -> bool:
+        """Check if a column name matches any of the given patterns."""
+        column_lower = column.lower()
+        for pattern in patterns:
+            if re.search(pattern, column_lower):
+                return True
+        return False
+    
+    def _detect_column_type(self, column: str) -> str:
+        """Detect the type of a column based on its name."""
+        if self._match_column_pattern(column, self.INSTRUCTION_PATTERNS):
+            return 'instruction'
+        elif self._match_column_pattern(column, self.INPUT_PATTERNS):
+            return 'input'
+        elif self._match_column_pattern(column, self.OUTPUT_PATTERNS):
+            return 'output'
+        return 'unknown'
+    
+    def analyze_dataset(self, df: pd.DataFrame) -> DatasetAnalysis:
+        """
+        Analyze a dataset to understand its structure and quality.
+        
+        Args:
+            df: Input DataFrame
+            
+        Returns:
+            DatasetAnalysis with detected columns and quality metrics
+        """
+        console.print("\n[bold blue]🔍 Analyzing dataset structure...[/]")
+        
+        # Detect column types
+        detected_columns = {}
+        instruction_col = None
+        input_col = None
+        output_col = None
+        
+        for col in df.columns:
+            col_type = self._detect_column_type(col)
+            detected_columns[col] = col_type
+            
+            if col_type == 'instruction' and instruction_col is None:
+                instruction_col = col
+            elif col_type == 'input' and input_col is None:
+                input_col = col
+            elif col_type == 'output' and output_col is None:
+                output_col = col
+        
+        # Count issues
+        issues = []
+        valid_rows = 0
+        invalid_rows = 0
+        
+        # Check for required columns
+        if instruction_col is None:
+            issues.append("❌ No instruction/prompt column detected")
+        if output_col is None:
+            issues.append("❌ No output/response column detected")
+        
+        # Analyze row validity
+        for _, row in df.iterrows():
+            is_valid = True
+            
+            if instruction_col:
+                inst_val = str(row.get(instruction_col, ''))
+                if len(inst_val) < self.config.min_instruction_length:
+                    is_valid = False
+                elif len(inst_val) > self.config.max_instruction_length:
+                    is_valid = False
+            else:
+                is_valid = False
+            
+            if output_col:
+                out_val = str(row.get(output_col, ''))
+                if len(out_val) < self.config.min_response_length:
+                    is_valid = False
+                elif len(out_val) > self.config.max_response_length:
+                    is_valid = False
+            else:
+                is_valid = False
+            
+            if is_valid:
+                valid_rows += 1
+            else:
+                invalid_rows += 1
+        
+        # Count duplicates
+        duplicate_rows = 0
+        if instruction_col:
+            duplicate_rows = df[instruction_col].duplicated().sum()
+        
+        # Calculate quality score
+        quality_score = valid_rows / len(df) if len(df) > 0 else 0.0
+        
+        self.analysis = DatasetAnalysis(
+            total_rows=len(df),
+            valid_rows=valid_rows,
+            invalid_rows=invalid_rows,
+            duplicate_rows=duplicate_rows,
+            detected_columns=detected_columns,
+            instruction_column=instruction_col,
+            input_column=input_col,
+            output_column=output_col,
+            quality_score=quality_score,
+            issues=issues
+        )
+        
+        # Display analysis results
+        self._display_analysis()
+        
+        return self.analysis
+    
+    def _display_analysis(self):
+        """Display the analysis results in a formatted table."""
+        if not self.analysis:
+            return
+        
+        table = Table(title="Dataset Analysis", show_header=True)
+        table.add_column("Metric", style="cyan")
+        table.add_column("Value", style="green")
+        
+        table.add_row("Total Rows", str(self.analysis.total_rows))
+        table.add_row("Valid Rows", str(self.analysis.valid_rows))
+        table.add_row("Invalid Rows", str(self.analysis.invalid_rows))
+        table.add_row("Duplicate Rows", str(self.analysis.duplicate_rows))
+        table.add_row("Quality Score", f"{self.analysis.quality_score:.2%}")
+        
+        console.print(table)
+        
+        # Show detected columns
+        console.print("\n[bold]Detected Column Mappings:[/]")
+        console.print(f"  • Instruction: [cyan]{self.analysis.instruction_column or 'Not detected'}[/]")
+        console.print(f"  • Input/Context: [cyan]{self.analysis.input_column or 'Not detected'}[/]")
+        console.print(f"  • Output/Response: [cyan]{self.analysis.output_column or 'Not detected'}[/]")
+        
+        if self.analysis.issues:
+            console.print("\n[bold red]Issues Found:[/]")
+            for issue in self.analysis.issues:
+                console.print(f"  {issue}")
+    
+    def clean_data(
+        self, 
+        df: pd.DataFrame, 
+        instruction_col: Optional[str] = None,
+        input_col: Optional[str] = None,
+        output_col: Optional[str] = None
+    ) -> pd.DataFrame:
+        """
+        Clean and validate the dataset.
+        
+        Args:
+            df: Input DataFrame
+            instruction_col: Override instruction column name
+            input_col: Override input column name
+            output_col: Override output column name
+            
+        Returns:
+            Cleaned DataFrame
+        """
+        console.print("\n[bold blue]🧹 Cleaning dataset...[/]")
+        
+        # Use detected columns if not specified
+        if self.analysis:
+            instruction_col = instruction_col or self.analysis.instruction_column
+            input_col = input_col or self.analysis.input_column
+            output_col = output_col or self.analysis.output_column
+        
+        if not instruction_col or not output_col:
+            raise ValueError("Instruction and output columns are required")
+        
+        df_clean = df.copy()
+        original_count = len(df_clean)
+        
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            console=console
+        ) as progress:
+            # Remove empty values
+            task = progress.add_task("Removing empty values...", total=None)
+            df_clean = df_clean.dropna(subset=[instruction_col, output_col])
+            progress.update(task, completed=True)
+            
+            # Remove duplicates
+            if self.config.remove_duplicates:
+                task = progress.add_task("Removing duplicates...", total=None)
+                df_clean = df_clean.drop_duplicates(subset=[instruction_col])
+                progress.update(task, completed=True)
+            
+            # Filter by length constraints
+            task = progress.add_task("Applying length filters...", total=None)
+            
+            # Instruction length filter
+            df_clean = df_clean[
+                df_clean[instruction_col].str.len() >= self.config.min_instruction_length
+            ]
+            df_clean = df_clean[
+                df_clean[instruction_col].str.len() <= self.config.max_instruction_length
+            ]
+            
+            # Response length filter
+            df_clean = df_clean[
+                df_clean[output_col].str.len() >= self.config.min_response_length
+            ]
+            df_clean = df_clean[
+                df_clean[output_col].str.len() <= self.config.max_response_length
+            ]
+            progress.update(task, completed=True)
+            
+            # Clean text
+            task = progress.add_task("Cleaning text...", total=None)
+            df_clean[instruction_col] = df_clean[instruction_col].str.strip()
+            df_clean[output_col] = df_clean[output_col].str.strip()
+            if input_col and input_col in df_clean.columns:
+                df_clean[input_col] = df_clean[input_col].fillna('').str.strip()
+            progress.update(task, completed=True)
+        
+        removed_count = original_count - len(df_clean)
+        console.print(f"[green]✓ Cleaned dataset: {len(df_clean)} rows remaining ({removed_count} removed)[/]")
+        
+        return df_clean
+    
+    def format_for_training(
+        self,
+        df: pd.DataFrame,
+        goal: str,
+        output_path: str,
+        instruction_col: Optional[str] = None,
+        input_col: Optional[str] = None,
+        output_col: Optional[str] = None
+    ) -> str:
+        """
+        Format the dataset into HuggingFace-ready JSONL.
+        
+        Args:
+            df: Cleaned DataFrame
+            goal: Training goal/purpose (e.g., 'medical_assistant')
+            output_path: Path to save the JSONL file
+            instruction_col: Instruction column name
+            input_col: Input/context column name
+            output_col: Output/response column name
+            
+        Returns:
+            Path to the created JSONL file
+        """
+        console.print(f"\n[bold blue]📝 Formatting for training goal: [cyan]{goal}[/][/]")
+        
+        # Use detected columns if not specified
+        if self.analysis:
+            instruction_col = instruction_col or self.analysis.instruction_column
+            input_col = input_col or self.analysis.input_column
+            output_col = output_col or self.analysis.output_column
+        
+        if not instruction_col or not output_col:
+            raise ValueError("Instruction and output columns are required")
+        
+        output_path = Path(output_path)
+        output_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        # Create system prompt based on goal
+        system_prompt = self._generate_system_prompt(goal)
+        
+        formatted_data = []
+        
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            console=console
+        ) as progress:
+            task = progress.add_task("Formatting entries...", total=len(df))
+            
+            for _, row in df.iterrows():
+                instruction = str(row[instruction_col])
+                output = str(row[output_col])
+                context = str(row.get(input_col, '')) if input_col and input_col in df.columns else ''
+                
+                # Format as Alpaca-style instruction format
+                entry = {
+                    "instruction": instruction,
+                    "input": context,
+                    "output": output,
+                    "system": system_prompt
+                }
+                
+                # Also create chat format for compatibility
+                entry["conversations"] = [
+                    {"role": "system", "content": system_prompt},
+                    {"role": "user", "content": instruction + (f"\n\nContext: {context}" if context else "")},
+                    {"role": "assistant", "content": output}
+                ]
+                
+                formatted_data.append(entry)
+                progress.advance(task)
+        
+        # Write JSONL
+        with open(output_path, 'w', encoding='utf-8') as f:
+            for entry in formatted_data:
+                f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+        
+        console.print(f"[green]✓ Created training file: {output_path}[/]")
+        console.print(f"  • Total samples: {len(formatted_data)}")
+        console.print(f"  • Format: JSONL (Alpaca-style + Chat format)")
+        
+        return str(output_path)
+    
+    def _generate_system_prompt(self, goal: str) -> str:
+        """Generate a system prompt based on the training goal."""
+        goal_lower = goal.lower().replace('_', ' ').replace('-', ' ')
+        
+        # Common goal templates
+        templates = {
+            'medical': "You are a knowledgeable medical assistant. Provide accurate, helpful medical information while always recommending users consult healthcare professionals for specific medical advice.",
+            'legal': "You are a legal information assistant. Provide helpful legal information while noting that you are not a lawyer and users should consult legal professionals for specific legal advice.",
+            'coding': "You are an expert programming assistant. Help users write clean, efficient, and well-documented code. Explain your solutions clearly.",
+            'customer': "You are a helpful customer service assistant. Be polite, professional, and focused on solving customer issues efficiently.",
+            'education': "You are an educational assistant. Explain concepts clearly and adapt your explanations to the user's level of understanding.",
+            'writing': "You are a skilled writing assistant. Help users improve their writing with clear, constructive feedback and suggestions.",
+            'assistant': "You are a helpful AI assistant. Provide accurate, useful responses while being conversational and engaging."
+        }
+        
+        # Find matching template
+        for key, prompt in templates.items():
+            if key in goal_lower:
+                return prompt
+        
+        # Default template
+        return f"You are a specialized AI assistant for {goal}. Provide helpful, accurate, and relevant responses to user queries."
+    
+    def process(
+        self,
+        input_path: str,
+        output_path: str,
+        goal: str,
+        instruction_col: Optional[str] = None,
+        input_col: Optional[str] = None,
+        output_col: Optional[str] = None
+    ) -> Tuple[str, DatasetAnalysis]:
+        """
+        Complete end-to-end processing pipeline.
+        
+        Args:
+            input_path: Path to input dataset
+            output_path: Path for output JSONL
+            goal: Training goal
+            instruction_col: Override instruction column
+            input_col: Override input column
+            output_col: Override output column
+            
+        Returns:
+            Tuple of (output_path, analysis)
+        """
+        console.print("\n" + "="*60)
+        console.print("[bold magenta]🏗️  DATA ARCHITECT AGENT[/]")
+        console.print("="*60)
+        
+        # Load
+        df = self.load_dataset(input_path)
+        
+        # Analyze
+        analysis = self.analyze_dataset(df)
+        
+        # Check quality
+        if analysis.quality_score < self.config.quality_threshold:
+            console.print(f"[yellow]⚠️  Warning: Quality score ({analysis.quality_score:.2%}) below threshold ({self.config.quality_threshold:.2%})[/]")
+        
+        # Clean
+        df_clean = self.clean_data(
+            df,
+            instruction_col=instruction_col or analysis.instruction_column,
+            input_col=input_col or analysis.input_column,
+            output_col=output_col or analysis.output_column
+        )
+        
+        # Format
+        final_path = self.format_for_training(
+            df_clean,
+            goal=goal,
+            output_path=output_path,
+            instruction_col=instruction_col or analysis.instruction_column,
+            input_col=input_col or analysis.input_column,
+            output_col=output_col or analysis.output_column
+        )
+        
+        console.print("\n[bold green]✅ Data preparation complete![/]")
+        
+        return final_path, analysis
+
+
+if __name__ == "__main__":
+    # Example usage
+    import sys
+    
+    if len(sys.argv) < 3:
+        print("Usage: python data_architect.py <input_file> <goal>")
+        sys.exit(1)
+    
+    input_file = sys.argv[1]
+    goal = sys.argv[2]
+    output_file = f"./output/processed_data/{goal}_training.jsonl"
+    
+    agent = DataArchitectAgent()
+    agent.process(input_file, output_file, goal)

agents/the_judge.py

@@ -0,0 +1,566 @@
+"""
+TheJudge - LLM-as-a-Judge Evaluation Agent
+=============================================
+Runs a 'Model Arena' comparing base vs fine-tuned models using
+a Judge LLM (GPT-4o or Claude 3.5) to score responses.
+"""
+
+import os
+import json
+import random
+from pathlib import Path
+from dataclasses import dataclass, field
+from typing import Optional, List, Dict, Any, Tuple
+from datetime import datetime
+from enum import Enum
+
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn
+from rich.table import Table
+from rich.panel import Panel
+from rich.markdown import Markdown
+
+console = Console()
+
+
+class JudgeModel(Enum):
+    """Supported Judge LLM models."""
+    GPT4O = "gpt-4o"
+    CLAUDE_35_SONNET = "claude-3-5-sonnet-20241022"
+
+
+@dataclass
+class Verdict:
+    """Verdict from a single comparison."""
+    prompt: str
+    response_a: str  # Base model
+    response_b: str  # Fine-tuned model
+    winner: str  # 'A', 'B', or 'TIE'
+    score_a: int  # 1-10
+    score_b: int  # 1-10
+    reasoning: str
+    criteria_scores: Dict[str, Dict[str, int]] = field(default_factory=dict)
+
+
+@dataclass
+class ArenaResult:
+    """Complete arena evaluation results."""
+    verdicts: List[Verdict]
+    base_model_wins: int
+    finetuned_wins: int
+    ties: int
+    base_model_avg_score: float
+    finetuned_avg_score: float
+    win_rate: float
+    total_comparisons: int
+    evaluation_time: float
+    judge_model: str
+
+
+class TheJudge:
+    """
+    Model Arena evaluation agent using LLM-as-a-Judge.
+    
+    Compares base model vs fine-tuned model responses and
+    provides detailed scoring based on multiple criteria.
+    """
+    
+    EVALUATION_CRITERIA = [
+        ("helpfulness", "How helpful and useful is the response?"),
+        ("accuracy", "How accurate and factually correct is the response?"),
+        ("relevance", "How relevant is the response to the user's query?"),
+        ("clarity", "How clear and well-structured is the response?"),
+        ("completeness", "How complete and thorough is the response?")
+    ]
+    
+    JUDGE_PROMPT = """You are an expert evaluator comparing two AI assistant responses. Your task is to evaluate which response is better based on multiple criteria.
+
+## User Query
+{prompt}
+
+## Response A
+{response_a}
+
+## Response B
+{response_b}
+
+## Evaluation Criteria
+For each criterion, rate both responses on a scale of 1-10:
+1. Helpfulness: How helpful and useful is the response?
+2. Accuracy: How accurate and factually correct is the response?
+3. Relevance: How relevant is the response to the user's query?
+4. Clarity: How clear and well-structured is the response?
+5. Completeness: How complete and thorough is the response?
+
+## Instructions
+1. Evaluate both responses fairly and objectively
+2. Consider the strengths and weaknesses of each response
+3. Provide specific reasoning for your evaluation
+4. Determine the overall winner (A, B, or TIE)
+
+## Output Format (JSON)
+{{
+    "helpfulness": {{"A": <1-10>, "B": <1-10>}},
+    "accuracy": {{"A": <1-10>, "B": <1-10>}},
+    "relevance": {{"A": <1-10>, "B": <1-10>}},
+    "clarity": {{"A": <1-10>, "B": <1-10>}},
+    "completeness": {{"A": <1-10>, "B": <1-10>}},
+    "overall_score_a": <1-10>,
+    "overall_score_b": <1-10>,
+    "winner": "<A|B|TIE>",
+    "reasoning": "<detailed explanation of your evaluation>"
+}}
+
+Respond with ONLY the JSON object, no additional text."""
+
+    def __init__(
+        self,
+        judge_model: JudgeModel = JudgeModel.GPT4O,
+        temperature: float = 0.2,
+        max_tokens: int = 1024
+    ):
+        """
+        Initialize TheJudge.
+        
+        Args:
+            judge_model: Which LLM to use as judge
+            temperature: Sampling temperature for judge
+            max_tokens: Max tokens for judge response
+        """
+        self.judge_model = judge_model
+        self.temperature = temperature
+        self.max_tokens = max_tokens
+        self._client = None
+        
+    def _get_client(self):
+        """Get or create the API client."""
+        if self._client is not None:
+            return self._client
+        
+        if self.judge_model == JudgeModel.GPT4O:
+            try:
+                from openai import OpenAI
+                api_key = os.getenv("OPENAI_API_KEY")
+                if not api_key:
+                    raise ValueError("OPENAI_API_KEY environment variable not set")
+                self._client = OpenAI(api_key=api_key)
+            except ImportError:
+                raise ImportError("OpenAI package required. Install with: pip install openai")
+        else:
+            try:
+                from anthropic import Anthropic
+                api_key = os.getenv("ANTHROPIC_API_KEY")
+                if not api_key:
+                    raise ValueError("ANTHROPIC_API_KEY environment variable not set")
+                self._client = Anthropic(api_key=api_key)
+            except ImportError:
+                raise ImportError("Anthropic package required. Install with: pip install anthropic")
+        
+        return self._client
+    
+    def _call_judge(self, prompt: str) -> str:
+        """Call the judge LLM."""
+        client = self._get_client()
+        
+        if self.judge_model == JudgeModel.GPT4O:
+            response = client.chat.completions.create(
+                model=self.judge_model.value,
+                messages=[{"role": "user", "content": prompt}],
+                temperature=self.temperature,
+                max_tokens=self.max_tokens
+            )
+            return response.choices[0].message.content
+        else:
+            response = client.messages.create(
+                model=self.judge_model.value,
+                max_tokens=self.max_tokens,
+                temperature=self.temperature,
+                messages=[{"role": "user", "content": prompt}]
+            )
+            return response.content[0].text
+    
+    def _parse_verdict(self, response: str, prompt: str, resp_a: str, resp_b: str) -> Verdict:
+        """Parse the judge's response into a Verdict."""
+        try:
+            # Clean response (remove markdown code blocks if present)
+            clean_response = response.strip()
+            if clean_response.startswith("```"):
+                clean_response = clean_response.split("```")[1]
+                if clean_response.startswith("json"):
+                    clean_response = clean_response[4:]
+            
+            data = json.loads(clean_response)
+            
+            criteria_scores = {}
+            for criterion, _ in self.EVALUATION_CRITERIA:
+                if criterion in data:
+                    criteria_scores[criterion] = data[criterion]
+            
+            return Verdict(
+                prompt=prompt,
+                response_a=resp_a,
+                response_b=resp_b,
+                winner=data.get("winner", "TIE"),
+                score_a=data.get("overall_score_a", 5),
+                score_b=data.get("overall_score_b", 5),
+                reasoning=data.get("reasoning", "No reasoning provided"),
+                criteria_scores=criteria_scores
+            )
+        except (json.JSONDecodeError, KeyError) as e:
+            console.print(f"[yellow]⚠️ Warning: Failed to parse judge response: {e}[/]")
+            return Verdict(
+                prompt=prompt,
+                response_a=resp_a,
+                response_b=resp_b,
+                winner="TIE",
+                score_a=5,
+                score_b=5,
+                reasoning=f"Parse error: {response[:200]}...",
+                criteria_scores={}
+            )
+    
+    def generate_response(
+        self,
+        model: Any,
+        tokenizer: Any,
+        prompt: str,
+        max_new_tokens: int = 512
+    ) -> str:
+        """
+        Generate a response from a model.
+        
+        Args:
+            model: The language model
+            tokenizer: The tokenizer
+            prompt: Input prompt
+            max_new_tokens: Maximum tokens to generate
+            
+        Returns:
+            Generated response string
+        """
+        try:
+            from unsloth import FastLanguageModel
+            FastLanguageModel.for_inference(model)
+        except ImportError:
+            pass
+        
+        # Format with Alpaca template
+        alpaca_prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+"""
+        
+        inputs = tokenizer(alpaca_prompt, return_tensors="pt").to(model.device)
+        outputs = model.generate(
+            **inputs,
+            max_new_tokens=max_new_tokens,
+            temperature=0.7,
+            do_sample=True,
+            top_p=0.9,
+            pad_token_id=tokenizer.eos_token_id
+        )
+        
+        response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract just the response part
+        if "### Response:" in response:
+            response = response.split("### Response:")[-1].strip()
+        
+        return response
+    
+    def get_judge_verdict(
+        self,
+        prompt: str,
+        response_a: str,
+        response_b: str,
+        randomize: bool = True
+    ) -> Verdict:
+        """
+        Get judge verdict for a single comparison.
+        
+        Args:
+            prompt: Original user prompt
+            response_a: Response from model A (base)
+            response_b: Response from model B (fine-tuned)
+            randomize: Randomize A/B order to reduce position bias
+            
+        Returns:
+            Verdict with scores and reasoning
+        """
+        # Randomize order to reduce position bias
+        if randomize and random.random() > 0.5:
+            judge_prompt = self.JUDGE_PROMPT.format(
+                prompt=prompt,
+                response_a=response_b,
+                response_b=response_a
+            )
+            swapped = True
+        else:
+            judge_prompt = self.JUDGE_PROMPT.format(
+                prompt=prompt,
+                response_a=response_a,
+                response_b=response_b
+            )
+            swapped = False
+        
+        # Get judge response
+        judge_response = self._call_judge(judge_prompt)
+        
+        # Parse verdict
+        verdict = self._parse_verdict(
+            judge_response,
+            prompt,
+            response_a if not swapped else response_b,
+            response_b if not swapped else response_a
+        )
+        
+        # Swap back if needed
+        if swapped:
+            verdict.response_a = response_a
+            verdict.response_b = response_b
+            verdict.score_a, verdict.score_b = verdict.score_b, verdict.score_a
+            if verdict.winner == "A":
+                verdict.winner = "B"
+            elif verdict.winner == "B":
+                verdict.winner = "A"
+        
+        return verdict
+    
+    def run_arena(
+        self,
+        base_model: Any,
+        finetuned_model: Any,
+        tokenizer: Any,
+        test_prompts: List[str],
+        finetuned_tokenizer: Optional[Any] = None
+    ) -> ArenaResult:
+        """
+        Run the complete Model Arena evaluation.
+        
+        Args:
+            base_model: Base model for comparison
+            finetuned_model: Fine-tuned model
+            tokenizer: Tokenizer for base model
+            test_prompts: List of evaluation prompts
+            finetuned_tokenizer: Optional separate tokenizer for fine-tuned model
+            
+        Returns:
+            ArenaResult with all verdicts and statistics
+        """
+        console.print("\n" + "="*60)
+        console.print("[bold magenta]⚖️  THE JUDGE - MODEL ARENA[/]")
+        console.print("="*60)
+        
+        console.print(f"\n[bold]Judge Model:[/] {self.judge_model.value}")
+        console.print(f"[bold]Test Samples:[/] {len(test_prompts)}")
+        
+        ft_tokenizer = finetuned_tokenizer or tokenizer
+        verdicts = []
+        start_time = datetime.now()
+        
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            BarColumn(),
+            TextColumn("[progress.percentage]{task.percentage:>3.0f}%"),
+            console=console
+        ) as progress:
+            task = progress.add_task("Running arena battles...", total=len(test_prompts))
+            
+            for i, prompt in enumerate(test_prompts):
+                progress.update(task, description=f"Battle {i+1}/{len(test_prompts)}...")
+                
+                # Generate responses
+                response_a = self.generate_response(base_model, tokenizer, prompt)
+                response_b = self.generate_response(finetuned_model, ft_tokenizer, prompt)
+                
+                # Get verdict
+                verdict = self.get_judge_verdict(prompt, response_a, response_b)
+                verdicts.append(verdict)
+                
+                progress.advance(task)
+        
+        evaluation_time = (datetime.now() - start_time).total_seconds()
+        
+        # Calculate statistics
+        base_wins = sum(1 for v in verdicts if v.winner == "A")
+        ft_wins = sum(1 for v in verdicts if v.winner == "B")
+        ties = sum(1 for v in verdicts if v.winner == "TIE")
+        
+        base_avg = sum(v.score_a for v in verdicts) / len(verdicts) if verdicts else 0
+        ft_avg = sum(v.score_b for v in verdicts) / len(verdicts) if verdicts else 0
+        
+        win_rate = ft_wins / len(verdicts) if verdicts else 0
+        
+        result = ArenaResult(
+            verdicts=verdicts,
+            base_model_wins=base_wins,
+            finetuned_wins=ft_wins,
+            ties=ties,
+            base_model_avg_score=base_avg,
+            finetuned_avg_score=ft_avg,
+            win_rate=win_rate,
+            total_comparisons=len(verdicts),
+            evaluation_time=evaluation_time,
+            judge_model=self.judge_model.value
+        )
+        
+        # Display results
+        self._display_results(result)
+        
+        return result
+    
+    def _display_results(self, result: ArenaResult):
+        """Display arena results."""
+        console.print("\n" + "-"*40)
+        console.print("[bold]📊 ARENA RESULTS[/]")
+        console.print("-"*40)
+        
+        # Win statistics
+        table = Table(title="Battle Statistics", show_header=True)
+        table.add_column("Metric", style="cyan")
+        table.add_column("Value", style="green")
+        
+        table.add_row("Base Model Wins", str(result.base_model_wins))
+        table.add_row("Fine-tuned Wins", f"[bold green]{result.finetuned_wins}[/]")
+        table.add_row("Ties", str(result.ties))
+        table.add_row("Total Comparisons", str(result.total_comparisons))
+        table.add_row("Fine-tuned Win Rate", f"[bold]{result.win_rate:.1%}[/]")
+        
+        console.print(table)
+        
+        # Score comparison
+        table2 = Table(title="Average Scores (1-10)", show_header=True)
+        table2.add_column("Model", style="cyan")
+        table2.add_column("Score", style="green")
+        
+        table2.add_row("Base Model", f"{result.base_model_avg_score:.2f}")
+        table2.add_row("Fine-tuned Model", f"[bold]{result.finetuned_avg_score:.2f}[/]")
+        table2.add_row("Improvement", f"+{result.finetuned_avg_score - result.base_model_avg_score:.2f}")
+        
+        console.print(table2)
+        
+        # Verdict
+        improvement_pct = ((result.finetuned_avg_score / result.base_model_avg_score) - 1) * 100 if result.base_model_avg_score > 0 else 0
+        
+        if result.win_rate > 0.6:
+            verdict_text = f"[bold green]✅ SIGNIFICANT IMPROVEMENT[/]\nFine-tuned model wins {result.win_rate:.0%} of battles with {improvement_pct:.1f}% score improvement!"
+        elif result.win_rate > 0.4:
+            verdict_text = f"[bold yellow]⚖️ MARGINAL IMPROVEMENT[/]\nFine-tuned model shows moderate improvement ({result.win_rate:.0%} win rate)"
+        else:
+            verdict_text = f"[bold red]⚠️ NO IMPROVEMENT[/]\nFine-tuning did not improve model performance. Consider adjusting training data or hyperparameters."
+        
+        console.print(Panel(verdict_text, title="Final Verdict", border_style="blue"))
+    
+    def generate_report(
+        self,
+        result: ArenaResult,
+        output_path: str,
+        include_examples: int = 5
+    ) -> str:
+        """
+        Generate a detailed evaluation report.
+        
+        Args:
+            result: ArenaResult from run_arena
+            output_path: Path to save the report
+            include_examples: Number of example comparisons to include
+            
+        Returns:
+            Path to the generated report
+        """
+        report_path = Path(output_path)
+        report_path.parent.mkdir(parents=True, exist_ok=True)
+        
+        report = {
+            "timestamp": datetime.now().isoformat(),
+            "judge_model": result.judge_model,
+            "summary": {
+                "total_comparisons": result.total_comparisons,
+                "base_model_wins": result.base_model_wins,
+                "finetuned_wins": result.finetuned_wins,
+                "ties": result.ties,
+                "finetuned_win_rate": result.win_rate,
+                "base_model_avg_score": result.base_model_avg_score,
+                "finetuned_avg_score": result.finetuned_avg_score,
+                "score_improvement": result.finetuned_avg_score - result.base_model_avg_score,
+                "evaluation_time_seconds": result.evaluation_time
+            },
+            "example_verdicts": [
+                {
+                    "prompt": v.prompt,
+                    "response_base": v.response_a[:500] + "..." if len(v.response_a) > 500 else v.response_a,
+                    "response_finetuned": v.response_b[:500] + "..." if len(v.response_b) > 500 else v.response_b,
+                    "winner": v.winner,
+                    "score_base": v.score_a,
+                    "score_finetuned": v.score_b,
+                    "reasoning": v.reasoning
+                }
+                for v in result.verdicts[:include_examples]
+            ]
+        }
+        
+        with open(report_path, 'w', encoding='utf-8') as f:
+            json.dump(report, f, indent=2, ensure_ascii=False)
+        
+        console.print(f"\n[green]✓ Report saved to: {report_path}[/]")
+        
+        return str(report_path)
+    
+    def run_with_test_data(
+        self,
+        base_model: Any,
+        finetuned_model: Any,
+        tokenizer: Any,
+        test_data_path: str,
+        num_samples: int = 50,
+        finetuned_tokenizer: Optional[Any] = None
+    ) -> ArenaResult:
+        """
+        Run arena with test data from a JSONL file.
+        
+        Args:
+            base_model: Base model
+            finetuned_model: Fine-tuned model
+            tokenizer: Tokenizer
+            test_data_path: Path to test JSONL file
+            num_samples: Number of samples to evaluate
+            finetuned_tokenizer: Optional separate tokenizer
+            
+        Returns:
+            ArenaResult
+        """
+        console.print(f"\n[blue]Loading test data from: {test_data_path}[/]")
+        
+        prompts = []
+        with open(test_data_path, 'r', encoding='utf-8') as f:
+            for line in f:
+                data = json.loads(line)
+                prompts.append(data.get('instruction', data.get('prompt', '')))
+        
+        # Sample if needed
+        if len(prompts) > num_samples:
+            prompts = random.sample(prompts, num_samples)
+        
+        console.print(f"[green]✓ Loaded {len(prompts)} test prompts[/]")
+        
+        return self.run_arena(
+            base_model,
+            finetuned_model,
+            tokenizer,
+            prompts,
+            finetuned_tokenizer
+        )
+
+
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) < 4:
+        print("Usage: python the_judge.py <base_model_path> <finetuned_model_path> <test_data.jsonl>")
+        sys.exit(1)
+    
+    print("TheJudge requires models to be loaded. See main.py for integrated usage.")

agents/training_pilot.py

@@ -0,0 +1,528 @@
+"""
+TrainingPilot - Automated Fine-Tuning Agent
+=============================================
+Uses Unsloth for ultra-fast LoRA fine-tuning with auto-configured
+hyperparameters based on dataset size.
+"""
+
+import os
+import yaml
+from pathlib import Path
+from dataclasses import dataclass
+from typing import Optional, Dict, Any, Tuple
+from datetime import datetime
+
+from rich.console import Console
+from rich.progress import Progress, SpinnerColumn, TextColumn, BarColumn, TimeRemainingColumn
+from rich.table import Table
+from rich.panel import Panel
+
+console = Console()
+
+
+@dataclass
+class HyperParams:
+    """Training hyperparameters configuration."""
+    lora_rank: int = 16
+    lora_alpha: int = 32
+    learning_rate: float = 1e-4
+    num_epochs: int = 3
+    batch_size: int = 8
+    gradient_accumulation_steps: int = 2
+    warmup_ratio: float = 0.03
+    weight_decay: float = 0.01
+    max_grad_norm: float = 1.0
+    optimizer: str = "adamw_8bit"
+    lr_scheduler: str = "cosine"
+    gradient_checkpointing: bool = True
+    
+    def to_dict(self) -> Dict[str, Any]:
+        return {
+            'lora_rank': self.lora_rank,
+            'lora_alpha': self.lora_alpha,
+            'learning_rate': self.learning_rate,
+            'num_epochs': self.num_epochs,
+            'batch_size': self.batch_size,
+            'gradient_accumulation_steps': self.gradient_accumulation_steps,
+            'warmup_ratio': self.warmup_ratio,
+            'weight_decay': self.weight_decay,
+            'max_grad_norm': self.max_grad_norm,
+            'optimizer': self.optimizer,
+            'lr_scheduler': self.lr_scheduler,
+            'gradient_checkpointing': self.gradient_checkpointing
+        }
+
+
+@dataclass
+class TrainingResult:
+    """Results from a training run."""
+    model_path: str
+    training_time: float
+    final_loss: float
+    num_steps: int
+    hyperparams: HyperParams
+    dataset_size: int
+    metrics: Dict[str, Any]
+
+
+class TrainingPilot:
+    """
+    Automated fine-tuning agent using Unsloth for ultra-fast LoRA training.
+    
+    Features:
+    - Auto-configures hyperparameters based on dataset size
+    - Uses 4-bit quantization for memory efficiency
+    - Supports gradient checkpointing
+    - Automatic checkpoint saving
+    """
+    
+    # Dataset size thresholds
+    SMALL_THRESHOLD = 1000
+    MEDIUM_THRESHOLD = 10000
+    
+    # Default target modules for LoRA
+    DEFAULT_TARGET_MODULES = [
+        "q_proj", "k_proj", "v_proj", "o_proj",
+        "gate_proj", "up_proj", "down_proj"
+    ]
+    
+    def __init__(
+        self,
+        config_path: Optional[str] = None,
+        base_model: str = "unsloth/llama-3-8b-bnb-4bit",
+        max_seq_length: int = 2048,
+        output_dir: str = "./output/models"
+    ):
+        """
+        Initialize the TrainingPilot.
+        
+        Args:
+            config_path: Path to config YAML file
+            base_model: HuggingFace model identifier
+            max_seq_length: Maximum sequence length
+            output_dir: Directory for saving models
+        """
+        self.config = self._load_config(config_path)
+        self.base_model = base_model
+        self.max_seq_length = max_seq_length
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        
+        self.model = None
+        self.tokenizer = None
+        self.trainer = None
+        
+    def _load_config(self, config_path: Optional[str]) -> Dict[str, Any]:
+        """Load configuration from YAML file."""
+        if config_path and Path(config_path).exists():
+            with open(config_path, 'r') as f:
+                return yaml.safe_load(f)
+        return {}
+    
+    def auto_configure(self, dataset_size: int) -> HyperParams:
+        """
+        Auto-configure hyperparameters based on dataset size.
+        
+        Args:
+            dataset_size: Number of training samples
+            
+        Returns:
+            Optimized HyperParams configuration
+        """
+        console.print(f"\n[bold blue]⚙️  Auto-configuring for {dataset_size:,} samples...[/]")
+        
+        if dataset_size < self.SMALL_THRESHOLD:
+            # Small dataset: Higher learning rate, more epochs, smaller batch
+            params = HyperParams(
+                lora_rank=8,
+                lora_alpha=16,
+                learning_rate=2e-4,
+                num_epochs=5,
+                batch_size=4,
+                gradient_accumulation_steps=4
+            )
+            tier = "SMALL"
+        elif dataset_size < self.MEDIUM_THRESHOLD:
+            # Medium dataset: Balanced parameters
+            params = HyperParams(
+                lora_rank=16,
+                lora_alpha=32,
+                learning_rate=1e-4,
+                num_epochs=3,
+                batch_size=8,
+                gradient_accumulation_steps=2
+            )
+            tier = "MEDIUM"
+        else:
+            # Large dataset: Lower learning rate, fewer epochs, larger batch
+            params = HyperParams(
+                lora_rank=32,
+                lora_alpha=64,
+                learning_rate=5e-5,
+                num_epochs=2,
+                batch_size=16,
+                gradient_accumulation_steps=1
+            )
+            tier = "LARGE"
+        
+        # Display configuration
+        table = Table(title=f"Auto-Configured Parameters [{tier}]", show_header=True)
+        table.add_column("Parameter", style="cyan")
+        table.add_column("Value", style="green")
+        
+        for key, value in params.to_dict().items():
+            table.add_row(key, str(value))
+        
+        console.print(table)
+        
+        return params
+    
+    def setup_model(
+        self,
+        hyperparams: HyperParams,
+        model_name: Optional[str] = None
+    ) -> Tuple[Any, Any]:
+        """
+        Setup the model with LoRA configuration using Unsloth.
+        
+        Args:
+            hyperparams: Training hyperparameters
+            model_name: Override model name
+            
+        Returns:
+            Tuple of (model, tokenizer)
+        """
+        console.print("\n[bold blue]🚀 Setting up model with Unsloth...[/]")
+        
+        try:
+            from unsloth import FastLanguageModel
+        except ImportError:
+            console.print("[red]❌ Unsloth not installed. Please install with: pip install unsloth[/]")
+            raise ImportError("Unsloth is required for training. Install with: pip install unsloth")
+        
+        model_name = model_name or self.base_model
+        
+        with Progress(
+            SpinnerColumn(),
+            TextColumn("[progress.description]{task.description}"),
+            console=console
+        ) as progress:
+            task = progress.add_task("Loading model...", total=None)
+            
+            # Load model with Unsloth
+            model, tokenizer = FastLanguageModel.from_pretrained(
+                model_name=model_name,
+                max_seq_length=self.max_seq_length,
+                dtype=None,  # Auto-detect
+                load_in_4bit=True,
+            )
+            
+            progress.update(task, description="Applying LoRA...")
+            
+            # Apply LoRA with PEFT
+            model = FastLanguageModel.get_peft_model(
+                model,
+                r=hyperparams.lora_rank,
+                lora_alpha=hyperparams.lora_alpha,
+                lora_dropout=0,
+                target_modules=self.DEFAULT_TARGET_MODULES,
+                bias="none",
+                use_gradient_checkpointing="unsloth",
+                random_state=42,
+                use_rslora=True,
+            )
+            
+            progress.update(task, completed=True)
+        
+        self.model = model
+        self.tokenizer = tokenizer
+        
+        console.print("[green]✓ Model setup complete[/]")
+        self._print_model_info()
+        
+        return model, tokenizer
+    
+    def _print_model_info(self):
+        """Print model information."""
+        if self.model is None:
+            return
+        
+        # Calculate trainable parameters
+        trainable_params = sum(p.numel() for p in self.model.parameters() if p.requires_grad)
+        total_params = sum(p.numel() for p in self.model.parameters())
+        
+        console.print(Panel(
+            f"[bold]Model:[/] {self.base_model}\n"
+            f"[bold]Trainable Parameters:[/] {trainable_params:,} ({100 * trainable_params / total_params:.2f}%)\n"
+            f"[bold]Total Parameters:[/] {total_params:,}",
+            title="Model Information",
+            border_style="blue"
+        ))
+    
+    def load_dataset(self, data_path: str) -> Any:
+        """
+        Load and prepare the training dataset.
+        
+        Args:
+            data_path: Path to JSONL training file
+            
+        Returns:
+            HuggingFace Dataset object
+        """
+        from datasets import load_dataset
+        
+        console.print(f"\n[bold blue]📂 Loading training data:[/] {data_path}")
+        
+        dataset = load_dataset('json', data_files=data_path, split='train')
+        console.print(f"[green]✓ Loaded {len(dataset):,} training samples[/]")
+        
+        return dataset
+    
+    def _format_prompts(self, dataset: Any) -> Any:
+        """
+        Format dataset into training prompts.
+        
+        Args:
+            dataset: HuggingFace Dataset
+            
+        Returns:
+            Formatted dataset
+        """
+        # Alpaca-style prompt template
+        alpaca_template = """Below is an instruction that describes a task, paired with an input that provides further context. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Input:
+{input}
+
+### Response:
+{output}"""
+
+        alpaca_template_no_input = """Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{instruction}
+
+### Response:
+{output}"""
+
+        def format_prompt(example):
+            if example.get('input') and len(str(example['input']).strip()) > 0:
+                text = alpaca_template.format(
+                    instruction=example['instruction'],
+                    input=example['input'],
+                    output=example['output']
+                )
+            else:
+                text = alpaca_template_no_input.format(
+                    instruction=example['instruction'],
+                    output=example['output']
+                )
+            return {"text": text}
+        
+        return dataset.map(format_prompt)
+    
+    def train(
+        self,
+        dataset: Any,
+        hyperparams: HyperParams,
+        output_name: Optional[str] = None
+    ) -> TrainingResult:
+        """
+        Run the fine-tuning training loop.
+        
+        Args:
+            dataset: Training dataset
+            hyperparams: Training hyperparameters
+            output_name: Custom name for output model
+            
+        Returns:
+            TrainingResult with metrics and model path
+        """
+        from trl import SFTTrainer
+        from transformers import TrainingArguments
+        
+        console.print("\n" + "="*60)
+        console.print("[bold magenta]🎯 TRAINING PILOT - STARTING TRAINING[/]")
+        console.print("="*60)
+        
+        if self.model is None or self.tokenizer is None:
+            raise RuntimeError("Model not setup. Call setup_model() first.")
+        
+        # Format dataset
+        console.print("\n[blue]Formatting training prompts...[/]")
+        formatted_dataset = self._format_prompts(dataset)
+        
+        # Generate output name
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        output_name = output_name or f"finetuned_model_{timestamp}"
+        model_output_path = self.output_dir / output_name
+        
+        # Setup training arguments
+        training_args = TrainingArguments(
+            output_dir=str(model_output_path),
+            num_train_epochs=hyperparams.num_epochs,
+            per_device_train_batch_size=hyperparams.batch_size,
+            gradient_accumulation_steps=hyperparams.gradient_accumulation_steps,
+            learning_rate=hyperparams.learning_rate,
+            warmup_ratio=hyperparams.warmup_ratio,
+            weight_decay=hyperparams.weight_decay,
+            max_grad_norm=hyperparams.max_grad_norm,
+            lr_scheduler_type=hyperparams.lr_scheduler,
+            optim=hyperparams.optimizer,
+            fp16=True,
+            logging_steps=10,
+            save_strategy="epoch",
+            save_total_limit=2,
+            report_to="none",
+            seed=42,
+        )
+        
+        # Initialize trainer
+        trainer = SFTTrainer(
+            model=self.model,
+            tokenizer=self.tokenizer,
+            train_dataset=formatted_dataset,
+            dataset_text_field="text",
+            max_seq_length=self.max_seq_length,
+            args=training_args,
+        )
+        
+        self.trainer = trainer
+        
+        # Train
+        console.print("\n[bold green]🏋️ Training in progress...[/]")
+        start_time = datetime.now()
+        
+        train_result = trainer.train()
+        
+        training_time = (datetime.now() - start_time).total_seconds()
+        
+        # Save model
+        console.print("\n[blue]Saving model...[/]")
+        trainer.save_model(str(model_output_path))
+        self.tokenizer.save_pretrained(str(model_output_path))
+        
+        # Get final metrics
+        final_loss = train_result.training_loss
+        num_steps = train_result.global_step
+        
+        result = TrainingResult(
+            model_path=str(model_output_path),
+            training_time=training_time,
+            final_loss=final_loss,
+            num_steps=num_steps,
+            hyperparams=hyperparams,
+            dataset_size=len(dataset),
+            metrics=train_result.metrics
+        )
+        
+        # Display results
+        self._display_results(result)
+        
+        return result
+    
+    def _display_results(self, result: TrainingResult):
+        """Display training results."""
+        hours, remainder = divmod(result.training_time, 3600)
+        minutes, seconds = divmod(remainder, 60)
+        time_str = f"{int(hours)}h {int(minutes)}m {int(seconds)}s"
+        
+        table = Table(title="Training Complete", show_header=True)
+        table.add_column("Metric", style="cyan")
+        table.add_column("Value", style="green")
+        
+        table.add_row("Model Path", result.model_path)
+        table.add_row("Training Time", time_str)
+        table.add_row("Final Loss", f"{result.final_loss:.4f}")
+        table.add_row("Total Steps", str(result.num_steps))
+        table.add_row("Dataset Size", f"{result.dataset_size:,}")
+        
+        console.print(table)
+        console.print("\n[bold green]✅ Training complete![/]")
+    
+    def export_for_deployment(self, model_path: str, export_path: Optional[str] = None) -> str:
+        """
+        Export the fine-tuned model for deployment.
+        
+        Args:
+            model_path: Path to the trained model
+            export_path: Custom export path
+            
+        Returns:
+            Path to exported model
+        """
+        try:
+            from unsloth import FastLanguageModel
+        except ImportError:
+            raise ImportError("Unsloth is required for export")
+        
+        console.print(f"\n[bold blue]📦 Exporting model for deployment...[/]")
+        
+        export_path = export_path or str(Path(model_path) / "deployment")
+        
+        # Load and merge LoRA weights
+        model, tokenizer = FastLanguageModel.from_pretrained(
+            model_name=model_path,
+            max_seq_length=self.max_seq_length,
+            dtype=None,
+            load_in_4bit=True,
+        )
+        
+        # Save merged model
+        model.save_pretrained_merged(export_path, tokenizer, save_method="merged_16bit")
+        
+        console.print(f"[green]✓ Exported to: {export_path}[/]")
+        
+        return export_path
+    
+    def run(
+        self,
+        data_path: str,
+        model_name: Optional[str] = None,
+        output_name: Optional[str] = None
+    ) -> TrainingResult:
+        """
+        Complete training pipeline.
+        
+        Args:
+            data_path: Path to JSONL training data
+            model_name: Override base model
+            output_name: Custom name for output model
+            
+        Returns:
+            TrainingResult
+        """
+        console.print("\n" + "="*60)
+        console.print("[bold magenta]🧑‍✈️ TRAINING PILOT AGENT[/]")
+        console.print("="*60)
+        
+        # Load dataset
+        dataset = self.load_dataset(data_path)
+        
+        # Auto-configure hyperparameters
+        hyperparams = self.auto_configure(len(dataset))
+        
+        # Setup model
+        self.setup_model(hyperparams, model_name)
+        
+        # Train
+        result = self.train(dataset, hyperparams, output_name)
+        
+        return result
+
+
+if __name__ == "__main__":
+    import sys
+    
+    if len(sys.argv) < 2:
+        print("Usage: python training_pilot.py <training_data.jsonl> [output_name]")
+        sys.exit(1)
+    
+    data_path = sys.argv[1]
+    output_name = sys.argv[2] if len(sys.argv) > 2 else None
+    
+    pilot = TrainingPilot()
+    result = pilot.run(data_path, output_name=output_name)

app.py

@@ -0,0 +1,1500 @@
+"""
+Auto-FineTune-Ops: Streamlit Dashboard
+======================================
+Premium interactive dashboard for ML fine-tuning pipeline.
+"""
+
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+import plotly.graph_objects as go
+from pathlib import Path
+import sys
+import os
+import json
+import time
+from datetime import datetime
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).parent))
+
+# Page configuration
+st.set_page_config(
+    page_title="Auto-FineTune-Ops",
+    page_icon="🤖",
+    layout="wide",
+    initial_sidebar_state="expanded"
+)
+
+# Premium CSS styling
+st.markdown("""
+<style>
+    /* Main container */
+    .main .block-container {
+        padding-top: 2rem;
+        padding-bottom: 2rem;
+    }
+    
+    /* Cards */
+    .stMetric {
+        background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
+        padding: 1rem;
+        border-radius: 12px;
+        border: 1px solid rgba(99, 102, 241, 0.2);
+        box-shadow: 0 4px 20px rgba(0, 0, 0, 0.3);
+    }
+    
+    /* Gradient headers */
+    .gradient-header {
+        background: linear-gradient(90deg, #6366f1, #8b5cf6, #a855f7);
+        -webkit-background-clip: text;
+        -webkit-text-fill-color: transparent;
+        font-size: 2.5rem;
+        font-weight: 700;
+        margin-bottom: 1rem;
+    }
+    
+    /* Info cards */
+    .info-card {
+        background: linear-gradient(135deg, #1a1a2e 0%, #16213e 100%);
+        padding: 1.5rem;
+        border-radius: 16px;
+        border: 1px solid rgba(99, 102, 241, 0.3);
+        margin: 1rem 0;
+    }
+    
+    /* Success badge */
+    .success-badge {
+        background: linear-gradient(90deg, #10b981, #059669);
+        color: white;
+        padding: 0.5rem 1rem;
+        border-radius: 20px;
+        font-weight: 600;
+        display: inline-block;
+    }
+    
+    /* Warning badge */
+    .warning-badge {
+        background: linear-gradient(90deg, #f59e0b, #d97706);
+        color: white;
+        padding: 0.5rem 1rem;
+        border-radius: 20px;
+        font-weight: 600;
+        display: inline-block;
+    }
+    
+    /* Sidebar styling */
+    section[data-testid="stSidebar"] {
+        background: linear-gradient(180deg, #0f0f23 0%, #1a1a2e 100%);
+    }
+    
+    /* Button styling */
+    .stButton > button {
+        background: linear-gradient(90deg, #6366f1, #8b5cf6);
+        color: white;
+        border: none;
+        border-radius: 8px;
+        padding: 0.5rem 2rem;
+        font-weight: 600;
+        transition: all 0.3s ease;
+    }
+    
+    .stButton > button:hover {
+        transform: translateY(-2px);
+        box-shadow: 0 4px 20px rgba(99, 102, 241, 0.4);
+    }
+    
+    /* Progress bar */
+    .stProgress > div > div {
+        background: linear-gradient(90deg, #6366f1, #8b5cf6, #a855f7);
+    }
+    
+    /* Tab styling */
+    .stTabs [data-baseweb="tab-list"] {
+        gap: 8px;
+    }
+    
+    .stTabs [data-baseweb="tab"] {
+        background: rgba(99, 102, 241, 0.1);
+        border-radius: 8px;
+        padding: 0.5rem 1rem;
+    }
+    
+    .stTabs [aria-selected="true"] {
+        background: linear-gradient(90deg, #6366f1, #8b5cf6);
+    }
+</style>
+""", unsafe_allow_html=True)
+
+# Initialize session state
+if 'current_page' not in st.session_state:
+    st.session_state.current_page = 'home'
+if 'uploaded_data' not in st.session_state:
+    st.session_state.uploaded_data = None
+if 'processed_data_path' not in st.session_state:
+    st.session_state.processed_data_path = None
+if 'model_path' not in st.session_state:
+    st.session_state.model_path = None
+if 'training_goal' not in st.session_state:
+    st.session_state.training_goal = None
+if 'pipeline_status' not in st.session_state:
+    st.session_state.pipeline_status = {
+        'data': 'pending',
+        'training': 'pending',
+        'evaluation': 'pending',
+        'deployment': 'pending'
+    }
+
+# Sidebar navigation
+with st.sidebar:
+    st.markdown('<p class="gradient-header" style="font-size: 1.5rem;">🤖 Auto-FineTune-Ops</p>', unsafe_allow_html=True)
+    st.markdown("---")
+    
+    # Navigation
+    pages = {
+        'home': ('🏠', 'Dashboard'),
+        'data': ('📊', 'Data Upload'),
+        'process': ('🧹', 'Processing'),
+        'training': ('🚀', 'Training'),
+        'evaluation': ('⚖️', 'Evaluation'),
+        'deploy': ('🌐', 'Deploy')
+    }
+    
+    for key, (icon, label) in pages.items():
+        if st.button(f"{icon} {label}", key=f"nav_{key}", use_container_width=True):
+            st.session_state.current_page = key
+    
+    st.markdown("---")
+    
+    # Pipeline status
+    st.markdown("### 📋 Pipeline Status")
+    status_icons = {'pending': '⏳', 'running': '🔄', 'complete': '✅', 'error': '❌'}
+    for stage, status in st.session_state.pipeline_status.items():
+        st.markdown(f"{status_icons.get(status, '⏳')} **{stage.title()}**: {status}")
+    
+    st.markdown("---")
+    st.markdown("*Built with ❤️ using Streamlit*")
+
+
+# ============================================================================
+# PAGE: HOME DASHBOARD
+# ============================================================================
+def render_home():
+    st.markdown('<p class="gradient-header">🏠 Pipeline Dashboard</p>', unsafe_allow_html=True)
+    st.markdown("**One-click autonomous ML fine-tuning pipeline**")
+    
+    # Status cards
+    col1, col2, col3, col4 = st.columns(4)
+    
+    with col1:
+        st.metric(
+            label="📊 Dataset",
+            value="Ready" if st.session_state.uploaded_data is not None else "Not Loaded",
+            delta="Uploaded" if st.session_state.uploaded_data is not None else None
+        )
+    
+    with col2:
+        st.metric(
+            label="🧹 Processing",
+            value=st.session_state.pipeline_status['data'].title(),
+            delta="Complete" if st.session_state.pipeline_status['data'] == 'complete' else None
+        )
+    
+    with col3:
+        st.metric(
+            label="🚀 Training",
+            value=st.session_state.pipeline_status['training'].title(),
+            delta="Complete" if st.session_state.pipeline_status['training'] == 'complete' else None
+        )
+    
+    with col4:
+        st.metric(
+            label="⚖️ Evaluation",
+            value=st.session_state.pipeline_status['evaluation'].title(),
+            delta="Complete" if st.session_state.pipeline_status['evaluation'] == 'complete' else None
+        )
+    
+    st.markdown("---")
+    
+    # Quick start guide
+    st.markdown("### 🚀 Quick Start Guide")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("""
+        <div class="info-card">
+            <h4>📊 Step 1: Upload Data</h4>
+            <p>Upload your CSV/JSON dataset with instruction-response pairs.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("""
+        <div class="info-card">
+            <h4>🧹 Step 2: Process Data</h4>
+            <p>The DataArchitectAgent will clean and format your data.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    with col2:
+        st.markdown("""
+        <div class="info-card">
+            <h4>🚀 Step 3: Train Model</h4>
+            <p>Fine-tune with auto-configured hyperparameters.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        st.markdown("""
+        <div class="info-card">
+            <h4>⚖️ Step 4: Evaluate</h4>
+            <p>Run Model Arena with LLM-as-Judge evaluation.</p>
+        </div>
+        """, unsafe_allow_html=True)
+    
+    # Recent output files
+    st.markdown("---")
+    st.markdown("### 📁 Output Files")
+    
+    output_dir = Path("./output")
+    if output_dir.exists():
+        tabs = st.tabs(["📂 Models", "📊 Reports", "📝 Logs"])
+        
+        with tabs[0]:
+            models_dir = output_dir / "models"
+            if models_dir.exists():
+                models = list(models_dir.glob("*"))
+                if models:
+                    for model in models[:5]:
+                        st.markdown(f"- 🤖 `{model.name}`")
+                else:
+                    st.info("No trained models yet.")
+            else:
+                st.info("Models directory not found.")
+        
+        with tabs[1]:
+            reports_dir = output_dir / "reports"
+            if reports_dir.exists():
+                reports = list(reports_dir.glob("*.json"))
+                if reports:
+                    for report in reports[:5]:
+                        st.markdown(f"- 📊 `{report.name}`")
+                else:
+                    st.info("No evaluation reports yet.")
+            else:
+                st.info("Reports directory not found.")
+        
+        with tabs[2]:
+            logs_dir = output_dir / "logs"
+            if logs_dir.exists():
+                logs = list(logs_dir.glob("*.yaml"))
+                if logs:
+                    for log in logs[:5]:
+                        st.markdown(f"- 📝 `{log.name}`")
+                else:
+                    st.info("No log files yet.")
+            else:
+                st.info("Logs directory not found.")
+    else:
+        st.info("Output directory will be created when you run the pipeline.")
+
+
+# ============================================================================
+# PAGE: DATA UPLOAD
+# ============================================================================
+def render_data_upload():
+    st.markdown('<p class="gradient-header">📊 Data Upload & Preview</p>', unsafe_allow_html=True)
+
+    # ── File Management Bar ──
+    if st.session_state.uploaded_data is not None:
+        fm1, fm2, fm3 = st.columns([3, 1, 1])
+        with fm1:
+            st.info(f"📂 Currently loaded: **{st.session_state.get('uploaded_filename', 'dataset')}** ({len(st.session_state.uploaded_data):,} rows)")
+        with fm2:
+            if st.button("🗑️ Remove Dataset", type="secondary"):
+                st.session_state.uploaded_data = None
+                st.session_state.uploaded_filename = None
+                st.session_state.processed_data_path = None
+                st.session_state.pipeline_status['data'] = 'pending'
+                st.rerun()
+        with fm3:
+            if st.button("📎 Add More Data"):
+                st.session_state['show_add_file'] = True
+
+    # ── File Uploader ──
+    show_uploader = (st.session_state.uploaded_data is None) or st.session_state.get('show_add_file', False)
+
+    if show_uploader:
+        upload_label = "Upload your dataset (CSV, JSON, or JSONL)" if st.session_state.uploaded_data is None else "Upload additional file to merge with current dataset"
+        uploaded_file = st.file_uploader(
+            upload_label,
+            type=['csv', 'json', 'jsonl'],
+            help="Your dataset should contain instruction-response pairs.",
+            key=f"uploader_{st.session_state.get('upload_counter', 0)}"
+        )
+
+        if uploaded_file:
+            try:
+                if uploaded_file.name.endswith('.csv'):
+                    new_df = pd.read_csv(uploaded_file)
+                elif uploaded_file.name.endswith('.jsonl'):
+                    new_df = pd.read_json(uploaded_file, lines=True)
+                else:
+                    new_df = pd.read_json(uploaded_file)
+
+                # Merge or replace
+                if st.session_state.uploaded_data is not None and st.session_state.get('show_add_file', False):
+                    existing_df = st.session_state.uploaded_data
+                    if list(new_df.columns) == list(existing_df.columns):
+                        st.session_state.uploaded_data = pd.concat([existing_df, new_df], ignore_index=True)
+                        st.session_state.uploaded_filename = f"{st.session_state.get('uploaded_filename', 'data')} + {uploaded_file.name}"
+                        st.success(f"✅ Merged **{uploaded_file.name}** ({len(new_df):,} rows) → Total: **{len(st.session_state.uploaded_data):,}** rows")
+                    else:
+                        st.error(f"❌ Column mismatch! Existing: {list(existing_df.columns)} vs New: {list(new_df.columns)}")
+                else:
+                    st.session_state.uploaded_data = new_df
+                    st.session_state.uploaded_filename = uploaded_file.name
+                    st.success(f"✅ Successfully loaded **{uploaded_file.name}**")
+
+                st.session_state['show_add_file'] = False
+                st.session_state['upload_counter'] = st.session_state.get('upload_counter', 0) + 1
+
+            except Exception as e:
+                st.error(f"Error loading file: {str(e)}")
+
+    # ── Data Display ──
+    if st.session_state.uploaded_data is not None:
+        df = st.session_state.uploaded_data
+
+        # Dataset statistics
+        st.markdown("### 📈 Dataset Statistics")
+        col1, col2, col3, col4 = st.columns(4)
+        with col1:
+            st.metric("Total Rows", f"{len(df):,}")
+        with col2:
+            st.metric("Total Columns", len(df.columns))
+        with col3:
+            total_bytes = df.memory_usage(deep=True).sum()
+            st.metric("Memory Size", f"{total_bytes / 1024:.1f} KB")
+        with col4:
+            missing = df.isnull().sum().sum()
+            st.metric("Missing Values", missing)
+
+        st.markdown("---")
+
+        # Column detection
+        st.markdown("### 🔍 Auto-Detected Columns")
+        instruction_patterns = ['instruction', 'prompt', 'question', 'query', 'user', 'input_text']
+        output_patterns = ['output', 'response', 'answer', 'completion', 'assistant', 'target']
+
+        detected_instruction = None
+        detected_output = None
+
+        for col in df.columns:
+            col_lower = col.lower()
+            for pattern in instruction_patterns:
+                if pattern in col_lower and not detected_instruction:
+                    detected_instruction = col
+            for pattern in output_patterns:
+                if pattern in col_lower and not detected_output:
+                    detected_output = col
+
+        col1, col2 = st.columns(2)
+        with col1:
+            if detected_instruction:
+                st.markdown(f'<span class="success-badge">Instruction: {detected_instruction}</span>', unsafe_allow_html=True)
+            else:
+                st.markdown(f'<span class="warning-badge">Instruction: Not detected</span>', unsafe_allow_html=True)
+        with col2:
+            if detected_output:
+                st.markdown(f'<span class="success-badge">Output: {detected_output}</span>', unsafe_allow_html=True)
+            else:
+                st.markdown(f'<span class="warning-badge">Output: Not detected</span>', unsafe_allow_html=True)
+
+        st.markdown("---")
+
+        # Full data preview (scrollable)
+        st.markdown("### 👀 Complete Data Preview")
+        st.caption(f"Showing all **{len(df):,}** rows. Scroll to browse the full dataset.")
+        st.dataframe(df, use_container_width=True, height=450)
+
+        # Download raw data
+        st.markdown("### 📥 Download Dataset")
+        dl1, dl2 = st.columns(2)
+        with dl1:
+            csv_data = df.to_csv(index=False).encode('utf-8')
+            st.download_button("⬇️ Download as CSV", csv_data,
+                file_name=f"{st.session_state.get('uploaded_filename', 'dataset').rsplit('.', 1)[0]}.csv",
+                mime="text/csv")
+        with dl2:
+            json_data = df.to_json(orient='records', indent=2).encode('utf-8')
+            st.download_button("⬇️ Download as JSON", json_data,
+                file_name=f"{st.session_state.get('uploaded_filename', 'dataset').rsplit('.', 1)[0]}.json",
+                mime="application/json")
+
+        # Column summary
+        st.markdown("### 📋 Column Summary")
+        col_info = []
+        for col in df.columns:
+            col_info.append({
+                'Column': col,
+                'Type': str(df[col].dtype),
+                'Non-Null': df[col].notna().sum(),
+                'Unique': df[col].nunique(),
+                'Sample': str(df[col].iloc[0])[:80] + '...' if len(str(df[col].iloc[0])) > 80 else str(df[col].iloc[0])
+            })
+        st.dataframe(pd.DataFrame(col_info), use_container_width=True)
+
+
+# ============================================================================
+# PAGE: DATA PROCESSING
+# ============================================================================
+def render_processing():
+    st.markdown('<p class="gradient-header">🧹 Advanced Data Processing</p>', unsafe_allow_html=True)
+
+    if st.session_state.uploaded_data is None:
+        st.warning("⚠️ Please upload a dataset first!")
+        if st.button("📊 Go to Data Upload"):
+            st.session_state.current_page = 'data'
+            st.rerun()
+        return
+
+    df = st.session_state.uploaded_data
+
+    # ── Dataset Stats Header ──
+    st.markdown("### 📈 Dataset Statistics")
+    sc1, sc2, sc3, sc4 = st.columns(4)
+    with sc1:
+        st.metric("Total Rows", f"{len(df):,}")
+    with sc2:
+        st.metric("Columns", len(df.columns))
+    with sc3:
+        avg_len = int(df.iloc[:, 0].astype(str).str.len().mean()) if len(df) > 0 else 0
+        st.metric("Avg Text Length", f"{avg_len:,} chars")
+    with sc4:
+        est_tokens = int(avg_len * len(df) / 4) if avg_len > 0 else 0
+        st.metric("Est. Total Tokens", f"{est_tokens:,}")
+
+    st.markdown("---")
+
+    # ── Training Goal ──
+    goal = st.text_input(
+        "Training Goal",
+        value=st.session_state.training_goal or "assistant",
+        help="e.g., medical_assistant, customer_support, code_helper"
+    )
+    st.session_state.training_goal = goal
+
+    # ── Column Mapping ──
+    st.markdown("### 🎯 Column Mapping")
+    instruction_patterns = ['instruction', 'prompt', 'question', 'query', 'user', 'input_text', 'human']
+    output_patterns = ['output', 'response', 'answer', 'completion', 'assistant', 'target']
+    input_patterns = ['context', 'input', 'background', 'reference']
+
+    detected_instruction = detected_output = detected_input = None
+    available_columns = list(df.columns)
+
+    for col in available_columns:
+        col_lower = col.lower()
+        for p in instruction_patterns:
+            if p in col_lower and not detected_instruction:
+                detected_instruction = col
+        for p in output_patterns:
+            if p in col_lower and not detected_output:
+                detected_output = col
+        for p in input_patterns:
+            if p in col_lower and not detected_input:
+                detected_input = col
+
+    mc1, mc2, mc3 = st.columns(3)
+    with mc1:
+        instruction_col = st.selectbox("Instruction Column *", options=available_columns,
+            index=available_columns.index(detected_instruction) if detected_instruction else 0,
+            help="Column containing instructions/prompts/questions")
+    with mc2:
+        output_col = st.selectbox("Output Column *", options=available_columns,
+            index=available_columns.index(detected_output) if detected_output else (1 if len(available_columns) > 1 else 0),
+            help="Column containing responses/answers/outputs")
+    with mc3:
+        input_col_options = ["None"] + available_columns
+        default_input_idx = input_col_options.index(detected_input) if detected_input else 0
+        input_col_selection = st.selectbox("Input/Context Column (Optional)", options=input_col_options,
+            index=default_input_idx, help="Optional column containing additional context")
+        input_col = None if input_col_selection == "None" else input_col_selection
+
+    st.markdown("---")
+
+    # ── Safe Preset Button ──
+    if st.button("🛡️ Load Safe Preset", help="Apply recommended defaults for most datasets"):
+        st.session_state['safe_preset'] = True
+        st.rerun()
+
+    use_safe = st.session_state.get('safe_preset', False)
+
+    # ====================================================================
+    # 1️⃣ Text Cleaning Controls
+    # ====================================================================
+    with st.expander("1️⃣ Text Cleaning Controls", expanded=False):
+        tc1, tc2 = st.columns(2)
+        with tc1:
+            clean_html = st.checkbox("Remove HTML Tags", value=use_safe, help="Strip all HTML/XML tags from text")
+            clean_urls = st.checkbox("Remove URLs", value=use_safe, help="Remove http/https/www links")
+            clean_emojis = st.checkbox("Remove Emojis", value=False, help="Strip emoji characters")
+            clean_whitespace = st.checkbox("Normalize Whitespace", value=True, help="Collapse multiple spaces/tabs into one")
+        with tc2:
+            clean_lowercase = st.checkbox("Lowercase All Text", value=False, help="Convert text to lowercase (disable to preserve case)")
+            clean_special = st.checkbox("Remove Special Characters", value=False, help="Keep only alphanumeric + basic punctuation")
+            clean_linebreaks = st.checkbox("Strip Extra Line Breaks", value=True, help="Reduce 3+ newlines to double newlines")
+
+    # ====================================================================
+    # 2️⃣ Tokenization Controls
+    # ====================================================================
+    with st.expander("2️⃣ Tokenization Controls", expanded=False):
+        tk1, tk2 = st.columns(2)
+        with tk1:
+            tokenizer_choice = st.selectbox("Tokenizer", ["tiktoken", "HuggingFace"],
+                help="tiktoken = OpenAI-compatible, HuggingFace = model-specific tokenizer")
+            if tokenizer_choice == "HuggingFace":
+                hf_model_name = st.text_input("HF Model Name", value="meta-llama/Llama-3-8b",
+                    help="HuggingFace model name for tokenizer")
+            else:
+                hf_model_name = ""
+            max_total_tokens = st.slider("Max Tokens per Sample", 128, 8192, 2048,
+                help="Maximum total tokens allowed per sample")
+        with tk2:
+            truncate_long = st.checkbox("Truncate Long Samples", value=False,
+                help="Cut text exceeding max tokens")
+            split_long = st.checkbox("Split Long Samples into Chunks", value=False,
+                help="Break long texts into overlapping chunks")
+            if split_long:
+                split_overlap = st.slider("Chunk Overlap Tokens", 0, 200, 50,
+                    help="Number of overlapping tokens between chunks")
+            else:
+                split_overlap = 50
+
+        # Token stats preview
+        if st.button("📊 Show Token Stats Preview", key="token_stats_btn"):
+            with st.spinner("Counting tokens..."):
+                try:
+                    from preprocessing.tokenization import TokenizationConfig, get_tokenizer, compute_token_stats
+                    tk_cfg = TokenizationConfig(
+                        tokenizer_name="tiktoken" if tokenizer_choice == "tiktoken" else hf_model_name,
+                    )
+                    tokenizer = get_tokenizer(tk_cfg)
+                    is_tiktoken = tokenizer_choice == "tiktoken"
+                    stats_cols = [c for c in [instruction_col, output_col] if c in df.columns]
+                    stats = compute_token_stats(df.head(200), stats_cols, tokenizer, is_tiktoken)
+                    for col_name, s in stats.items():
+                        st.markdown(f"**{col_name}**: min={s['min']}, max={s['max']}, mean={s['mean']}, p95={s['p95']}")
+                except Exception as e:
+                    st.warning(f"Could not compute token stats: {e}")
+
+    # ====================================================================
+    # 3️⃣ System Prompt Configuration
+    # ====================================================================
+    with st.expander("3️⃣ System Prompt Configuration", expanded=False):
+        system_prompt_text = st.text_area("Global System Prompt",
+            value="You are a helpful AI assistant." if not use_safe else "You are a helpful AI assistant.",
+            height=100, help="System prompt prepended to every sample in chat format")
+        prepend_system = st.checkbox("Prepend System Prompt to All Samples", value=True,
+            help="Include this system prompt in all formatted entries")
+
+        if st.button("👁️ Preview Formatted Chat JSON", key="preview_chat_btn"):
+            try:
+                from preprocessing.system_prompt import preview_formatted_json
+                preview = preview_formatted_json(df, system_prompt_text, instruction_col, output_col, input_col, n=2)
+                st.code(preview, language="json")
+            except Exception as e:
+                st.warning(f"Preview error: {e}")
+
+    # ====================================================================
+    # 4️⃣ Dataset Balancing
+    # ====================================================================
+    with st.expander("4️⃣ Dataset Balancing (Classification)", expanded=False):
+        balance_enabled = st.checkbox("Enable Class Balancing", value=False,
+            help="Balance class distribution for classification tasks")
+        if balance_enabled:
+            label_col_options = available_columns
+            label_col = st.selectbox("Label Column", options=label_col_options,
+                help="Column containing class labels")
+            balance_strategy = st.radio("Strategy", ["none", "oversample", "undersample"],
+                help="Oversample = duplicate minority, Undersample = drop majority")
+
+            # Show distribution chart
+            if label_col in df.columns:
+                from preprocessing.dataset_balancing import compute_label_distribution
+                dist = compute_label_distribution(df, label_col)
+                if dist:
+                    fig = px.bar(x=list(dist.keys()), y=list(dist.values()),
+                        labels={'x': 'Label', 'y': 'Count'}, title="Label Distribution")
+                    fig.update_layout(paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)',
+                        font_color='#e2e8f0')
+                    st.plotly_chart(fig, use_container_width=True)
+        else:
+            label_col = None
+            balance_strategy = "none"
+
+    # ====================================================================
+    # 5️⃣ Quality Filters
+    # ====================================================================
+    with st.expander("5️⃣ Quality Filters", expanded=False):
+        qf1, qf2 = st.columns(2)
+        with qf1:
+            min_words = st.number_input("Min Word Count", min_value=0, value=3 if use_safe else 0,
+                help="Minimum words required per sample (0 = no filter)")
+            max_words = st.number_input("Max Word Count", min_value=0, value=0,
+                help="Maximum words allowed per sample (0 = no limit)")
+            profanity_filter = st.checkbox("Profanity Filter", value=False,
+                help="Remove samples containing profane language")
+        with qf2:
+            language_filter = st.checkbox("Language Detection Filter", value=False,
+                help="Keep only samples in specified languages")
+            if language_filter:
+                allowed_langs = st.text_input("Allowed Languages (comma-separated)", value="en",
+                    help="ISO 639-1 codes, e.g. en,fr,de")
+            else:
+                allowed_langs = "en"
+            remove_low_quality = st.checkbox("Remove Low-Quality Responses", value=use_safe,
+                help="Remove short / generic / placeholder responses")
+
+    # ====================================================================
+    # 6️⃣ Deduplication Advanced
+    # ====================================================================
+    with st.expander("6️⃣ Deduplication", expanded=False):
+        dedup_exact = st.checkbox("Remove Exact Duplicates", value=True,
+            help="Remove rows with identical instruction text")
+        dedup_semantic = st.checkbox("Remove Semantic Duplicates", value=False,
+            help="Use TF-IDF cosine similarity to find near-duplicates")
+        if dedup_semantic:
+            semantic_threshold = st.slider("Similarity Threshold", 0.5, 1.0, 0.90, 0.01,
+                help="Cosine similarity above this threshold = duplicate (higher = stricter)")
+        else:
+            semantic_threshold = 0.90
+
+    # ====================================================================
+    # 7️⃣ Train / Validation Split
+    # ====================================================================
+    with st.expander("7️⃣ Train / Validation Split", expanded=False):
+        split_enabled = st.checkbox("Enable Train/Val Split", value=True,
+            help="Split dataset into training and validation sets")
+        if split_enabled:
+            train_ratio = st.slider("Train Ratio", 0.5, 0.95, 0.9 if use_safe else 0.8, 0.05,
+                help="Proportion of data used for training")
+            st.markdown(f"**Split**: {int(train_ratio*100)}% Train / {int((1-train_ratio)*100)}% Validation")
+            random_seed = st.number_input("Random Seed", min_value=0, value=42,
+                help="Seed for reproducible splits")
+            shuffle_data = st.checkbox("Shuffle Before Split", value=True,
+                help="Randomly shuffle data before splitting")
+        else:
+            train_ratio = 0.8
+            random_seed = 42
+            shuffle_data = True
+
+    # ====================================================================
+    # 8️⃣ Output Formatting
+    # ====================================================================
+    with st.expander("8️⃣ Output Formatting", expanded=False):
+        format_type = st.selectbox("Export Format", ["openai_chat", "completion", "classification", "custom"],
+            help="OpenAI Chat = messages format, Completion = prompt/completion, Classification = text/label")
+
+        custom_schema = {}
+        if format_type == "custom":
+            st.markdown("**Define Custom Schema** (output_key → source_column)")
+            num_fields = st.number_input("Number of Fields", 1, 10, 2)
+            for i in range(int(num_fields)):
+                fc1, fc2 = st.columns(2)
+                with fc1:
+                    key = st.text_input(f"Output Key {i+1}", value=f"field_{i+1}", key=f"ckey_{i}")
+                with fc2:
+                    val = st.selectbox(f"Source Column {i+1}", options=available_columns, key=f"cval_{i}")
+                custom_schema[key] = val
+
+    # ====================================================================
+    # 9️⃣ Safety & PII Filtering
+    # ====================================================================
+    with st.expander("9️⃣ Safety & PII Filtering", expanded=False):
+        pii1, pii2 = st.columns(2)
+        with pii1:
+            pii_emails = st.checkbox("Detect & Mask Emails", value=use_safe,
+                help="Replace email addresses with [REDACTED]")
+            pii_phones = st.checkbox("Detect & Mask Phone Numbers", value=use_safe,
+                help="Replace phone numbers with [REDACTED]")
+            pii_ids = st.checkbox("Detect & Mask CNIC/SSN", value=use_safe,
+                help="Replace national ID / SSN patterns with [REDACTED]")
+        with pii2:
+            pii_keys = st.checkbox("Detect & Mask API Keys", value=use_safe,
+                help="Replace long hex/base64 strings that look like secrets")
+            pii_addresses = st.checkbox("Detect & Mask Addresses", value=False,
+                help="Replace street addresses and zip codes")
+
+    # ====================================================================
+    # 🔟 Augmentation (Optional)
+    # ====================================================================
+    with st.expander("🔟 Augmentation (Optional)", expanded=False):
+        aug_enabled = st.checkbox("Enable Data Augmentation", value=False,
+            help="Generate synthetic variations of existing samples")
+        if aug_enabled:
+            ag1, ag2 = st.columns(2)
+            with ag1:
+                aug_paraphrase = st.checkbox("Paraphrase Instructions", value=True,
+                    help="Synonym-based paraphrasing of instructions")
+                aug_variations = st.checkbox("Generate Variations", value=False,
+                    help="Minor text variations (punctuation, casing)")
+            with ag2:
+                aug_backtranslate = st.checkbox("Back Translation", value=False,
+                    help="Simulate back-translation for diversity")
+                aug_tone = st.checkbox("Tone Rewriting", value=False,
+                    help="Rewrite instructions in different tones")
+            aug_factor = st.slider("Augmentation Factor", 1, 5, 1,
+                help="Number of augmented copies per original sample")
+        else:
+            aug_paraphrase = aug_variations = aug_backtranslate = aug_tone = False
+            aug_factor = 1
+
+    st.markdown("---")
+
+    # ── Run Pipeline Button ──
+    if st.button("🚀 Run Advanced Processing Pipeline", type="primary", use_container_width=True):
+        st.session_state.pipeline_status['data'] = 'running'
+
+        with st.spinner("Running preprocessing pipeline..."):
+            progress_bar = st.progress(0)
+            status_text = st.empty()
+
+            try:
+                from preprocessing.pipeline import PreprocessingPipeline, PreprocessingConfig
+                from preprocessing.text_cleaning import TextCleaningConfig
+                from preprocessing.tokenization import TokenizationConfig
+                from preprocessing.system_prompt import SystemPromptConfig
+                from preprocessing.dataset_balancing import BalancingConfig
+                from preprocessing.quality_filters import QualityFilterConfig
+                from preprocessing.deduplication import DeduplicationConfig
+                from preprocessing.train_val_split import SplitConfig
+                from preprocessing.output_formatter import OutputFormatConfig, format_dataset, export_jsonl, generate_preview
+                from preprocessing.pii_filter import PIIFilterConfig
+                from preprocessing.augmentation import AugmentationConfig
+
+                # Build config from UI values
+                config = PreprocessingConfig(
+                    instruction_col=instruction_col,
+                    output_col=output_col,
+                    input_col=input_col,
+                    label_col=label_col if balance_enabled else None,
+                    text_cleaning=TextCleaningConfig(
+                        remove_html=clean_html, remove_urls=clean_urls,
+                        remove_emojis=clean_emojis, normalize_whitespace=clean_whitespace,
+                        lowercase=clean_lowercase, remove_special_chars=clean_special,
+                        strip_extra_linebreaks=clean_linebreaks,
+                    ),
+                    tokenization=TokenizationConfig(
+                        tokenizer_name="tiktoken" if tokenizer_choice == "tiktoken" else hf_model_name,
+                        max_total_tokens=max_total_tokens,
+                        truncate_long=truncate_long, split_long=split_long,
+                        split_overlap=split_overlap,
+                    ),
+                    system_prompt=SystemPromptConfig(
+                        system_prompt=system_prompt_text,
+                        prepend_to_all=prepend_system,
+                    ),
+                    balancing=BalancingConfig(
+                        enabled=balance_enabled,
+                        label_column=label_col if balance_enabled else "",
+                        strategy=balance_strategy if balance_enabled else "none",
+                    ),
+                    quality_filters=QualityFilterConfig(
+                        min_word_count=min_words, max_word_count=max_words,
+                        profanity_filter=profanity_filter,
+                        language_filter=language_filter,
+                        allowed_languages=[l.strip() for l in allowed_langs.split(',')],
+                        remove_low_quality=remove_low_quality,
+                    ),
+                    deduplication=DeduplicationConfig(
+                        remove_exact=dedup_exact, remove_semantic=dedup_semantic,
+                        semantic_threshold=semantic_threshold,
+                    ),
+                    split=SplitConfig(
+                        enabled=split_enabled, train_ratio=train_ratio,
+                        random_seed=int(random_seed), shuffle=shuffle_data,
+                    ),
+                    output_format=OutputFormatConfig(
+                        format_type=format_type, custom_schema=custom_schema,
+                    ),
+                    pii_filter=PIIFilterConfig(
+                        filter_emails=pii_emails, filter_phones=pii_phones,
+                        filter_id_numbers=pii_ids, filter_api_keys=pii_keys,
+                        filter_addresses=pii_addresses,
+                    ),
+                    augmentation=AugmentationConfig(
+                        enabled=aug_enabled, paraphrase=aug_paraphrase,
+                        generate_variations=aug_variations,
+                        back_translate=aug_backtranslate,
+                        tone_rewrite=aug_tone,
+                        augmentation_factor=aug_factor,
+                    ),
+                )
+
+                def progress_cb(stage_name, pct):
+                    status_text.text(f"⚙️ {stage_name}...")
+                    progress_bar.progress(min(pct, 100))
+
+                pipeline = PreprocessingPipeline(config)
+                train_df, val_df, logs = pipeline.run(df, progress_callback=progress_cb)
+
+                # Format output
+                sys_prompt = system_prompt_text if prepend_system else ""
+                formatted_data = format_dataset(
+                    train_df, config.output_format,
+                    system_prompt=sys_prompt,
+                    instruction_col=instruction_col,
+                    output_col=output_col,
+                    input_col=input_col,
+                    label_col=label_col if balance_enabled else None,
+                )
+
+                # Export
+                output_dir = Path("./output/processed_data")
+                output_dir.mkdir(parents=True, exist_ok=True)
+                train_path = export_jsonl(formatted_data, str(output_dir / f"{goal}_train.jsonl"))
+
+                val_path = None
+                if len(val_df) > 0:
+                    val_formatted = format_dataset(
+                        val_df, config.output_format,
+                        system_prompt=sys_prompt,
+                        instruction_col=instruction_col,
+                        output_col=output_col,
+                        input_col=input_col,
+                        label_col=label_col if balance_enabled else None,
+                    )
+                    val_path = export_jsonl(val_formatted, str(output_dir / f"{goal}_val.jsonl"))
+
+                progress_bar.progress(100)
+                status_text.text("✅ Pipeline complete!")
+
+                st.session_state.processed_data_path = train_path
+                st.session_state.pipeline_status['data'] = 'complete'
+
+                # ── Results ──
+                st.success(f"✅ Training data saved to: `{train_path}`")
+                if val_path:
+                    st.success(f"✅ Validation data saved to: `{val_path}`")
+
+                # Stats
+                rc1, rc2, rc3, rc4 = st.columns(4)
+                with rc1:
+                    st.metric("Original Rows", f"{len(df):,}")
+                with rc2:
+                    st.metric("Train Samples", f"{len(train_df):,}")
+                with rc3:
+                    st.metric("Val Samples", f"{len(val_df):,}")
+                with rc4:
+                    removed = len(df) - len(train_df) - len(val_df)
+                    st.metric("Removed", f"{max(0, removed):,}")
+
+                # ── Pipeline Logs ──
+                st.markdown("### 📋 Pipeline Logs")
+                log_data = []
+                for log in logs:
+                    log_data.append({
+                        'Stage': log.stage,
+                        'Description': log.description,
+                        'Rows Before': log.rows_before,
+                        'Rows After': log.rows_after,
+                        'Delta': log.rows_delta,
+                        'Time (ms)': log.duration_ms,
+                    })
+                st.dataframe(pd.DataFrame(log_data), use_container_width=True)
+
+                # ── Preview ──
+                st.markdown("### 👁️ Output Preview")
+                preview_json = generate_preview(formatted_data, n=3)
+                st.code(preview_json, language="json")
+
+                # ── Download ──
+                st.markdown("### 📥 Download")
+                dl1, dl2 = st.columns(2)
+                with dl1:
+                    with open(train_path, 'r', encoding='utf-8') as f:
+                        st.download_button("⬇️ Download Train JSONL", f.read(),
+                            file_name=f"{goal}_train.jsonl", mime="application/jsonl")
+                with dl2:
+                    if val_path and Path(val_path).exists():
+                        with open(val_path, 'r', encoding='utf-8') as f:
+                            st.download_button("⬇️ Download Val JSONL", f.read(),
+                                file_name=f"{goal}_val.jsonl", mime="application/jsonl")
+
+            except Exception as e:
+                st.session_state.pipeline_status['data'] = 'error'
+                st.error(f"❌ Pipeline Error: {str(e)}")
+                import traceback
+                st.code(traceback.format_exc())
+
+    # Show previously processed data
+    if st.session_state.processed_data_path:
+        st.markdown("---")
+        st.markdown("### 📂 Last Processed Data")
+        try:
+            processed_path = Path(st.session_state.processed_data_path)
+            if processed_path.exists():
+                with open(processed_path, encoding='utf-8') as f:
+                    samples = [json.loads(line) for line in f.readlines()[:5]]
+                for i, sample in enumerate(samples):
+                    with st.expander(f"Sample {i+1}"):
+                        st.json(sample)
+        except Exception as e:
+            st.warning(f"Could not load preview: {e}")
+
+
+# ============================================================================
+# PAGE: TRAINING
+# ============================================================================
+def render_training():
+    st.markdown('<p class="gradient-header">🚀 Model Training</p>', unsafe_allow_html=True)
+
+    # Check prerequisites
+    if st.session_state.processed_data_path is None:
+        st.warning("⚠️ Please process your data first!")
+        if st.button("🧹 Go to Processing"):
+            st.session_state.current_page = 'process'
+            st.rerun()
+        return
+
+    # ── GPU Detection ──
+    try:
+        import torch
+        has_gpu = torch.cuda.is_available()
+        if has_gpu:
+            gpu_name = torch.cuda.get_device_name(0)
+            gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1e9
+            st.success(f"✅ GPU Available: **{gpu_name}** ({gpu_memory:.1f} GB)")
+    except Exception:
+        has_gpu = False
+
+    # ── Download Preprocessed Data (always available) ──
+    st.markdown("### 📥 Preprocessed Training Data")
+    processed_path = Path(st.session_state.processed_data_path)
+    if processed_path.exists():
+        with open(processed_path, 'r', encoding='utf-8') as f:
+            processed_content = f.read()
+        dl1, dl2 = st.columns(2)
+        with dl1:
+            st.download_button("⬇️ Download Training JSONL", processed_content,
+                file_name=processed_path.name, mime="application/jsonl")
+        with dl2:
+            # Check for validation file
+            val_path = processed_path.parent / processed_path.name.replace('_train', '_val')
+            if val_path.exists():
+                with open(val_path, 'r', encoding='utf-8') as f:
+                    st.download_button("⬇️ Download Validation JSONL", f.read(),
+                        file_name=val_path.name, mime="application/jsonl")
+        try:
+            sample_count = sum(1 for _ in processed_content.split('\n') if _.strip())
+        except Exception:
+            sample_count = 0
+        st.info(f"📊 Dataset: **{sample_count:,}** samples ready for training")
+    else:
+        st.warning("Processed data file not found.")
+
+    st.markdown("---")
+
+    # ====================================================================
+    # TWO PATHS: GPU Training OR Colab Notebook
+    # ====================================================================
+    if has_gpu:
+        training_mode = "gpu"
+    else:
+        training_mode = st.radio("🖥️ Select Training Mode", [
+            "☁️ Use Google Colab (Recommended – Free GPU)",
+            "📤 Upload Fine-Tuned Model (Already trained externally)"
+        ], help="No GPU detected on this machine. Choose how to proceed.")
+
+    # ====================================================================
+    # PATH A: GPU Training (local)
+    # ====================================================================
+    if training_mode == "gpu":
+        st.markdown("### ⚙️ Training Configuration")
+
+        col1, col2 = st.columns(2)
+        with col1:
+            model_source = st.radio("Model Source", ["Preset Models", "Custom HuggingFace Model"])
+            if model_source == "Preset Models":
+                base_model = st.selectbox("Base Model", [
+                    "unsloth/llama-3-8b-bnb-4bit",
+                    "unsloth/llama-3-70b-bnb-4bit",
+                    "unsloth/mistral-7b-bnb-4bit",
+                    "unsloth/gemma-7b-bnb-4bit",
+                ])
+            else:
+                base_model = st.text_input("HuggingFace Model ID",
+                    value="unsloth/llama-3-8b-bnb-4bit",
+                    help="Enter any HuggingFace model ID, e.g. 'meta-llama/Llama-3-8b', 'mistralai/Mistral-7B-v0.1'")
+            max_seq_length = st.slider("Max Sequence Length", 512, 4096, 2048)
+
+        with col2:
+            dataset_size = sample_count if sample_count > 0 else 1000
+            if dataset_size < 1000:
+                auto_rank, auto_alpha, auto_lr, auto_epochs = 8, 16, 2e-4, 5
+                size_category = "Small"
+            elif dataset_size < 10000:
+                auto_rank, auto_alpha, auto_lr, auto_epochs = 16, 32, 1e-4, 3
+                size_category = "Medium"
+            else:
+                auto_rank, auto_alpha, auto_lr, auto_epochs = 32, 64, 5e-5, 2
+                size_category = "Large"
+            st.success(f"Auto-configured for **{size_category}** dataset ({dataset_size:,} samples)")
+
+        st.markdown("---")
+
+        with st.expander("🔧 Advanced Hyperparameters"):
+            hc1, hc2, hc3 = st.columns(3)
+            with hc1:
+                lora_rank = st.slider("LoRA Rank", 4, 64, auto_rank)
+                lora_alpha = st.slider("LoRA Alpha", 8, 128, auto_alpha)
+            with hc2:
+                learning_rate = st.select_slider("Learning Rate",
+                    options=[1e-5, 2e-5, 5e-5, 1e-4, 2e-4, 5e-4], value=auto_lr)
+                num_epochs = st.slider("Epochs", 1, 10, auto_epochs)
+            with hc3:
+                batch_size = st.slider("Batch Size", 1, 16, 4)
+                gradient_accumulation = st.slider("Gradient Accumulation", 1, 8, 4)
+
+        st.markdown("---")
+
+        col1, col2, col3 = st.columns([1, 2, 1])
+        with col2:
+            if st.button("🚀 Start Training", type="primary", use_container_width=True):
+                st.session_state.pipeline_status['training'] = 'running'
+                with st.spinner("Training in progress..."):
+                    progress_bar = st.progress(0)
+                    status_text = st.empty()
+                    try:
+                        from agents.training_pilot import TrainingPilot, HyperParams
+                        status_text.text("📦 Loading model...")
+                        progress_bar.progress(10)
+                        pilot = TrainingPilot(
+                            base_model=base_model,
+                            max_seq_length=max_seq_length,
+                            output_dir="./output/models"
+                        )
+                        status_text.text("🚀 Training...")
+                        progress_bar.progress(30)
+                        result = pilot.run(
+                            data_path=st.session_state.processed_data_path,
+                            output_name=st.session_state.training_goal
+                        )
+                        progress_bar.progress(100)
+                        status_text.text("✅ Training complete!")
+                        st.session_state.model_path = result.model_path
+                        st.session_state.pipeline_status['training'] = 'complete'
+                        st.success(f"✅ Model saved to: `{result.model_path}`")
+                        rc1, rc2, rc3 = st.columns(3)
+                        with rc1:
+                            st.metric("Final Loss", f"{result.final_loss:.4f}")
+                        with rc2:
+                            st.metric("Training Time", f"{result.training_time:.1f}s")
+                        with rc3:
+                            st.metric("Total Steps", result.num_steps)
+                    except Exception as e:
+                        st.session_state.pipeline_status['training'] = 'error'
+                        st.error(f"❌ Training failed: {str(e)}")
+                        import traceback
+                        st.code(traceback.format_exc())
+
+    # ====================================================================
+    # PATH B: Google Colab Notebook
+    # ====================================================================
+    elif "Colab" in training_mode:
+        st.markdown("### ☁️ Train on Google Colab (Free GPU)")
+        st.markdown("""
+        Since no GPU was detected on this machine, you can fine-tune your model on Google Colab with a free GPU.
+        Follow these steps:
+        """)
+
+        st.markdown("""
+        **Step 1:** Download your preprocessed training data (above) ⬆️
+
+        **Step 2:** Download or copy the Colab notebook below
+
+        **Step 3:** Open [Google Colab](https://colab.research.google.com/) → Upload the notebook
+
+        **Step 4:** Upload your training JSONL to Colab's file browser
+
+        **Step 5:** Run all cells → Download the fine-tuned model
+
+        **Step 6:** Come back here → Upload your fine-tuned model results for evaluation
+        """)
+
+        # Show / Download Colab notebook
+        notebook_path = Path("./Auto_FineTune_Ops_Colab.ipynb")
+        if notebook_path.exists():
+            with open(notebook_path, 'r', encoding='utf-8') as f:
+                notebook_content = f.read()
+
+            st.download_button("📓 Download Colab Notebook (.ipynb)", notebook_content,
+                file_name="Auto_FineTune_Ops_Colab.ipynb", mime="application/json",
+                type="primary", use_container_width=True)
+
+            with st.expander("👁️ View Notebook Code", expanded=False):
+                try:
+                    import json as json_mod
+                    nb = json_mod.loads(notebook_content)
+                    for cell in nb.get('cells', []):
+                        if cell.get('cell_type') == 'code':
+                            source = ''.join(cell.get('source', []))
+                            if source.strip():
+                                st.code(source, language='python')
+                        elif cell.get('cell_type') == 'markdown':
+                            source = ''.join(cell.get('source', []))
+                            st.markdown(source)
+                except Exception:
+                    st.code(notebook_content[:5000], language='json')
+        else:
+            st.warning("⚠️ Colab notebook not found at `Auto_FineTune_Ops_Colab.ipynb`")
+
+        st.markdown("---")
+        st.markdown("### 📤 After Training on Colab")
+        st.info("Once you've finished training on Colab, download your fine-tuned model outputs and upload them below for evaluation.")
+
+    # ====================================================================
+    # PATH C: Upload Fine-Tuned Model / Results
+    # ====================================================================
+    else:
+        st.markdown("### 📤 Upload Fine-Tuned Model Results")
+        st.markdown("Upload outputs from your externally trained model for evaluation.")
+
+    # ── Upload Fine-Tuned Results (always shown at bottom) ──
+    st.markdown("---")
+    st.markdown("### 📦 Upload Fine-Tuned Results for Evaluation")
+    st.caption("If you trained on Colab or another machine, upload your model outputs here.")
+
+    upload_tab1, upload_tab2 = st.tabs(["📊 Upload Evaluation Results (JSONL)", "📁 Upload Model Folder Path"])
+
+    with upload_tab1:
+        ft_file = st.file_uploader("Upload fine-tuned model outputs (JSONL with predictions)",
+            type=['jsonl', 'json'], key="ft_results_upload",
+            help="JSONL file with model predictions/outputs from your fine-tuned model")
+        if ft_file:
+            try:
+                ft_df = pd.read_json(ft_file, lines=ft_file.name.endswith('.jsonl'))
+                st.success(f"✅ Loaded **{len(ft_df):,}** evaluation samples")
+                st.dataframe(ft_df.head(5), use_container_width=True)
+
+                # Save for evaluation
+                eval_output = Path("./output/eval_results")
+                eval_output.mkdir(parents=True, exist_ok=True)
+                eval_path = eval_output / f"finetuned_outputs_{ft_file.name}"
+                ft_df.to_json(eval_path, orient='records', lines=True)
+
+                st.session_state.model_path = str(eval_path)
+                st.session_state.pipeline_status['training'] = 'complete'
+                st.success(f"✅ Results saved! You can now proceed to **Evaluation** page.")
+
+                if st.button("⚖️ Go to Evaluation"):
+                    st.session_state.current_page = 'evaluation'
+                    st.rerun()
+            except Exception as e:
+                st.error(f"Error loading file: {e}")
+
+    with upload_tab2:
+        model_folder = st.text_input("Model Folder Path",
+            placeholder="e.g., ./output/models/my_finetuned_model or /path/to/model",
+            help="Local path to the fine-tuned model directory (LoRA adapter or full model)")
+        if model_folder and st.button("✅ Set Model Path"):
+            if Path(model_folder).exists():
+                st.session_state.model_path = model_folder
+                st.session_state.pipeline_status['training'] = 'complete'
+                st.success(f"✅ Model path set to: `{model_folder}`")
+            else:
+                st.error(f"❌ Path not found: `{model_folder}`")
+
+
+# ============================================================================
+# PAGE: EVALUATION
+# ============================================================================
+def render_evaluation():
+    st.markdown('<p class="gradient-header">⚖️ Model Evaluation</p>', unsafe_allow_html=True)
+
+    # ── Judge Provider Selection ──
+    st.markdown("### 🤖 Select AI Judge Provider")
+    st.caption("Choose which LLM provider to use as the evaluation judge. You can use any model you have API access to.")
+
+    judge_provider = st.selectbox("AI Provider", [
+        "OpenAI (GPT-4o, GPT-4-turbo, etc.)",
+        "Anthropic (Claude 3.5, Claude 3 Opus, etc.)",
+        "Google Gemini (Gemini Pro, Gemini 1.5, etc.)",
+        "Groq (Llama, Mixtral, Gemma, etc.)",
+        "Custom OpenAI-Compatible Endpoint"
+    ], help="Select the AI provider whose model will act as the judge for evaluating your fine-tuned model.")
+
+    st.markdown("---")
+    st.markdown("### 🔑 API Configuration")
+
+    if "OpenAI" in judge_provider:
+        col1, col2 = st.columns(2)
+        with col1:
+            openai_key = st.text_input("OpenAI API Key", type="password",
+                help="Your OpenAI API key (starts with sk-)")
+            if openai_key:
+                os.environ["OPENAI_API_KEY"] = openai_key
+        with col2:
+            judge_model = st.selectbox("Judge Model", [
+                "gpt-4o", "gpt-4o-mini", "gpt-4-turbo", "gpt-4", "gpt-3.5-turbo"
+            ])
+
+    elif "Anthropic" in judge_provider:
+        col1, col2 = st.columns(2)
+        with col1:
+            anthropic_key = st.text_input("Anthropic API Key", type="password",
+                help="Your Anthropic API key")
+            if anthropic_key:
+                os.environ["ANTHROPIC_API_KEY"] = anthropic_key
+        with col2:
+            judge_model = st.selectbox("Judge Model", [
+                "claude-3-5-sonnet-20241022", "claude-3-opus-20240229",
+                "claude-3-sonnet-20240229", "claude-3-haiku-20240307"
+            ])
+
+    elif "Gemini" in judge_provider:
+        col1, col2 = st.columns(2)
+        with col1:
+            gemini_key = st.text_input("Google AI API Key", type="password",
+                help="Your Google AI Studio API key for Gemini models")
+            if gemini_key:
+                os.environ["GOOGLE_API_KEY"] = gemini_key
+        with col2:
+            judge_model = st.selectbox("Judge Model", [
+                "gemini-1.5-pro", "gemini-1.5-flash", "gemini-pro"
+            ])
+
+    elif "Groq" in judge_provider:
+        col1, col2 = st.columns(2)
+        with col1:
+            groq_key = st.text_input("Groq API Key", type="password",
+                help="Your Groq API key for fast inference")
+            if groq_key:
+                os.environ["GROQ_API_KEY"] = groq_key
+        with col2:
+            judge_model = st.selectbox("Judge Model", [
+                "llama-3.1-70b-versatile", "llama-3.1-8b-instant",
+                "mixtral-8x7b-32768", "gemma2-9b-it"
+            ])
+
+    else:  # Custom endpoint
+        col1, col2 = st.columns(2)
+        with col1:
+            custom_base_url = st.text_input("API Base URL",
+                placeholder="https://api.your-provider.com/v1",
+                help="OpenAI-compatible API endpoint (e.g., vLLM, Ollama, LM Studio)")
+            custom_api_key = st.text_input("API Key", type="password",
+                help="API key for the custom endpoint (use 'none' for local servers)")
+            if custom_api_key:
+                os.environ["OPENAI_API_KEY"] = custom_api_key
+            if custom_base_url:
+                os.environ["OPENAI_BASE_URL"] = custom_base_url
+        with col2:
+            judge_model = st.text_input("Model Name",
+                placeholder="e.g., my-model, llama-3-8b",
+                help="Model identifier used by your custom endpoint")
+
+    st.markdown("---")
+
+    # ── Model / Results to Evaluate ──
+    st.markdown("### 📊 Evaluation Data")
+
+    if st.session_state.model_path:
+        st.info(f"📦 Model / Results: `{st.session_state.model_path}`")
+    else:
+        st.warning("⚠️ No trained model or uploaded results found. You can upload evaluation data below or train a model first.")
+
+    # Upload evaluation data
+    eval_upload = st.file_uploader("Upload evaluation data (JSONL with instruction + model output)",
+        type=['jsonl', 'json'], key="eval_data_upload",
+        help="Upload a JSONL file containing instruction-response pairs to evaluate")
+    if eval_upload:
+        try:
+            eval_df = pd.read_json(eval_upload, lines=eval_upload.name.endswith('.jsonl'))
+            st.success(f"✅ Loaded **{len(eval_df):,}** samples for evaluation")
+            st.dataframe(eval_df.head(5), use_container_width=True)
+            st.session_state['eval_data'] = eval_df
+        except Exception as e:
+            st.error(f"Error loading evaluation data: {e}")
+
+    st.markdown("---")
+
+    # ── Demo Charts ──
+    st.markdown("### 📈 Evaluation Results")
+
+    col1, col2 = st.columns(2)
+    with col1:
+        fig = go.Figure(data=[go.Pie(
+            values=[72, 18, 10],
+            labels=['Fine-tuned Wins', 'Base Model Wins', 'Ties'],
+            hole=0.6,
+            marker_colors=['#6366f1', '#ef4444', '#94a3b8']
+        )])
+        fig.update_layout(
+            title="Win Rate Distribution",
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            font_color='#e2e8f0',
+            showlegend=True
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+    with col2:
+        fig = go.Figure(data=[
+            go.Bar(name='Base Model', x=['Accuracy', 'Helpfulness', 'Clarity', 'Relevance'], y=[6.2, 5.8, 6.5, 6.0], marker_color='#ef4444'),
+            go.Bar(name='Fine-tuned', x=['Accuracy', 'Helpfulness', 'Clarity', 'Relevance'], y=[7.8, 8.1, 7.5, 8.2], marker_color='#6366f1')
+        ])
+        fig.update_layout(
+            title="Score Comparison by Category",
+            barmode='group',
+            paper_bgcolor='rgba(0,0,0,0)',
+            plot_bgcolor='rgba(0,0,0,0)',
+            font_color='#e2e8f0',
+            yaxis_title="Score (1-10)"
+        )
+        st.plotly_chart(fig, use_container_width=True)
+
+    # Summary metrics
+    col1, col2, col3, col4 = st.columns(4)
+    with col1:
+        st.metric("Win Rate", "72%", "+22%")
+    with col2:
+        st.metric("Base Avg Score", "6.4/10")
+    with col3:
+        st.metric("Fine-tuned Avg", "7.8/10", "+1.4")
+    with col4:
+        st.metric("Comparisons", "50")
+
+    st.markdown("---")
+
+    # Run evaluation
+    col1, col2, col3 = st.columns([1, 2, 1])
+    with col2:
+        if st.button("🏃 Run Full Evaluation", type="primary", use_container_width=True):
+            has_key = any([
+                os.environ.get("OPENAI_API_KEY"),
+                os.environ.get("ANTHROPIC_API_KEY"),
+                os.environ.get("GOOGLE_API_KEY"),
+                os.environ.get("GROQ_API_KEY"),
+            ])
+            if not has_key:
+                st.error("❌ Please provide an API key for your selected judge provider.")
+            elif not st.session_state.model_path and not st.session_state.get('eval_data') is not None:
+                st.error("❌ Please either train a model, upload fine-tuned results, or upload evaluation data.")
+            else:
+                st.info(f"🏃 Starting evaluation with **{judge_model}** as judge...")
+                st.warning("⏳ Full evaluation pipeline integration coming soon. Demo results shown above.")
+
+
+# ============================================================================
+# PAGE: DEPLOYMENT
+# ============================================================================
+def render_deploy():
+    st.markdown('<p class="gradient-header">🌐 Model Deployment</p>', unsafe_allow_html=True)
+    
+    # Model selection
+    st.markdown("### 📦 Select Model")
+    
+    models_dir = Path("./output/models")
+    if models_dir.exists():
+        models = [d.name for d in models_dir.iterdir() if d.is_dir()]
+        if models:
+            selected_model = st.selectbox("Trained Models", models)
+            model_path = models_dir / selected_model
+            st.info(f"📂 Model path: `{model_path}`")
+        else:
+            st.warning("No trained models found.")
+            selected_model = None
+    else:
+        st.warning("Models directory not found.")
+        selected_model = None
+    
+    st.markdown("---")
+    
+    # Deployment options
+    st.markdown("### 🚀 Deployment Options")
+    
+    col1, col2 = st.columns(2)
+    
+    with col1:
+        st.markdown("""
+        <div class="info-card">
+            <h4>🖥️ Local FastAPI Server</h4>
+            <p>Deploy as a REST API on your local machine.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        port = st.number_input("Port", value=8000, min_value=1000, max_value=65535)
+        
+        if st.button("🚀 Start Server", disabled=not selected_model):
+            st.code(f"python scripts/deploy.py --model ./output/models/{selected_model} --port {port}")
+            st.info("Run the command above in your terminal to start the server.")
+    
+    with col2:
+        st.markdown("""
+        <div class="info-card">
+            <h4>☁️ HuggingFace Hub</h4>
+            <p>Push your model to HuggingFace for sharing.</p>
+        </div>
+        """, unsafe_allow_html=True)
+        
+        hf_token = st.text_input("HuggingFace Token", type="password")
+        repo_name = st.text_input("Repository Name", value=f"my-finetuned-{selected_model}" if selected_model else "")
+        
+        if st.button("☁️ Push to Hub", disabled=not selected_model or not hf_token):
+            st.info("Pushing to HuggingFace Hub...")
+    
+    st.markdown("---")
+    
+    # API documentation
+    st.markdown("### 📚 API Documentation")
+    
+    st.markdown("""
+    Once deployed, your API will have these endpoints:
+    
+    | Endpoint | Method | Description |
+    |----------|--------|-------------|
+    | `/` | GET | API info |
+    | `/health` | GET | Health check |
+    | `/generate` | POST | Generate text |
+    | `/generate/batch` | POST | Batch generation |
+    """)
+    
+    with st.expander("📝 Example Request"):
+        st.code("""
+import requests
+
+response = requests.post("http://localhost:8000/generate", json={
+    "prompt": "What are the symptoms of the common cold?",
+    "max_tokens": 256,
+    "temperature": 0.7
+})
+print(response.json()["generated_text"])
+        """, language="python")
+
+
+# ============================================================================
+# MAIN ROUTER
+# ============================================================================
+def main():
+    page = st.session_state.current_page
+    
+    if page == 'home':
+        render_home()
+    elif page == 'data':
+        render_data_upload()
+    elif page == 'process':
+        render_processing()
+    elif page == 'training':
+        render_training()
+    elif page == 'evaluation':
+        render_evaluation()
+    elif page == 'deploy':
+        render_deploy()
+    else:
+        render_home()
+
+
+if __name__ == "__main__":
+    main()

configs/default_config.yaml

@@ -0,0 +1,160 @@
+# Auto-FineTune-Ops Default Configuration
+# ========================================
+
+# Model Configuration
+model:
+  base_model: "unsloth/llama-3-8b-bnb-4bit"
+  max_seq_length: 2048
+  load_in_4bit: true
+  dtype: null  # Auto-detect
+
+# Data Processing
+data:
+  min_instruction_length: 10
+  max_instruction_length: 2048
+  min_response_length: 20
+  max_response_length: 4096
+  remove_duplicates: true
+  quality_threshold: 0.7
+
+# Advanced Preprocessing Pipeline
+preprocessing:
+  text_cleaning:
+    remove_html: true
+    remove_urls: true
+    remove_emojis: false
+    normalize_whitespace: true
+    lowercase: false
+    remove_special_chars: false
+    strip_extra_linebreaks: true
+
+  tokenization:
+    tokenizer_name: "tiktoken"
+    tiktoken_encoding: "cl100k_base"
+    max_total_tokens: 2048
+    truncate_long: false
+    split_long: false
+    split_overlap: 50
+
+  system_prompt:
+    prompt: "You are a helpful AI assistant."
+    prepend_to_all: true
+
+  balancing:
+    enabled: false
+    label_column: ""
+    strategy: "none"
+
+  quality_filters:
+    min_word_count: 3
+    max_word_count: 0
+    profanity_filter: false
+    language_filter: false
+    allowed_languages: ["en"]
+    remove_low_quality: true
+    min_quality_length: 20
+
+  deduplication:
+    remove_exact: true
+    remove_semantic: false
+    semantic_threshold: 0.90
+
+  split:
+    enabled: true
+    train_ratio: 0.9
+    random_seed: 42
+    shuffle: true
+
+  output_format:
+    format_type: "openai_chat"
+
+  pii_filter:
+    filter_emails: true
+    filter_phones: true
+    filter_id_numbers: true
+    filter_api_keys: true
+    filter_addresses: false
+    mask_char: "[REDACTED]"
+
+  augmentation:
+    enabled: false
+    paraphrase: false
+    generate_variations: false
+    back_translate: false
+    tone_rewrite: false
+    augmentation_factor: 1
+
+
+# Training Hyperparameters (Auto-configured based on dataset size)
+training:
+  # Small datasets (<1K samples)
+  small:
+    lora_rank: 8
+    lora_alpha: 16
+    learning_rate: 2.0e-4
+    num_epochs: 5
+    batch_size: 4
+    gradient_accumulation_steps: 4
+  
+  # Medium datasets (1K-10K samples)
+  medium:
+    lora_rank: 16
+    lora_alpha: 32
+    learning_rate: 1.0e-4
+    num_epochs: 3
+    batch_size: 8
+    gradient_accumulation_steps: 2
+  
+  # Large datasets (>10K samples)
+  large:
+    lora_rank: 32
+    lora_alpha: 64
+    learning_rate: 5.0e-5
+    num_epochs: 2
+    batch_size: 16
+    gradient_accumulation_steps: 1
+
+  # Common settings
+  common:
+    warmup_ratio: 0.03
+    weight_decay: 0.01
+    optimizer: "adamw_8bit"
+    lr_scheduler: "cosine"
+    gradient_checkpointing: true
+    max_grad_norm: 1.0
+
+# LoRA Configuration
+lora:
+  target_modules:
+    - "q_proj"
+    - "k_proj"
+    - "v_proj"
+    - "o_proj"
+    - "gate_proj"
+    - "up_proj"
+    - "down_proj"
+  lora_dropout: 0.0
+  bias: "none"
+  use_rslora: true
+
+# Evaluation (TheJudge)
+evaluation:
+  judge_model: "gpt-4o"  # Options: gpt-4o, claude-3-5-sonnet-20241022
+  num_test_samples: 50
+  temperature: 0.7
+  max_tokens: 512
+
+# Deployment
+deployment:
+  host: "0.0.0.0"
+  port: 8000
+  max_batch_size: 8
+  inference_max_tokens: 1024
+
+# Output Paths
+output:
+  base_dir: "./output"
+  models_dir: "./output/models"
+  logs_dir: "./output/logs"
+  reports_dir: "./output/reports"
+  data_dir: "./output/processed_data"

main.py

@@ -0,0 +1,482 @@
+"""
+Auto-FineTune-Ops: The Boss Orchestrator
+==========================================
+One-click autonomous ML fine-tuning pipeline.
+
+Usage:
+    python main.py --data ./data.csv --goal "medical_assistant"
+"""
+
+import os
+import sys
+import yaml
+import argparse
+from pathlib import Path
+from datetime import datetime
+from typing import Optional, Dict, Any
+
+from rich.console import Console
+from rich.panel import Panel
+from rich.progress import Progress, SpinnerColumn, TextColumn
+from rich.markdown import Markdown
+
+# Add project root to path
+sys.path.insert(0, str(Path(__file__).parent))
+
+from agents.data_architect import DataArchitectAgent, CleaningConfig
+from agents.training_pilot import TrainingPilot
+from agents.the_judge import TheJudge, JudgeModel
+
+console = Console()
+
+
+class AutoFineTuneOps:
+    """
+    The Boss Orchestrator - Runs the complete end-to-end fine-tuning pipeline.
+    
+    Pipeline stages:
+    1. Data Preparation (DataArchitectAgent)
+    2. Fine-Tuning (TrainingPilot)
+    3. Evaluation (TheJudge)
+    4. Deployment Ready
+    """
+    
+    def __init__(
+        self,
+        config_path: Optional[str] = None,
+        output_dir: str = "./output"
+    ):
+        """
+        Initialize the orchestrator.
+        
+        Args:
+            config_path: Path to configuration YAML
+            output_dir: Base output directory
+        """
+        self.config = self._load_config(config_path)
+        self.output_dir = Path(output_dir)
+        self.output_dir.mkdir(parents=True, exist_ok=True)
+        
+        # Create subdirectories
+        (self.output_dir / "processed_data").mkdir(exist_ok=True)
+        (self.output_dir / "models").mkdir(exist_ok=True)
+        (self.output_dir / "logs").mkdir(exist_ok=True)
+        (self.output_dir / "reports").mkdir(exist_ok=True)
+        
+        # Initialize agents
+        self.data_agent = None
+        self.training_agent = None
+        self.judge_agent = None
+        
+        # Pipeline state
+        self.processed_data_path = None
+        self.model_path = None
+        self.evaluation_result = None
+        
+    def _load_config(self, config_path: Optional[str]) -> Dict[str, Any]:
+        """Load configuration from YAML file."""
+        default_config_path = Path(__file__).parent / "configs" / "default_config.yaml"
+        
+        if config_path and Path(config_path).exists():
+            with open(config_path, 'r') as f:
+                return yaml.safe_load(f)
+        elif default_config_path.exists():
+            with open(default_config_path, 'r') as f:
+                return yaml.safe_load(f)
+        return {}
+    
+    def _print_header(self):
+        """Print the main header."""
+        header = """
+╔═══════════════════════════════════════════════════════════════╗
+║                                                               ║
+║     🤖 AUTO-FINETUNE-OPS: AUTONOMOUS ML PIPELINE 🤖          ║
+║                                                               ║
+║     "One-Click Fine-Tuning That Replaces Senior Engineers"   ║
+║                                                               ║
+╚═══════════════════════════════════════════════════════════════╝
+        """
+        console.print(Panel(header, style="bold magenta"))
+    
+    def _print_stage(self, stage: int, name: str, description: str):
+        """Print a stage header."""
+        console.print(f"\n[bold cyan]{'='*60}[/]")
+        console.print(f"[bold cyan]STAGE {stage}: {name}[/]")
+        console.print(f"[dim]{description}[/]")
+        console.print(f"[bold cyan]{'='*60}[/]\n")
+    
+    def run(
+        self,
+        data_path: str,
+        goal: str,
+        base_model: Optional[str] = None,
+        skip_training: bool = False,
+        skip_evaluation: bool = False,
+        judge_model: str = "gpt-4o",
+        num_eval_samples: int = 50
+    ) -> Dict[str, Any]:
+        """
+        Run the complete fine-tuning pipeline.
+        
+        Args:
+            data_path: Path to input dataset (CSV/JSON)
+            goal: Training goal/purpose
+            base_model: Override base model
+            skip_training: Skip training stage (use existing model)
+            skip_evaluation: Skip evaluation stage
+            judge_model: LLM to use as judge
+            num_eval_samples: Number of samples for evaluation
+            
+        Returns:
+            Dict with pipeline results
+        """
+        self._print_header()
+        
+        timestamp = datetime.now().strftime("%Y%m%d_%H%M%S")
+        run_name = f"{goal}_{timestamp}"
+        
+        console.print(f"[bold]Run Name:[/] {run_name}")
+        console.print(f"[bold]Input Data:[/] {data_path}")
+        console.print(f"[bold]Goal:[/] {goal}")
+        console.print(f"[bold]Base Model:[/] {base_model or self.config.get('model', {}).get('base_model', 'unsloth/llama-3-8b-bnb-4bit')}")
+        
+        results = {
+            "run_name": run_name,
+            "goal": goal,
+            "stages": {}
+        }
+        
+        try:
+            # ═══════════════════════════════════════════════════════════
+            # STAGE 1: DATA PREPARATION
+            # ═══════════════════════════════════════════════════════════
+            self._print_stage(
+                1,
+                "DATA PREPARATION",
+                "Analyzing, cleaning, and formatting dataset for training"
+            )
+            
+            # Initialize data agent with config
+            data_config = self.config.get('data', {})
+            cleaning_config = CleaningConfig(
+                min_instruction_length=data_config.get('min_instruction_length', 10),
+                max_instruction_length=data_config.get('max_instruction_length', 2048),
+                min_response_length=data_config.get('min_response_length', 20),
+                max_response_length=data_config.get('max_response_length', 4096),
+                remove_duplicates=data_config.get('remove_duplicates', True),
+                quality_threshold=data_config.get('quality_threshold', 0.7)
+            )
+            
+            self.data_agent = DataArchitectAgent(config=cleaning_config)
+            
+            # Process data
+            output_jsonl = self.output_dir / "processed_data" / f"{run_name}_training.jsonl"
+            self.processed_data_path, data_analysis = self.data_agent.process(
+                input_path=data_path,
+                output_path=str(output_jsonl),
+                goal=goal
+            )
+            
+            results["stages"]["data_preparation"] = {
+                "status": "success",
+                "output_path": self.processed_data_path,
+                "total_samples": data_analysis.valid_rows,
+                "quality_score": data_analysis.quality_score
+            }
+            
+            # ═══════════════════════════════════════════════════════════
+            # STAGE 2: FINE-TUNING
+            # ═══════════════════════════════════════════════════════════
+            if not skip_training:
+                self._print_stage(
+                    2,
+                    "FINE-TUNING",
+                    "Auto-configuring hyperparameters and training with Unsloth"
+                )
+                
+                # Get model config
+                model_config = self.config.get('model', {})
+                base_model = base_model or model_config.get('base_model', 'unsloth/llama-3-8b-bnb-4bit')
+                max_seq_length = model_config.get('max_seq_length', 2048)
+                
+                self.training_agent = TrainingPilot(
+                    base_model=base_model,
+                    max_seq_length=max_seq_length,
+                    output_dir=str(self.output_dir / "models"),
+                    config_path=None
+                )
+                
+                # Run training
+                training_result = self.training_agent.run(
+                    data_path=self.processed_data_path,
+                    output_name=run_name
+                )
+                
+                self.model_path = training_result.model_path
+                
+                results["stages"]["training"] = {
+                    "status": "success",
+                    "model_path": self.model_path,
+                    "training_time": training_result.training_time,
+                    "final_loss": training_result.final_loss,
+                    "hyperparams": training_result.hyperparams.to_dict()
+                }
+            else:
+                console.print("[yellow]⏭️  Skipping training stage[/]")
+                results["stages"]["training"] = {"status": "skipped"}
+            
+            # ═══════════════════════════════════════════════════════════
+            # STAGE 3: EVALUATION
+            # ═══════════════════════════════════════════════════════════
+            if not skip_evaluation and self.model_path:
+                self._print_stage(
+                    3,
+                    "EVALUATION",
+                    "Running Model Arena with LLM-as-Judge"
+                )
+                
+                # Check for API keys
+                eval_config = self.config.get('evaluation', {})
+                judge_model_str = judge_model or eval_config.get('judge_model', 'gpt-4o')
+                
+                if judge_model_str == "gpt-4o" and not os.getenv("OPENAI_API_KEY"):
+                    console.print("[yellow]⚠️  OPENAI_API_KEY not set. Skipping evaluation.[/]")
+                    results["stages"]["evaluation"] = {
+                        "status": "skipped",
+                        "reason": "No API key"
+                    }
+                elif "claude" in judge_model_str and not os.getenv("ANTHROPIC_API_KEY"):
+                    console.print("[yellow]⚠️  ANTHROPIC_API_KEY not set. Skipping evaluation.[/]")
+                    results["stages"]["evaluation"] = {
+                        "status": "skipped",
+                        "reason": "No API key"
+                    }
+                else:
+                    # Determine judge model enum
+                    if "claude" in judge_model_str.lower():
+                        judge_enum = JudgeModel.CLAUDE_35_SONNET
+                    else:
+                        judge_enum = JudgeModel.GPT4O
+                    
+                    self.judge_agent = TheJudge(
+                        judge_model=judge_enum,
+                        temperature=eval_config.get('temperature', 0.2),
+                        max_tokens=eval_config.get('max_tokens', 1024)
+                    )
+                    
+                    # Load models for evaluation
+                    console.print("[blue]Loading models for evaluation...[/]")
+                    
+                    try:
+                        from unsloth import FastLanguageModel
+                        
+                        # Load base model
+                        base_model_name = base_model or self.config.get('model', {}).get('base_model', 'unsloth/llama-3-8b-bnb-4bit')
+                        base_model_obj, base_tokenizer = FastLanguageModel.from_pretrained(
+                            model_name=base_model_name,
+                            max_seq_length=2048,
+                            load_in_4bit=True,
+                        )
+                        
+                        # Load fine-tuned model
+                        ft_model, ft_tokenizer = FastLanguageModel.from_pretrained(
+                            model_name=self.model_path,
+                            max_seq_length=2048,
+                            load_in_4bit=True,
+                        )
+                        
+                        # Run evaluation
+                        self.evaluation_result = self.judge_agent.run_with_test_data(
+                            base_model=base_model_obj,
+                            finetuned_model=ft_model,
+                            tokenizer=base_tokenizer,
+                            test_data_path=self.processed_data_path,
+                            num_samples=num_eval_samples,
+                            finetuned_tokenizer=ft_tokenizer
+                        )
+                        
+                        # Generate report
+                        report_path = self.output_dir / "reports" / f"{run_name}_evaluation.json"
+                        self.judge_agent.generate_report(
+                            self.evaluation_result,
+                            str(report_path)
+                        )
+                        
+                        results["stages"]["evaluation"] = {
+                            "status": "success",
+                            "win_rate": self.evaluation_result.win_rate,
+                            "base_avg_score": self.evaluation_result.base_model_avg_score,
+                            "finetuned_avg_score": self.evaluation_result.finetuned_avg_score,
+                            "report_path": str(report_path)
+                        }
+                        
+                    except ImportError:
+                        console.print("[yellow]⚠️  Unsloth not available for evaluation. Skipping.[/]")
+                        results["stages"]["evaluation"] = {
+                            "status": "skipped",
+                            "reason": "Unsloth not available"
+                        }
+            else:
+                if skip_evaluation:
+                    console.print("[yellow]⏭️  Skipping evaluation stage[/]")
+                results["stages"]["evaluation"] = {"status": "skipped"}
+            
+            # ═══════════════════════════════════════════════════════════
+            # STAGE 4: SUMMARY
+            # ═══════════════════════════════════════════════════════════
+            self._print_stage(
+                4,
+                "PIPELINE COMPLETE",
+                "Summary of the autonomous fine-tuning run"
+            )
+            
+            self._print_summary(results)
+            
+            # Save results
+            results_path = self.output_dir / "logs" / f"{run_name}_results.yaml"
+            with open(results_path, 'w') as f:
+                yaml.dump(results, f, default_flow_style=False)
+            
+            console.print(f"\n[green]✓ Results saved to: {results_path}[/]")
+            
+            return results
+            
+        except Exception as e:
+            console.print(f"\n[bold red]❌ Pipeline failed: {str(e)}[/]")
+            import traceback
+            traceback.print_exc()
+            results["error"] = str(e)
+            return results
+    
+    def _print_summary(self, results: Dict[str, Any]):
+        """Print pipeline summary."""
+        from rich.table import Table
+        
+        table = Table(title="Pipeline Summary", show_header=True)
+        table.add_column("Stage", style="cyan")
+        table.add_column("Status", style="green")
+        table.add_column("Details", style="dim")
+        
+        # Data preparation
+        data_stage = results["stages"].get("data_preparation", {})
+        if data_stage.get("status") == "success":
+            table.add_row(
+                "Data Preparation",
+                "✅ Success",
+                f"{data_stage.get('total_samples', 0):,} samples (Quality: {data_stage.get('quality_score', 0):.1%})"
+            )
+        
+        # Training
+        train_stage = results["stages"].get("training", {})
+        if train_stage.get("status") == "success":
+            table.add_row(
+                "Fine-Tuning",
+                "✅ Success",
+                f"Loss: {train_stage.get('final_loss', 0):.4f}"
+            )
+        elif train_stage.get("status") == "skipped":
+            table.add_row("Fine-Tuning", "⏭️ Skipped", "")
+        
+        # Evaluation
+        eval_stage = results["stages"].get("evaluation", {})
+        if eval_stage.get("status") == "success":
+            table.add_row(
+                "Evaluation",
+                "✅ Success",
+                f"Win Rate: {eval_stage.get('win_rate', 0):.1%}"
+            )
+        elif eval_stage.get("status") == "skipped":
+            table.add_row("Evaluation", "⏭️ Skipped", eval_stage.get("reason", ""))
+        
+        console.print(table)
+        
+        # Print model path if available
+        if self.model_path:
+            console.print(f"\n[bold green]📦 Fine-tuned model saved to:[/]")
+            console.print(f"   {self.model_path}")
+            console.print(f"\n[bold]To deploy, run:[/]")
+            console.print(f"   [cyan]python scripts/deploy.py --model {self.model_path}[/]")
+
+
+def main():
+    """CLI entry point."""
+    parser = argparse.ArgumentParser(
+        description="Auto-FineTune-Ops: One-click autonomous ML fine-tuning pipeline",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+        epilog="""
+Examples:
+  python main.py --data ./data.csv --goal medical_assistant
+  python main.py --data ./qa_pairs.json --goal customer_support --model unsloth/llama-3-8b-bnb-4bit
+  python main.py --data ./dataset.jsonl --goal code_assistant --skip-eval
+        """
+    )
+    
+    parser.add_argument(
+        "--data",
+        required=True,
+        help="Path to input dataset (CSV, JSON, or JSONL)"
+    )
+    parser.add_argument(
+        "--goal",
+        required=True,
+        help="Training goal (e.g., medical_assistant, customer_support)"
+    )
+    parser.add_argument(
+        "--model",
+        default=None,
+        help="Base model to fine-tune (default: unsloth/llama-3-8b-bnb-4bit)"
+    )
+    parser.add_argument(
+        "--config",
+        default=None,
+        help="Path to configuration YAML file"
+    )
+    parser.add_argument(
+        "--output",
+        default="./output",
+        help="Output directory (default: ./output)"
+    )
+    parser.add_argument(
+        "--skip-training",
+        action="store_true",
+        help="Skip training stage"
+    )
+    parser.add_argument(
+        "--skip-eval",
+        action="store_true",
+        help="Skip evaluation stage"
+    )
+    parser.add_argument(
+        "--judge",
+        choices=["gpt-4o", "claude-3-5-sonnet"],
+        default="gpt-4o",
+        help="Judge LLM for evaluation (default: gpt-4o)"
+    )
+    parser.add_argument(
+        "--eval-samples",
+        type=int,
+        default=50,
+        help="Number of samples for evaluation (default: 50)"
+    )
+    
+    args = parser.parse_args()
+    
+    # Run pipeline
+    orchestrator = AutoFineTuneOps(
+        config_path=args.config,
+        output_dir=args.output
+    )
+    
+    orchestrator.run(
+        data_path=args.data,
+        goal=args.goal,
+        base_model=args.model,
+        skip_training=args.skip_training,
+        skip_evaluation=args.skip_eval,
+        judge_model=args.judge,
+        num_eval_samples=args.eval_samples
+    )
+
+
+if __name__ == "__main__":
+    main()

preprocessing/__init__.py

@@ -0,0 +1,8 @@
+"""
+Preprocessing Pipeline for LLM Fine-Tuning
+============================================
+Modular preprocessing stages for cleaning, filtering,
+formatting, and exporting datasets.
+"""
+
+from preprocessing.pipeline import PreprocessingPipeline, PreprocessingConfig

preprocessing/augmentation.py

@@ -0,0 +1,182 @@
+"""
+Augmentation Module (Optional)
+================================
+Lightweight synthetic data expansion stubs.
+These are pure-Python approximations. For production quality,
+integrate with an LLM API or NLP library.
+"""
+
+import random
+import re
+from dataclasses import dataclass
+from typing import List
+import pandas as pd
+
+
+@dataclass
+class AugmentationConfig:
+    """Configuration for data augmentation."""
+    enabled: bool = False
+    paraphrase: bool = False
+    generate_variations: bool = False
+    back_translate: bool = False
+    tone_rewrite: bool = False
+    augmentation_factor: int = 1  # how many extra copies per sample
+
+
+# ---------------------------------------------------------------------------
+# Synonym map for lightweight paraphrasing
+# ---------------------------------------------------------------------------
+_SYNONYMS = {
+    'explain': ['describe', 'elaborate on', 'clarify', 'break down'],
+    'create': ['generate', 'produce', 'make', 'build'],
+    'write': ['compose', 'draft', 'author', 'pen'],
+    'list': ['enumerate', 'outline', 'itemize', 'catalog'],
+    'help': ['assist', 'aid', 'support', 'guide'],
+    'show': ['demonstrate', 'display', 'present', 'illustrate'],
+    'tell': ['inform', 'describe', 'narrate', 'share'],
+    'give': ['provide', 'supply', 'offer', 'deliver'],
+    'find': ['locate', 'discover', 'identify', 'search for'],
+    'use': ['utilize', 'employ', 'apply', 'leverage'],
+    'what': ['which', 'what exactly'],
+    'how': ['in what way', 'by what method'],
+    'important': ['crucial', 'essential', 'significant', 'vital'],
+    'good': ['excellent', 'great', 'effective', 'beneficial'],
+    'bad': ['poor', 'negative', 'harmful', 'detrimental'],
+    'big': ['large', 'significant', 'substantial', 'major'],
+    'small': ['minor', 'slight', 'modest', 'minimal'],
+}
+
+
+def paraphrase_instruction(text: str) -> str:
+    """
+    Simple synonym-based paraphrasing.
+    Replaces one random word with a synonym.
+    """
+    if not isinstance(text, str) or len(text.strip()) < 5:
+        return text
+
+    words = text.split()
+    candidates = []
+
+    for i, word in enumerate(words):
+        word_lower = word.lower().strip('.,!?;:')
+        if word_lower in _SYNONYMS:
+            candidates.append((i, word_lower))
+
+    if not candidates:
+        return text
+
+    idx, orig_word = random.choice(candidates)
+    replacement = random.choice(_SYNONYMS[orig_word])
+
+    # Preserve original casing
+    if words[idx][0].isupper():
+        replacement = replacement.capitalize()
+
+    # Preserve trailing punctuation
+    trailing = ''
+    if words[idx] and words[idx][-1] in '.,!?;:':
+        trailing = words[idx][-1]
+        words[idx] = replacement + trailing
+    else:
+        words[idx] = replacement
+
+    return ' '.join(words)
+
+
+def generate_variation(text: str) -> str:
+    """
+    Generate a minor variation of the text:
+    - Random case changes
+    - Add/remove trailing punctuation
+    - Slight word reordering at clause boundaries
+    """
+    if not isinstance(text, str) or len(text.strip()) < 5:
+        return text
+
+    variations = [
+        lambda t: t.rstrip('.!?') + random.choice(['.', '!', '?', '']),
+        lambda t: t[0].upper() + t[1:] if len(t) > 1 else t,
+        lambda t: re.sub(r'\s+', ' ', t).strip(),
+        lambda t: t + ' Please be detailed.' if random.random() > 0.5 else t,
+    ]
+
+    variation = random.choice(variations)
+    return variation(text)
+
+
+def back_translate(text: str) -> str:
+    """
+    Stub for back-translation.
+    In production, this would translate to another language and back.
+    Here we just do a light paraphrase.
+    """
+    return paraphrase_instruction(text)
+
+
+def rewrite_tone(text: str, tone: str = "formal") -> str:
+    """
+    Stub for tone rewriting.
+    """
+    tone_prefixes = {
+        'formal': 'Please ',
+        'casual': 'Hey, can you ',
+        'academic': 'Kindly provide a detailed analysis of ',
+        'friendly': 'I would really appreciate if you could ',
+    }
+
+    prefix = tone_prefixes.get(tone, '')
+
+    # Don't double-prefix
+    if text.lower().startswith(prefix.lower().strip()):
+        return text
+
+    # Simple approach: prepend tone prefix if the text starts with a verb-like word
+    first_word = text.split()[0].lower() if text.split() else ''
+    action_words = {'explain', 'describe', 'write', 'create', 'list', 'show', 'tell', 'give', 'find', 'help', 'make'}
+
+    if first_word in action_words:
+        return prefix + text[0].lower() + text[1:]
+
+    return text
+
+
+def augment_dataset(
+    df: pd.DataFrame,
+    col: str,
+    config: AugmentationConfig,
+) -> pd.DataFrame:
+    """
+    Apply augmentation to create additional samples.
+    Returns the original + augmented samples.
+    """
+    if not config.enabled:
+        return df
+
+    methods = []
+    if config.paraphrase:
+        methods.append(paraphrase_instruction)
+    if config.generate_variations:
+        methods.append(generate_variation)
+    if config.back_translate:
+        methods.append(back_translate)
+    if config.tone_rewrite:
+        methods.append(lambda t: rewrite_tone(t, "formal"))
+
+    if not methods:
+        return df
+
+    new_rows = []
+    for _, row in df.iterrows():
+        for _ in range(config.augmentation_factor):
+            method = random.choice(methods)
+            new_row = row.copy()
+            new_row[col] = method(str(row[col]))
+            new_rows.append(new_row)
+
+    if new_rows:
+        augmented = pd.DataFrame(new_rows)
+        return pd.concat([df, augmented], ignore_index=True)
+
+    return df

preprocessing/dataset_balancing.py

@@ -0,0 +1,97 @@
+"""
+Dataset Balancing Module
+=========================
+Class balancing for classification datasets via
+oversampling / undersampling strategies.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, Optional
+import pandas as pd
+
+
+@dataclass
+class BalancingConfig:
+    """Configuration for dataset balancing."""
+    enabled: bool = False
+    label_column: str = ""
+    strategy: str = "none"  # "none", "oversample", "undersample"
+
+
+def compute_label_distribution(
+    df: pd.DataFrame,
+    label_col: str,
+) -> Dict[str, int]:
+    """
+    Compute label distribution for a given column.
+    Returns dict of label_value -> count.
+    """
+    if label_col not in df.columns:
+        return {}
+    return df[label_col].value_counts().to_dict()
+
+
+def oversample_minority(
+    df: pd.DataFrame,
+    label_col: str,
+) -> pd.DataFrame:
+    """
+    Oversample minority classes to match the majority class count.
+    """
+    if label_col not in df.columns:
+        return df
+
+    counts = df[label_col].value_counts()
+    max_count = counts.max()
+
+    frames = []
+    for label, count in counts.items():
+        label_df = df[df[label_col] == label]
+        if count < max_count:
+            # Resample with replacement to reach max_count
+            extra = label_df.sample(n=max_count - count, replace=True, random_state=42)
+            frames.append(pd.concat([label_df, extra], ignore_index=True))
+        else:
+            frames.append(label_df)
+
+    return pd.concat(frames, ignore_index=True)
+
+
+def undersample_majority(
+    df: pd.DataFrame,
+    label_col: str,
+) -> pd.DataFrame:
+    """
+    Undersample majority classes to match the minority class count.
+    """
+    if label_col not in df.columns:
+        return df
+
+    counts = df[label_col].value_counts()
+    min_count = counts.min()
+
+    frames = []
+    for label in counts.index:
+        label_df = df[df[label_col] == label]
+        if len(label_df) > min_count:
+            frames.append(label_df.sample(n=min_count, random_state=42))
+        else:
+            frames.append(label_df)
+
+    return pd.concat(frames, ignore_index=True)
+
+
+def balance_dataset(
+    df: pd.DataFrame,
+    label_col: str,
+    strategy: str = "none",
+) -> pd.DataFrame:
+    """
+    Balance dataset using the specified strategy.
+    strategy: "none", "oversample", or "undersample"
+    """
+    if strategy == "oversample":
+        return oversample_minority(df, label_col)
+    elif strategy == "undersample":
+        return undersample_majority(df, label_col)
+    return df

preprocessing/deduplication.py

@@ -0,0 +1,84 @@
+"""
+Deduplication Module
+======================
+Exact and semantic (TF-IDF cosine similarity) deduplication.
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class DeduplicationConfig:
+    """Configuration for deduplication."""
+    remove_exact: bool = True
+    remove_semantic: bool = False
+    semantic_threshold: float = 0.90  # cosine similarity threshold
+
+
+def remove_exact_duplicates(
+    df: pd.DataFrame,
+    col: str,
+) -> pd.DataFrame:
+    """Remove rows with exact duplicate values in the given column."""
+    return df.drop_duplicates(subset=[col]).reset_index(drop=True)
+
+
+def remove_semantic_duplicates(
+    df: pd.DataFrame,
+    col: str,
+    threshold: float = 0.90,
+) -> pd.DataFrame:
+    """
+    Remove semantically similar rows using TF-IDF cosine similarity.
+    Rows with cosine similarity >= threshold to an earlier row are dropped.
+    """
+    if len(df) < 2:
+        return df
+
+    try:
+        from sklearn.feature_extraction.text import TfidfVectorizer
+        from sklearn.metrics.pairwise import cosine_similarity
+    except ImportError:
+        # If scikit-learn not available, just return as-is
+        return df
+
+    texts = df[col].fillna('').astype(str).tolist()
+
+    # Build TF-IDF matrix
+    vectorizer = TfidfVectorizer(max_features=5000, stop_words='english')
+    try:
+        tfidf_matrix = vectorizer.fit_transform(texts)
+    except ValueError:
+        return df
+
+    # Find duplicates — compare each row to all previous rows
+    keep_indices = [0]
+
+    for i in range(1, len(texts)):
+        # Compare row i against all kept rows
+        sim = cosine_similarity(
+            tfidf_matrix[i:i+1],
+            tfidf_matrix[keep_indices],
+        )
+        if sim.max() < threshold:
+            keep_indices.append(i)
+
+    return df.iloc[keep_indices].reset_index(drop=True)
+
+
+def apply_deduplication(
+    df: pd.DataFrame,
+    col: str,
+    config: DeduplicationConfig,
+) -> pd.DataFrame:
+    """Apply all enabled deduplication methods."""
+    if config.remove_exact:
+        df = remove_exact_duplicates(df, col)
+
+    if config.remove_semantic:
+        df = remove_semantic_duplicates(df, col, config.semantic_threshold)
+
+    return df

preprocessing/output_formatter.py

@@ -0,0 +1,150 @@
+"""
+Output Formatter Module
+=========================
+Export datasets in multiple JSONL formats:
+- OpenAI Chat JSONL
+- Completion JSONL
+- Classification JSONL
+- Custom schema JSONL
+"""
+
+import json
+from dataclasses import dataclass, field
+from typing import List, Dict, Any, Optional
+from pathlib import Path
+import pandas as pd
+
+
+@dataclass
+class OutputFormatConfig:
+    """Configuration for output formatting."""
+    format_type: str = "openai_chat"  # "openai_chat", "completion", "classification", "custom"
+    custom_schema: Dict[str, str] = field(default_factory=dict)
+    # custom_schema maps output_key -> source_column, e.g. {"text": "instruction", "label": "category"}
+
+
+def format_openai_chat(
+    df: pd.DataFrame,
+    system_prompt: str,
+    instruction_col: str,
+    output_col: str,
+    input_col: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """
+    Format as OpenAI Chat JSONL.
+    Each entry: {"messages": [{"role": "system", ...}, {"role": "user", ...}, {"role": "assistant", ...}]}
+    """
+    data = []
+    for _, row in df.iterrows():
+        messages = []
+        if system_prompt:
+            messages.append({"role": "system", "content": system_prompt})
+
+        user_content = str(row[instruction_col])
+        if input_col and input_col in df.columns:
+            context = str(row.get(input_col, ''))
+            if context and context != 'nan':
+                user_content += f"\n\nContext: {context}"
+
+        messages.append({"role": "user", "content": user_content})
+        messages.append({"role": "assistant", "content": str(row[output_col])})
+
+        data.append({"messages": messages})
+    return data
+
+
+def format_completion(
+    df: pd.DataFrame,
+    instruction_col: str,
+    output_col: str,
+) -> List[Dict[str, Any]]:
+    """
+    Format as Completion JSONL.
+    Each entry: {"prompt": "...", "completion": "..."}
+    """
+    data = []
+    for _, row in df.iterrows():
+        data.append({
+            "prompt": str(row[instruction_col]),
+            "completion": str(row[output_col]),
+        })
+    return data
+
+
+def format_classification(
+    df: pd.DataFrame,
+    text_col: str,
+    label_col: str,
+) -> List[Dict[str, Any]]:
+    """
+    Format as Classification JSONL.
+    Each entry: {"text": "...", "label": "..."}
+    """
+    data = []
+    for _, row in df.iterrows():
+        data.append({
+            "text": str(row[text_col]),
+            "label": str(row[label_col]),
+        })
+    return data
+
+
+def format_custom(
+    df: pd.DataFrame,
+    schema: Dict[str, str],
+) -> List[Dict[str, Any]]:
+    """
+    Format using a custom schema.
+    schema: dict mapping output_key -> source_column name
+    """
+    data = []
+    for _, row in df.iterrows():
+        entry = {}
+        for out_key, src_col in schema.items():
+            if src_col in df.columns:
+                entry[out_key] = str(row[src_col])
+            else:
+                entry[out_key] = ""
+        data.append(entry)
+    return data
+
+
+def export_jsonl(data: List[Dict[str, Any]], path: str) -> str:
+    """Write a list of dicts as JSONL to a file."""
+    output_path = Path(path)
+    output_path.parent.mkdir(parents=True, exist_ok=True)
+
+    with open(output_path, 'w', encoding='utf-8') as f:
+        for entry in data:
+            f.write(json.dumps(entry, ensure_ascii=False) + '\n')
+
+    return str(output_path)
+
+
+def generate_preview(data: List[Dict[str, Any]], n: int = 3) -> str:
+    """Return a pretty-printed JSON string of the first n entries."""
+    return json.dumps(data[:n], indent=2, ensure_ascii=False)
+
+
+def format_dataset(
+    df: pd.DataFrame,
+    config: OutputFormatConfig,
+    system_prompt: str = "",
+    instruction_col: str = "",
+    output_col: str = "",
+    input_col: Optional[str] = None,
+    label_col: Optional[str] = None,
+) -> List[Dict[str, Any]]:
+    """Format the dataset according to the configured format type."""
+    if config.format_type == "openai_chat":
+        return format_openai_chat(df, system_prompt, instruction_col, output_col, input_col)
+    elif config.format_type == "completion":
+        return format_completion(df, instruction_col, output_col)
+    elif config.format_type == "classification":
+        text_col = instruction_col or (list(df.columns)[0] if len(df.columns) > 0 else "")
+        lbl_col = label_col or output_col
+        return format_classification(df, text_col, lbl_col)
+    elif config.format_type == "custom":
+        return format_custom(df, config.custom_schema)
+    else:
+        return format_openai_chat(df, system_prompt, instruction_col, output_col, input_col)

preprocessing/pii_filter.py

@@ -0,0 +1,165 @@
+"""
+PII (Personally Identifiable Information) Filter Module
+=========================================================
+Regex-based detection and masking for emails, phone numbers,
+CNIC/SSN-like patterns, API keys, and addresses.
+"""
+
+import re
+from dataclasses import dataclass
+from typing import List, Dict, Tuple
+import pandas as pd
+
+
+@dataclass
+class PIIFilterConfig:
+    """Configuration for PII filtering."""
+    filter_emails: bool = False
+    filter_phones: bool = False
+    filter_id_numbers: bool = False   # CNIC / SSN patterns
+    filter_api_keys: bool = False
+    filter_addresses: bool = False
+    mask_char: str = "[REDACTED]"
+
+
+# ---------------------------------------------------------------------------
+# Detection + Masking patterns
+# ---------------------------------------------------------------------------
+
+_EMAIL_PATTERN = re.compile(
+    r'\b[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}\b'
+)
+
+_PHONE_PATTERN = re.compile(
+    r'(?:\+?\d{1,3}[-.\s]?)?\(?\d{2,4}\)?[-.\s]?\d{3,4}[-.\s]?\d{3,4}'
+)
+
+# SSN: 123-45-6789, CNIC: 12345-1234567-1
+_ID_NUMBER_PATTERN = re.compile(
+    r'\b\d{3}-\d{2}-\d{4}\b'        # US SSN
+    r'|\b\d{5}-\d{7}-\d{1}\b'       # PK CNIC
+    r'|\b\d{13}\b'                   # 13-digit ID
+)
+
+# Long hex or base64 strings that look like API keys / secrets
+_API_KEY_PATTERN = re.compile(
+    r'\b(?:sk|pk|api|key|secret|token)[_-]?[A-Za-z0-9]{20,}\b'
+    r'|[A-Fa-f0-9]{32,}'
+    r'|[A-Za-z0-9+/]{40,}={0,2}',
+    re.IGNORECASE,
+)
+
+# Basic address patterns (US-style zip, PO Box, street numbers)
+_ADDRESS_PATTERN = re.compile(
+    r'\b\d{1,5}\s+\w+\s+(?:St|Street|Ave|Avenue|Blvd|Boulevard|Dr|Drive|Rd|Road|Ln|Lane|Way|Ct|Court)\b'
+    r'|\bP\.?O\.?\s*Box\s+\d+\b'
+    r'|\b\d{5}(?:-\d{4})?\b',         # Zip code
+    re.IGNORECASE,
+)
+
+
+def detect_emails(text: str) -> List[str]:
+    """Find all email addresses in text."""
+    return _EMAIL_PATTERN.findall(text) if isinstance(text, str) else []
+
+
+def mask_emails(text: str, mask: str = "[REDACTED_EMAIL]") -> str:
+    """Replace email addresses with mask."""
+    return _EMAIL_PATTERN.sub(mask, text) if isinstance(text, str) else text
+
+
+def detect_phones(text: str) -> List[str]:
+    """Find all phone numbers in text."""
+    return _PHONE_PATTERN.findall(text) if isinstance(text, str) else []
+
+
+def mask_phones(text: str, mask: str = "[REDACTED_PHONE]") -> str:
+    """Replace phone numbers with mask."""
+    return _PHONE_PATTERN.sub(mask, text) if isinstance(text, str) else text
+
+
+def detect_id_numbers(text: str) -> List[str]:
+    """Find SSN/CNIC-like patterns in text."""
+    return _ID_NUMBER_PATTERN.findall(text) if isinstance(text, str) else []
+
+
+def mask_id_numbers(text: str, mask: str = "[REDACTED_ID]") -> str:
+    """Replace ID number patterns with mask."""
+    return _ID_NUMBER_PATTERN.sub(mask, text) if isinstance(text, str) else text
+
+
+def detect_api_keys(text: str) -> List[str]:
+    """Find API key / secret patterns in text."""
+    return _API_KEY_PATTERN.findall(text) if isinstance(text, str) else []
+
+
+def mask_api_keys(text: str, mask: str = "[REDACTED_KEY]") -> str:
+    """Replace API key patterns with mask."""
+    return _API_KEY_PATTERN.sub(mask, text) if isinstance(text, str) else text
+
+
+def detect_addresses(text: str) -> List[str]:
+    """Find address-like patterns in text."""
+    return _ADDRESS_PATTERN.findall(text) if isinstance(text, str) else []
+
+
+def mask_addresses(text: str, mask: str = "[REDACTED_ADDR]") -> str:
+    """Replace address patterns with mask."""
+    return _ADDRESS_PATTERN.sub(mask, text) if isinstance(text, str) else text
+
+
+def apply_pii_filter(
+    text: str,
+    config: PIIFilterConfig,
+) -> str:
+    """Apply all enabled PII filters to a single text string."""
+    mask = config.mask_char
+
+    if config.filter_emails:
+        text = mask_emails(text, mask)
+    if config.filter_phones:
+        text = mask_phones(text, mask)
+    if config.filter_id_numbers:
+        text = mask_id_numbers(text, mask)
+    if config.filter_api_keys:
+        text = mask_api_keys(text, mask)
+    if config.filter_addresses:
+        text = mask_addresses(text, mask)
+
+    return text
+
+
+def apply_pii_filter_df(
+    df: pd.DataFrame,
+    columns: List[str],
+    config: PIIFilterConfig,
+) -> pd.DataFrame:
+    """Apply PII filtering to specified columns of a DataFrame."""
+    df = df.copy()
+    for col in columns:
+        if col in df.columns:
+            df[col] = df[col].apply(lambda t: apply_pii_filter(str(t), config))
+    return df
+
+
+def detect_pii_summary(
+    df: pd.DataFrame,
+    columns: List[str],
+) -> Dict[str, int]:
+    """
+    Scan columns and count PII instances found.
+    Returns dict like {"emails": 5, "phones": 2, ...}.
+    """
+    summary = {"emails": 0, "phones": 0, "id_numbers": 0, "api_keys": 0, "addresses": 0}
+
+    for col in columns:
+        if col not in df.columns:
+            continue
+        for text in df[col].astype(str):
+            summary["emails"] += len(detect_emails(text))
+            summary["phones"] += len(detect_phones(text))
+            summary["id_numbers"] += len(detect_id_numbers(text))
+            summary["api_keys"] += len(detect_api_keys(text))
+            summary["addresses"] += len(detect_addresses(text))
+
+    return summary

preprocessing/pipeline.py

@@ -0,0 +1,253 @@
+"""
+Preprocessing Pipeline Runner
+================================
+Central pipeline that runs all enabled preprocessing stages
+sequentially and logs each step.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Dict, Any, Optional, Tuple
+import time
+import pandas as pd
+
+from preprocessing.text_cleaning import TextCleaningConfig, apply_text_cleaning
+from preprocessing.tokenization import (
+    TokenizationConfig, get_tokenizer, compute_token_stats,
+    truncate_samples, split_long_samples,
+)
+from preprocessing.system_prompt import SystemPromptConfig
+from preprocessing.dataset_balancing import BalancingConfig, balance_dataset
+from preprocessing.quality_filters import QualityFilterConfig, apply_quality_filters
+from preprocessing.deduplication import DeduplicationConfig, apply_deduplication
+from preprocessing.train_val_split import SplitConfig, split_dataset
+from preprocessing.output_formatter import OutputFormatConfig, format_dataset, export_jsonl
+from preprocessing.pii_filter import PIIFilterConfig, apply_pii_filter_df
+from preprocessing.augmentation import AugmentationConfig, augment_dataset
+
+
+@dataclass
+class PreprocessingConfig:
+    """Master configuration for the entire preprocessing pipeline."""
+    # Column mappings
+    instruction_col: str = ""
+    output_col: str = ""
+    input_col: Optional[str] = None
+    label_col: Optional[str] = None
+
+    # Sub-configs
+    text_cleaning: TextCleaningConfig = field(default_factory=TextCleaningConfig)
+    tokenization: TokenizationConfig = field(default_factory=TokenizationConfig)
+    system_prompt: SystemPromptConfig = field(default_factory=SystemPromptConfig)
+    balancing: BalancingConfig = field(default_factory=BalancingConfig)
+    quality_filters: QualityFilterConfig = field(default_factory=QualityFilterConfig)
+    deduplication: DeduplicationConfig = field(default_factory=DeduplicationConfig)
+    split: SplitConfig = field(default_factory=SplitConfig)
+    output_format: OutputFormatConfig = field(default_factory=OutputFormatConfig)
+    pii_filter: PIIFilterConfig = field(default_factory=PIIFilterConfig)
+    augmentation: AugmentationConfig = field(default_factory=AugmentationConfig)
+
+
+@dataclass
+class PipelineLog:
+    """A single log entry from a pipeline stage."""
+    stage: str
+    description: str
+    rows_before: int
+    rows_after: int
+    duration_ms: float
+
+    @property
+    def rows_delta(self) -> int:
+        return self.rows_after - self.rows_before
+
+
+class PreprocessingPipeline:
+    """
+    Sequential preprocessing pipeline runner.
+    Applies all enabled stages and collects logs.
+    """
+
+    def __init__(self, config: PreprocessingConfig):
+        self.config = config
+        self.logs: List[PipelineLog] = []
+
+    def _log(self, stage: str, desc: str, before: int, after: int, elapsed: float):
+        self.logs.append(PipelineLog(
+            stage=stage,
+            description=desc,
+            rows_before=before,
+            rows_after=after,
+            duration_ms=round(elapsed * 1000, 1),
+        ))
+
+    def run(
+        self,
+        df: pd.DataFrame,
+        progress_callback=None,
+    ) -> Tuple[pd.DataFrame, pd.DataFrame, List[PipelineLog]]:
+        """
+        Run the complete preprocessing pipeline.
+
+        Args:
+            df: Input DataFrame
+            progress_callback: Optional callable(stage_name, progress_pct) for UI updates
+
+        Returns:
+            (train_df, val_df, logs)
+            If split is disabled, val_df will be empty.
+        """
+        self.logs = []
+        total_stages = 7  # text cleaning, quality, dedup, pii, balancing, augmentation, tokenization
+        current_stage = 0
+
+        def _progress(name):
+            nonlocal current_stage
+            current_stage += 1
+            if progress_callback:
+                pct = int((current_stage / total_stages) * 100)
+                progress_callback(name, pct)
+
+        cfg = self.config
+        text_cols = [c for c in [cfg.instruction_col, cfg.output_col, cfg.input_col] if c and c in df.columns]
+
+        # ── Stage 1: Text Cleaning ──
+        t0 = time.time()
+        before = len(df)
+        any_cleaning = (
+            cfg.text_cleaning.remove_html or cfg.text_cleaning.remove_urls or
+            cfg.text_cleaning.remove_emojis or cfg.text_cleaning.normalize_whitespace or
+            cfg.text_cleaning.lowercase or cfg.text_cleaning.remove_special_chars or
+            cfg.text_cleaning.strip_extra_linebreaks
+        )
+        if any_cleaning:
+            df = apply_text_cleaning(df, text_cols, cfg.text_cleaning)
+        self._log("Text Cleaning", "Applied text cleaning filters", before, len(df), time.time() - t0)
+        _progress("Text Cleaning")
+
+        # ── Stage 2: Quality Filters ──
+        t0 = time.time()
+        before = len(df)
+        has_quality = (
+            cfg.quality_filters.min_word_count > 0 or
+            cfg.quality_filters.max_word_count > 0 or
+            cfg.quality_filters.profanity_filter or
+            cfg.quality_filters.language_filter or
+            cfg.quality_filters.remove_low_quality
+        )
+        if has_quality and cfg.output_col:
+            df = apply_quality_filters(df, cfg.output_col, cfg.quality_filters)
+        self._log("Quality Filters", "Applied quality filters", before, len(df), time.time() - t0)
+        _progress("Quality Filters")
+
+        # ── Stage 3: Deduplication ──
+        t0 = time.time()
+        before = len(df)
+        if cfg.instruction_col and (cfg.deduplication.remove_exact or cfg.deduplication.remove_semantic):
+            df = apply_deduplication(df, cfg.instruction_col, cfg.deduplication)
+        self._log("Deduplication", "Removed duplicate samples", before, len(df), time.time() - t0)
+        _progress("Deduplication")
+
+        # ── Stage 4: PII Filtering ──
+        t0 = time.time()
+        before = len(df)
+        has_pii = (
+            cfg.pii_filter.filter_emails or cfg.pii_filter.filter_phones or
+            cfg.pii_filter.filter_id_numbers or cfg.pii_filter.filter_api_keys or
+            cfg.pii_filter.filter_addresses
+        )
+        if has_pii:
+            df = apply_pii_filter_df(df, text_cols, cfg.pii_filter)
+        self._log("PII Filtering", "Masked PII data", before, len(df), time.time() - t0)
+        _progress("PII Filtering")
+
+        # ── Stage 5: Dataset Balancing ──
+        t0 = time.time()
+        before = len(df)
+        if cfg.balancing.enabled and cfg.balancing.label_column and cfg.balancing.strategy != "none":
+            df = balance_dataset(df, cfg.balancing.label_column, cfg.balancing.strategy)
+        self._log("Balancing", "Balanced dataset classes", before, len(df), time.time() - t0)
+        _progress("Balancing")
+
+        # ── Stage 6: Augmentation ──
+        t0 = time.time()
+        before = len(df)
+        if cfg.augmentation.enabled and cfg.instruction_col:
+            df = augment_dataset(df, cfg.instruction_col, cfg.augmentation)
+        self._log("Augmentation", "Generated augmented samples", before, len(df), time.time() - t0)
+        _progress("Augmentation")
+
+        # ── Stage 7: Tokenization Controls ──
+        t0 = time.time()
+        before = len(df)
+        if cfg.tokenization.truncate_long or cfg.tokenization.split_long:
+            try:
+                tokenizer = get_tokenizer(cfg.tokenization)
+                is_tiktoken = cfg.tokenization.tokenizer_name == "tiktoken"
+
+                for col in text_cols:
+                    if cfg.tokenization.split_long:
+                        df = split_long_samples(
+                            df, col, cfg.tokenization.max_total_tokens,
+                            tokenizer, is_tiktoken, cfg.tokenization.split_overlap,
+                        )
+                    elif cfg.tokenization.truncate_long:
+                        df = truncate_samples(
+                            df, col, cfg.tokenization.max_total_tokens,
+                            tokenizer, is_tiktoken,
+                        )
+            except ImportError:
+                pass  # tokenizer not available
+        self._log("Tokenization", "Applied tokenization controls", before, len(df), time.time() - t0)
+        _progress("Tokenization")
+
+        # ── Split ──
+        train_df, val_df = split_dataset(df, cfg.split)
+
+        return train_df, val_df, self.logs
+
+
+def get_safe_preset() -> PreprocessingConfig:
+    """Return a sensible 'safe preset' configuration for common use cases."""
+    return PreprocessingConfig(
+        text_cleaning=TextCleaningConfig(
+            remove_html=True,
+            remove_urls=True,
+            remove_emojis=False,
+            normalize_whitespace=True,
+            lowercase=False,
+            remove_special_chars=False,
+            strip_extra_linebreaks=True,
+        ),
+        quality_filters=QualityFilterConfig(
+            min_word_count=3,
+            max_word_count=0,
+            profanity_filter=False,
+            language_filter=False,
+            remove_low_quality=True,
+            min_quality_length=20,
+        ),
+        deduplication=DeduplicationConfig(
+            remove_exact=True,
+            remove_semantic=False,
+        ),
+        pii_filter=PIIFilterConfig(
+            filter_emails=True,
+            filter_phones=True,
+            filter_id_numbers=True,
+            filter_api_keys=True,
+            filter_addresses=False,
+        ),
+        split=SplitConfig(
+            enabled=True,
+            train_ratio=0.9,
+            random_seed=42,
+            shuffle=True,
+        ),
+        output_format=OutputFormatConfig(
+            format_type="openai_chat",
+        ),
+        system_prompt=SystemPromptConfig(
+            system_prompt="You are a helpful AI assistant.",
+            prepend_to_all=True,
+        ),
+    )

preprocessing/quality_filters.py

@@ -0,0 +1,172 @@
+"""
+Quality Filters Module
+========================
+Filter samples by word count, profanity, language,
+and low-quality response detection.
+"""
+
+from dataclasses import dataclass, field
+from typing import List, Optional
+import re
+import pandas as pd
+
+
+@dataclass
+class QualityFilterConfig:
+    """Configuration for quality filters."""
+    min_word_count: int = 0
+    max_word_count: int = 0          # 0 = no limit
+    profanity_filter: bool = False
+    language_filter: bool = False
+    allowed_languages: List[str] = field(default_factory=lambda: ["en"])
+    remove_low_quality: bool = False
+    min_quality_length: int = 20
+
+
+# ---------------------------------------------------------------------------
+# Profanity word list (small built-in set, extend as needed)
+# ---------------------------------------------------------------------------
+_PROFANITY_WORDS = {
+    'fuck', 'shit', 'damn', 'ass', 'bitch', 'bastard', 'crap',
+    'dick', 'piss', 'slut', 'whore', 'cock',
+}
+
+# Generic filler/placeholder responses that indicate low quality
+_GENERIC_RESPONSES = [
+    "i don't know",
+    "i am not sure",
+    "no comment",
+    "n/a",
+    "none",
+    "null",
+    "test",
+    "asdf",
+    "lorem ipsum",
+    "placeholder",
+    "todo",
+    "tbd",
+]
+
+
+def _word_count(text: str) -> int:
+    """Count words in a text string."""
+    if not isinstance(text, str):
+        return 0
+    return len(text.split())
+
+
+def filter_by_word_count(
+    df: pd.DataFrame,
+    col: str,
+    min_words: int = 0,
+    max_words: int = 0,
+) -> pd.DataFrame:
+    """Filter rows by word count in the given column."""
+    df = df.copy()
+    counts = df[col].apply(_word_count)
+
+    if min_words > 0:
+        df = df[counts >= min_words]
+        counts = counts[df.index]
+
+    if max_words > 0:
+        df = df[counts <= max_words]
+
+    return df.reset_index(drop=True)
+
+
+def contains_profanity(text: str) -> bool:
+    """Check if text contains any profanity words."""
+    if not isinstance(text, str):
+        return False
+    words = set(re.findall(r'\b\w+\b', text.lower()))
+    return bool(words & _PROFANITY_WORDS)
+
+
+def filter_profanity(
+    df: pd.DataFrame,
+    col: str,
+) -> pd.DataFrame:
+    """Remove rows containing profanity in the given column."""
+    mask = ~df[col].apply(contains_profanity)
+    return df[mask].reset_index(drop=True)
+
+
+def detect_language(text: str) -> str:
+    """
+    Detect the language of a text string.
+    Returns ISO 639-1 code (e.g., 'en', 'fr', 'de').
+    Falls back to 'unknown' if detection fails.
+    """
+    try:
+        from langdetect import detect
+        if not isinstance(text, str) or len(text.strip()) < 10:
+            return 'unknown'
+        return detect(text)
+    except ImportError:
+        return 'unknown'
+    except Exception:
+        return 'unknown'
+
+
+def filter_by_language(
+    df: pd.DataFrame,
+    col: str,
+    allowed_langs: List[str] = None,
+) -> pd.DataFrame:
+    """Keep only rows where the text is in one of the allowed languages."""
+    if allowed_langs is None:
+        allowed_langs = ['en']
+
+    langs = df[col].apply(detect_language)
+    mask = langs.isin(allowed_langs) | (langs == 'unknown')
+    return df[mask].reset_index(drop=True)
+
+
+def is_low_quality(text: str, min_len: int = 20) -> bool:
+    """
+    Check if a response is low-quality:
+    - Too short
+    - Matches generic/placeholder patterns
+    """
+    if not isinstance(text, str):
+        return True
+    text_stripped = text.strip()
+    if len(text_stripped) < min_len:
+        return True
+    text_lower = text_stripped.lower()
+    for phrase in _GENERIC_RESPONSES:
+        if text_lower == phrase or text_lower.startswith(phrase):
+            return True
+    return False
+
+
+def filter_low_quality(
+    df: pd.DataFrame,
+    col: str,
+    min_len: int = 20,
+) -> pd.DataFrame:
+    """Remove low-quality responses."""
+    mask = ~df[col].apply(lambda t: is_low_quality(t, min_len))
+    return df[mask].reset_index(drop=True)
+
+
+def apply_quality_filters(
+    df: pd.DataFrame,
+    col: str,
+    config: QualityFilterConfig,
+) -> pd.DataFrame:
+    """Apply all enabled quality filters to a DataFrame."""
+    if config.min_word_count > 0 or config.max_word_count > 0:
+        df = filter_by_word_count(df, col, config.min_word_count, config.max_word_count)
+
+    if config.profanity_filter:
+        df = filter_profanity(df, col)
+
+    if config.language_filter:
+        df = filter_by_language(df, col, config.allowed_languages)
+
+    if config.remove_low_quality:
+        df = filter_low_quality(df, col, config.min_quality_length)
+
+    return df

preprocessing/system_prompt.py

@@ -0,0 +1,80 @@
+"""
+System Prompt Configuration Module
+=====================================
+Manage global system prompts, prepend to samples,
+and preview formatted chat JSON.
+"""
+
+from dataclasses import dataclass
+from typing import List, Dict, Any, Optional
+import json
+import pandas as pd
+
+
+@dataclass
+class SystemPromptConfig:
+    """Configuration for system prompt handling."""
+    system_prompt: str = "You are a helpful AI assistant."
+    prepend_to_all: bool = True
+
+
+def build_chat_json(
+    instruction: str,
+    output: str,
+    system_prompt: str = "",
+    context: str = "",
+) -> Dict[str, Any]:
+    """
+    Build a single chat-format JSON entry.
+    Returns {"messages": [{"role": ..., "content": ...}, ...]}.
+    """
+    messages = []
+
+    if system_prompt:
+        messages.append({"role": "system", "content": system_prompt})
+
+    user_content = instruction
+    if context:
+        user_content += f"\n\nContext: {context}"
+
+    messages.append({"role": "user", "content": user_content})
+    messages.append({"role": "assistant", "content": output})
+
+    return {"messages": messages}
+
+
+def preview_formatted(
+    df: pd.DataFrame,
+    system_prompt: str,
+    instruction_col: str,
+    output_col: str,
+    input_col: Optional[str] = None,
+    n: int = 3,
+) -> List[Dict[str, Any]]:
+    """
+    Generate a preview of n formatted chat-JSON samples.
+    """
+    previews = []
+    for i, (_, row) in enumerate(df.head(n).iterrows()):
+        instruction = str(row.get(instruction_col, ''))
+        output = str(row.get(output_col, ''))
+        context = str(row.get(input_col, '')) if input_col and input_col in df.columns else ''
+        previews.append(
+            build_chat_json(instruction, output, system_prompt, context)
+        )
+    return previews
+
+
+def preview_formatted_json(
+    df: pd.DataFrame,
+    system_prompt: str,
+    instruction_col: str,
+    output_col: str,
+    input_col: Optional[str] = None,
+    n: int = 3,
+) -> str:
+    """Return a pretty-printed JSON string of n sample entries."""
+    samples = preview_formatted(
+        df, system_prompt, instruction_col, output_col, input_col, n
+    )
+    return json.dumps(samples, indent=2, ensure_ascii=False)

preprocessing/text_cleaning.py

@@ -0,0 +1,124 @@
+"""
+Text Cleaning Module
+=====================
+Pure functions for text preprocessing toggles.
+Each function operates on a single string and can be
+composed via apply_text_cleaning().
+"""
+
+import re
+import unicodedata
+from dataclasses import dataclass
+from typing import List
+import pandas as pd
+
+
+@dataclass
+class TextCleaningConfig:
+    """Configuration for text cleaning options."""
+    remove_html: bool = False
+    remove_urls: bool = False
+    remove_emojis: bool = False
+    normalize_whitespace: bool = True
+    lowercase: bool = False
+    remove_special_chars: bool = False
+    strip_extra_linebreaks: bool = True
+
+
+# ---------------------------------------------------------------------------
+# Individual cleaning functions
+# ---------------------------------------------------------------------------
+
+def remove_html_tags(text: str) -> str:
+    """Strip all HTML tags from text."""
+    return re.sub(r'<[^>]+>', '', text)
+
+
+def remove_urls(text: str) -> str:
+    """Remove URLs (http, https, ftp, www) from text."""
+    return re.sub(
+        r'https?://\S+|ftp://\S+|www\.\S+',
+        '', text
+    )
+
+
+_EMOJI_PATTERN = re.compile(
+    "["
+    "\U0001F600-\U0001F64F"  # emoticons
+    "\U0001F300-\U0001F5FF"  # symbols & pictographs
+    "\U0001F680-\U0001F6FF"  # transport & map symbols
+    "\U0001F1E0-\U0001F1FF"  # flags
+    "\U00002702-\U000027B0"
+    "\U000024C2-\U0001F251"
+    "\U0001F900-\U0001F9FF"  # supplemental symbols
+    "\U0001FA00-\U0001FA6F"
+    "\U0001FA70-\U0001FAFF"
+    "\U00002702-\U000027B0"
+    "]+",
+    flags=re.UNICODE,
+)
+
+
+def remove_emojis(text: str) -> str:
+    """Remove emoji characters from text."""
+    return _EMOJI_PATTERN.sub('', text)
+
+
+def normalize_whitespace(text: str) -> str:
+    """Collapse multiple spaces/tabs into a single space."""
+    return re.sub(r'[^\S\n]+', ' ', text).strip()
+
+
+def to_lowercase(text: str) -> str:
+    """Convert text to lowercase."""
+    return text.lower()
+
+
+def remove_special_characters(text: str) -> str:
+    """Keep only alphanumeric, basic punctuation, and whitespace."""
+    return re.sub(r'[^a-zA-Z0-9\s.,!?;:\'"()\-\n]', '', text)
+
+
+def strip_extra_linebreaks(text: str) -> str:
+    """Reduce three or more consecutive newlines to two."""
+    return re.sub(r'\n{3,}', '\n\n', text)
+
+
+# ---------------------------------------------------------------------------
+# Composed cleaner
+# ---------------------------------------------------------------------------
+
+def clean_text(text: str, config: TextCleaningConfig) -> str:
+    """Apply all enabled cleaning steps to a single text string."""
+    if not isinstance(text, str):
+        return str(text) if text else ''
+
+    if config.remove_html:
+        text = remove_html_tags(text)
+    if config.remove_urls:
+        text = remove_urls(text)
+    if config.remove_emojis:
+        text = remove_emojis(text)
+    if config.remove_special_chars:
+        text = remove_special_characters(text)
+    if config.lowercase:
+        text = to_lowercase(text)
+    if config.normalize_whitespace:
+        text = normalize_whitespace(text)
+    if config.strip_extra_linebreaks:
+        text = strip_extra_linebreaks(text)
+
+    return text
+
+
+def apply_text_cleaning(
+    df: pd.DataFrame,
+    columns: List[str],
+    config: TextCleaningConfig,
+) -> pd.DataFrame:
+    """Apply text cleaning to specified columns of a DataFrame."""
+    df = df.copy()
+    for col in columns:
+        if col in df.columns:
+            df[col] = df[col].apply(lambda t: clean_text(t, config))
+    return df

preprocessing/tokenization.py

@@ -0,0 +1,147 @@
+"""
+Tokenization Controls Module
+==============================
+Tokenizer selection, token counting, truncation, and splitting.
+Supports tiktoken (OpenAI) and HuggingFace tokenizers.
+"""
+
+from dataclasses import dataclass
+from typing import Dict, List, Any, Optional
+import pandas as pd
+import numpy as np
+
+
+@dataclass
+class TokenizationConfig:
+    """Configuration for tokenization controls."""
+    tokenizer_name: str = "tiktoken"          # "tiktoken" or HF model name
+    tiktoken_encoding: str = "cl100k_base"    # for tiktoken
+    max_total_tokens: int = 2048
+    truncate_long: bool = False
+    split_long: bool = False
+    split_overlap: int = 50                   # overlap tokens when splitting
+
+
+def get_tokenizer(config: TokenizationConfig):
+    """
+    Return a tokenizer-like object.
+    For tiktoken: returns the encoding object.
+    For HF: returns AutoTokenizer instance.
+    """
+    if config.tokenizer_name == "tiktoken":
+        try:
+            import tiktoken
+            return tiktoken.get_encoding(config.tiktoken_encoding)
+        except ImportError:
+            raise ImportError("tiktoken is required. Install with: pip install tiktoken")
+    else:
+        try:
+            from transformers import AutoTokenizer
+            return AutoTokenizer.from_pretrained(config.tokenizer_name)
+        except ImportError:
+            raise ImportError("transformers is required for HF tokenizers.")
+
+
+def count_tokens(text: str, tokenizer, is_tiktoken: bool = True) -> int:
+    """Count tokens in a text string."""
+    if not isinstance(text, str) or not text.strip():
+        return 0
+    if is_tiktoken:
+        return len(tokenizer.encode(text))
+    else:
+        return len(tokenizer.encode(text, add_special_tokens=False))
+
+
+def compute_token_stats(
+    df: pd.DataFrame,
+    columns: List[str],
+    tokenizer,
+    is_tiktoken: bool = True,
+) -> Dict[str, Dict[str, float]]:
+    """
+    Compute token statistics for specified columns.
+    Returns dict of column -> {min, max, mean, median, p95, total}.
+    """
+    stats = {}
+    for col in columns:
+        if col not in df.columns:
+            continue
+        counts = df[col].apply(lambda t: count_tokens(t, tokenizer, is_tiktoken))
+        stats[col] = {
+            'min': int(counts.min()) if len(counts) > 0 else 0,
+            'max': int(counts.max()) if len(counts) > 0 else 0,
+            'mean': round(float(counts.mean()), 1) if len(counts) > 0 else 0,
+            'median': int(counts.median()) if len(counts) > 0 else 0,
+            'p95': int(np.percentile(counts, 95)) if len(counts) > 0 else 0,
+            'total': int(counts.sum()),
+        }
+    return stats
+
+
+def truncate_samples(
+    df: pd.DataFrame,
+    col: str,
+    max_tokens: int,
+    tokenizer,
+    is_tiktoken: bool = True,
+) -> pd.DataFrame:
+    """Truncate text in a column to max_tokens."""
+    df = df.copy()
+
+    def _truncate(text):
+        if not isinstance(text, str):
+            return text
+        if is_tiktoken:
+            tokens = tokenizer.encode(text)
+            if len(tokens) > max_tokens:
+                return tokenizer.decode(tokens[:max_tokens])
+        else:
+            tokens = tokenizer.encode(text, add_special_tokens=False)
+            if len(tokens) > max_tokens:
+                return tokenizer.decode(tokens[:max_tokens])
+        return text
+
+    df[col] = df[col].apply(_truncate)
+    return df
+
+
+def split_long_samples(
+    df: pd.DataFrame,
+    col: str,
+    max_tokens: int,
+    tokenizer,
+    is_tiktoken: bool = True,
+    overlap: int = 50,
+) -> pd.DataFrame:
+    """
+    Split rows whose text exceeds max_tokens into multiple rows.
+    Each chunk has `overlap` tokens of context from the previous chunk.
+    """
+    new_rows = []
+    for _, row in df.iterrows():
+        text = row[col]
+        if not isinstance(text, str):
+            new_rows.append(row)
+            continue
+
+        if is_tiktoken:
+            tokens = tokenizer.encode(text)
+        else:
+            tokens = tokenizer.encode(text, add_special_tokens=False)
+
+        if len(tokens) <= max_tokens:
+            new_rows.append(row)
+        else:
+            step = max(1, max_tokens - overlap)
+            for i in range(0, len(tokens), step):
+                chunk_tokens = tokens[i:i + max_tokens]
+                if not chunk_tokens:
+                    break
+                new_row = row.copy()
+                if is_tiktoken:
+                    new_row[col] = tokenizer.decode(chunk_tokens)
+                else:
+                    new_row[col] = tokenizer.decode(chunk_tokens)
+                new_rows.append(new_row)
+
+    return pd.DataFrame(new_rows).reset_index(drop=True)

preprocessing/train_val_split.py

@@ -0,0 +1,41 @@
+"""
+Train / Validation Split Module
+==================================
+Split datasets with configurable ratio, seed, and shuffle.
+"""
+
+from dataclasses import dataclass
+from typing import Tuple
+import pandas as pd
+
+
+@dataclass
+class SplitConfig:
+    """Configuration for train/validation split."""
+    enabled: bool = True
+    train_ratio: float = 0.8  # e.g., 0.8 means 80% train, 20% val
+    random_seed: int = 42
+    shuffle: bool = True
+
+
+def split_dataset(
+    df: pd.DataFrame,
+    config: SplitConfig,
+) -> Tuple[pd.DataFrame, pd.DataFrame]:
+    """
+    Split DataFrame into train and validation sets.
+
+    Returns:
+        (train_df, val_df) tuple
+    """
+    if not config.enabled:
+        return df, pd.DataFrame(columns=df.columns)
+
+    if config.shuffle:
+        df = df.sample(frac=1, random_state=config.random_seed).reset_index(drop=True)
+
+    split_idx = int(len(df) * config.train_ratio)
+    train_df = df.iloc[:split_idx].reset_index(drop=True)
+    val_df = df.iloc[split_idx:].reset_index(drop=True)
+
+    return train_df, val_df

requirements.txt

@@ -0,0 +1,42 @@
+# Auto-FineTune-Ops Dependencies
+# Core ML Libraries
+unsloth @ git+https://github.com/unslothai/unsloth.git
+trl>=0.7.0
+peft>=0.7.0
+transformers>=4.36.0
+datasets>=2.14.0
+accelerate>=0.25.0
+bitsandbytes>=0.41.0
+
+# Data Processing
+pandas>=2.0.0
+numpy>=1.24.0
+
+# Advanced Preprocessing
+tiktoken>=0.5.0
+langdetect>=1.0.9
+scikit-learn>=1.3.0
+
+# API & Deployment
+fastapi>=0.104.0
+uvicorn>=0.24.0
+python-multipart>=0.0.6
+
+# LLM Judge Clients
+openai>=1.0.0
+anthropic>=0.8.0
+
+# Utilities
+pyyaml>=6.0
+python-dotenv>=1.0.0
+rich>=13.0.0
+typer>=0.9.0
+tqdm>=4.66.0
+
+# Dashboard
+streamlit>=1.32.0
+plotly>=5.18.0
+
+# CUDA/Torch (install separately based on your CUDA version)
+# torch>=2.1.0
+# xformers>=0.0.23

scripts/__init__.py

@@ -0,0 +1,5 @@
+"""Auto-FineTune-Ops Scripts Package"""
+
+from .deploy import DeploymentServer
+
+__all__ = ["DeploymentServer"]

scripts/deploy.py

@@ -0,0 +1,375 @@
+"""
+FastAPI Deployment Server
+==========================
+One-click deployment bridge for fine-tuned models.
+"""
+
+import os
+from pathlib import Path
+from typing import Optional, List, Dict, Any
+from dataclasses import dataclass
+from datetime import datetime
+
+from rich.console import Console
+
+console = Console()
+
+
+@dataclass
+class GenerationRequest:
+    """Request model for text generation."""
+    prompt: str
+    system_prompt: Optional[str] = None
+    max_tokens: int = 512
+    temperature: float = 0.7
+    top_p: float = 0.9
+    stream: bool = False
+
+
+@dataclass
+class GenerationResponse:
+    """Response model for text generation."""
+    generated_text: str
+    prompt: str
+    model: str
+    tokens_generated: int
+    generation_time: float
+
+
+class DeploymentServer:
+    """
+    FastAPI-based deployment server for fine-tuned models.
+    
+    Features:
+    - RESTful API for inference
+    - Health check endpoint
+    - Batch generation support
+    - Automatic model loading
+    """
+    
+    def __init__(
+        self,
+        model_path: str,
+        host: str = "0.0.0.0",
+        port: int = 8000,
+        max_seq_length: int = 2048
+    ):
+        """
+        Initialize the deployment server.
+        
+        Args:
+            model_path: Path to the fine-tuned model
+            host: Server host
+            port: Server port
+            max_seq_length: Maximum sequence length
+        """
+        self.model_path = model_path
+        self.host = host
+        self.port = port
+        self.max_seq_length = max_seq_length
+        
+        self.model = None
+        self.tokenizer = None
+        self.app = None
+        
+    def load_model(self):
+        """Load the fine-tuned model."""
+        console.print(f"\n[bold blue]📂 Loading model from:[/] {self.model_path}")
+        
+        try:
+            from unsloth import FastLanguageModel
+            
+            self.model, self.tokenizer = FastLanguageModel.from_pretrained(
+                model_name=self.model_path,
+                max_seq_length=self.max_seq_length,
+                dtype=None,
+                load_in_4bit=True,
+            )
+            
+            FastLanguageModel.for_inference(self.model)
+            
+            console.print("[green]✓ Model loaded successfully[/]")
+            
+        except ImportError:
+            console.print("[yellow]⚠️ Unsloth not available, trying transformers...[/]")
+            
+            from transformers import AutoModelForCausalLM, AutoTokenizer
+            
+            self.tokenizer = AutoTokenizer.from_pretrained(self.model_path)
+            self.model = AutoModelForCausalLM.from_pretrained(
+                self.model_path,
+                device_map="auto",
+                torch_dtype="auto"
+            )
+            
+            console.print("[green]✓ Model loaded with transformers[/]")
+    
+    def generate(
+        self,
+        prompt: str,
+        system_prompt: Optional[str] = None,
+        max_tokens: int = 512,
+        temperature: float = 0.7,
+        top_p: float = 0.9
+    ) -> GenerationResponse:
+        """
+        Generate text from the model.
+        
+        Args:
+            prompt: User prompt
+            system_prompt: Optional system prompt
+            max_tokens: Maximum tokens to generate
+            temperature: Sampling temperature
+            top_p: Top-p sampling parameter
+            
+        Returns:
+            GenerationResponse with generated text
+        """
+        if self.model is None:
+            raise RuntimeError("Model not loaded. Call load_model() first.")
+        
+        start_time = datetime.now()
+        
+        # Format prompt with Alpaca template
+        if system_prompt:
+            formatted_prompt = f"""{system_prompt}
+
+### Instruction:
+{prompt}
+
+### Response:
+"""
+        else:
+            formatted_prompt = f"""Below is an instruction that describes a task. Write a response that appropriately completes the request.
+
+### Instruction:
+{prompt}
+
+### Response:
+"""
+        
+        # Tokenize
+        inputs = self.tokenizer(
+            formatted_prompt,
+            return_tensors="pt"
+        ).to(self.model.device)
+        
+        # Generate
+        outputs = self.model.generate(
+            **inputs,
+            max_new_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            do_sample=True,
+            pad_token_id=self.tokenizer.eos_token_id
+        )
+        
+        # Decode
+        full_response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
+        
+        # Extract just the generated part
+        if "### Response:" in full_response:
+            generated_text = full_response.split("### Response:")[-1].strip()
+        else:
+            generated_text = full_response[len(formatted_prompt):].strip()
+        
+        generation_time = (datetime.now() - start_time).total_seconds()
+        tokens_generated = len(self.tokenizer.encode(generated_text))
+        
+        return GenerationResponse(
+            generated_text=generated_text,
+            prompt=prompt,
+            model=self.model_path,
+            tokens_generated=tokens_generated,
+            generation_time=generation_time
+        )
+    
+    def create_app(self):
+        """Create the FastAPI application."""
+        from fastapi import FastAPI, HTTPException
+        from fastapi.middleware.cors import CORSMiddleware
+        from pydantic import BaseModel
+        from typing import List, Optional
+        
+        app = FastAPI(
+            title="Auto-FineTune-Ops Inference API",
+            description="API for serving fine-tuned LLM models",
+            version="1.0.0"
+        )
+        
+        # CORS middleware
+        app.add_middleware(
+            CORSMiddleware,
+            allow_origins=["*"],
+            allow_credentials=True,
+            allow_methods=["*"],
+            allow_headers=["*"],
+        )
+        
+        # Pydantic models for API
+        class GenerateRequest(BaseModel):
+            prompt: str
+            system_prompt: Optional[str] = None
+            max_tokens: int = 512
+            temperature: float = 0.7
+            top_p: float = 0.9
+        
+        class GenerateResponse(BaseModel):
+            generated_text: str
+            prompt: str
+            model: str
+            tokens_generated: int
+            generation_time: float
+        
+        class BatchGenerateRequest(BaseModel):
+            prompts: List[str]
+            system_prompt: Optional[str] = None
+            max_tokens: int = 512
+            temperature: float = 0.7
+            top_p: float = 0.9
+        
+        class HealthResponse(BaseModel):
+            status: str
+            model: str
+            model_loaded: bool
+        
+        @app.get("/health", response_model=HealthResponse)
+        async def health_check():
+            """Health check endpoint."""
+            return HealthResponse(
+                status="healthy",
+                model=self.model_path,
+                model_loaded=self.model is not None
+            )
+        
+        @app.post("/generate", response_model=GenerateResponse)
+        async def generate_text(request: GenerateRequest):
+            """Generate text from a single prompt."""
+            if self.model is None:
+                raise HTTPException(status_code=503, detail="Model not loaded")
+            
+            try:
+                result = self.generate(
+                    prompt=request.prompt,
+                    system_prompt=request.system_prompt,
+                    max_tokens=request.max_tokens,
+                    temperature=request.temperature,
+                    top_p=request.top_p
+                )
+                
+                return GenerateResponse(
+                    generated_text=result.generated_text,
+                    prompt=result.prompt,
+                    model=result.model,
+                    tokens_generated=result.tokens_generated,
+                    generation_time=result.generation_time
+                )
+            except Exception as e:
+                raise HTTPException(status_code=500, detail=str(e))
+        
+        @app.post("/generate/batch", response_model=List[GenerateResponse])
+        async def batch_generate(request: BatchGenerateRequest):
+            """Generate text from multiple prompts."""
+            if self.model is None:
+                raise HTTPException(status_code=503, detail="Model not loaded")
+            
+            results = []
+            for prompt in request.prompts:
+                try:
+                    result = self.generate(
+                        prompt=prompt,
+                        system_prompt=request.system_prompt,
+                        max_tokens=request.max_tokens,
+                        temperature=request.temperature,
+                        top_p=request.top_p
+                    )
+                    results.append(GenerateResponse(
+                        generated_text=result.generated_text,
+                        prompt=result.prompt,
+                        model=result.model,
+                        tokens_generated=result.tokens_generated,
+                        generation_time=result.generation_time
+                    ))
+                except Exception as e:
+                    results.append(GenerateResponse(
+                        generated_text=f"Error: {str(e)}",
+                        prompt=prompt,
+                        model=self.model_path,
+                        tokens_generated=0,
+                        generation_time=0.0
+                    ))
+            
+            return results
+        
+        @app.get("/")
+        async def root():
+            """Root endpoint with API info."""
+            return {
+                "name": "Auto-FineTune-Ops Inference API",
+                "version": "1.0.0",
+                "model": self.model_path,
+                "endpoints": {
+                    "/health": "Health check",
+                    "/generate": "Generate text (POST)",
+                    "/generate/batch": "Batch generation (POST)"
+                }
+            }
+        
+        self.app = app
+        return app
+    
+    def run(self, reload: bool = False):
+        """
+        Start the FastAPI server.
+        
+        Args:
+            reload: Enable auto-reload for development
+        """
+        import uvicorn
+        
+        console.print("\n" + "="*60)
+        console.print("[bold magenta]🚀 DEPLOYMENT SERVER[/]")
+        console.print("="*60)
+        
+        # Load model if not already loaded
+        if self.model is None:
+            self.load_model()
+        
+        # Create app if not already created
+        if self.app is None:
+            self.create_app()
+        
+        console.print(f"\n[bold green]Starting server at http://{self.host}:{self.port}[/]")
+        console.print("[dim]Press Ctrl+C to stop[/]\n")
+        
+        uvicorn.run(
+            self.app,
+            host=self.host,
+            port=self.port,
+            reload=reload
+        )
+
+
+def main():
+    """CLI entry point for deployment."""
+    import argparse
+    
+    parser = argparse.ArgumentParser(description="Deploy fine-tuned model as API")
+    parser.add_argument("--model", required=True, help="Path to fine-tuned model")
+    parser.add_argument("--host", default="0.0.0.0", help="Server host")
+    parser.add_argument("--port", type=int, default=8000, help="Server port")
+    parser.add_argument("--reload", action="store_true", help="Enable auto-reload")
+    
+    args = parser.parse_args()
+    
+    server = DeploymentServer(
+        model_path=args.model,
+        host=args.host,
+        port=args.port
+    )
+    
+    server.run(reload=args.reload)
+
+
+if __name__ == "__main__":
+    main()