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import gradio as gr |
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import os |
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import json |
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import torch |
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from transformers import ( |
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AutoTokenizer, AutoModelForCausalLM, |
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TrainingArguments, Trainer, |
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DataCollatorForLanguageModeling, |
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pipeline |
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) |
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from datasets import Dataset |
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from huggingface_hub import HfApi, login |
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import spaces |
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from typing import Optional, Dict, Any, List, Tuple |
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import logging |
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import traceback |
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from datetime import datetime |
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import random |
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import re |
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from faker import Faker |
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import hashlib |
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import time |
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from collections import defaultdict |
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from functools import wraps |
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logging.basicConfig( |
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level=logging.INFO, |
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format='%(asctime)s - %(name)s - %(levelname)s - %(message)s' |
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) |
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logger = logging.getLogger(__name__) |
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class RateLimiter: |
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"""Token bucket rate limiter""" |
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def __init__(self): |
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self.requests = defaultdict(list) |
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self.limits = { |
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'synthetic_generation': {'calls': 10, 'period': 3600}, |
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'model_training': {'calls': 3, 'period': 3600}, |
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'model_inference': {'calls': 50, 'period': 3600}, |
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} |
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def _get_user_id(self, request: gr.Request) -> str: |
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if request: |
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identifier = f"{request.client.host}_{request.headers.get('user-agent', '')}" |
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return hashlib.md5(identifier.encode()).hexdigest() |
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return "anonymous" |
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def _clean_old_requests(self, user_id: str, endpoint: str): |
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if user_id not in self.requests: |
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return |
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current_time = time.time() |
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period = self.limits[endpoint]['period'] |
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self.requests[user_id] = [ |
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req for req in self.requests[user_id] |
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if req['endpoint'] == endpoint and current_time - req['timestamp'] < period |
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] |
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def check_rate_limit(self, user_id: str, endpoint: str) -> Tuple[bool, str]: |
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self._clean_old_requests(user_id, endpoint) |
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user_requests = [req for req in self.requests[user_id] if req['endpoint'] == endpoint] |
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limit = self.limits[endpoint]['calls'] |
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period = self.limits[endpoint]['period'] |
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if len(user_requests) >= limit: |
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time_until_reset = period - (time.time() - user_requests[0]['timestamp']) |
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minutes = int(time_until_reset / 60) |
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return False, f"⏱️ Rate limit exceeded! Please wait {minutes} minutes." |
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self.requests[user_id].append({'endpoint': endpoint, 'timestamp': time.time()}) |
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remaining = limit - len(user_requests) - 1 |
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return True, f"✅ Request accepted ({remaining} remaining this hour)" |
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rate_limiter = RateLimiter() |
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def rate_limit(endpoint: str): |
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def decorator(func): |
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@wraps(func) |
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def wrapper(*args, **kwargs): |
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request = kwargs.get('request', None) |
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if request: |
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user_id = rate_limiter._get_user_id(request) |
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allowed, message = rate_limiter.check_rate_limit(user_id, endpoint) |
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if not allowed: |
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return f"🚫 {message}" |
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return func(*args, **kwargs) |
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return wrapper |
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return decorator |
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class AuthManager: |
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def __init__(self): |
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self.authenticated_tokens = {} |
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self.token_expiry = 86400 |
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def validate_hf_token(self, token: str) -> Tuple[bool, str, Optional[str]]: |
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try: |
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if not token or not token.strip(): |
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return False, "❌ Please provide a HuggingFace token", None |
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token_hash = hashlib.sha256(token.encode()).hexdigest() |
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if token_hash in self.authenticated_tokens: |
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cached = self.authenticated_tokens[token_hash] |
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if time.time() - cached['timestamp'] < self.token_expiry: |
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return True, f"✅ Welcome back, {cached['username']}!", cached['username'] |
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api = HfApi(token=token) |
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user_info = api.whoami() |
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username = user_info.get('name', 'Anonymous Architect') |
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self.authenticated_tokens[token_hash] = { |
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'username': username, |
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'timestamp': time.time() |
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} |
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return True, f"🎉 Welcome, {username}!", username |
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except Exception as e: |
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return False, f"🔐 Token validation failed: {str(e)}", None |
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auth_manager = AuthManager() |
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class ArchitechError(Exception): |
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pass |
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class DataGenerationError(ArchitechError): |
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pass |
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class ModelTrainingError(ArchitechError): |
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pass |
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class ModelInferenceError(ArchitechError): |
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pass |
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def handle_errors(error_type: str = "general"): |
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def decorator(func): |
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@wraps(func) |
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def wrapper(*args, **kwargs): |
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try: |
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return func(*args, **kwargs) |
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except torch.cuda.OutOfMemoryError: |
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return "🔥 **GPU Memory Overflow!** Try: smaller batch size, smaller model, or less data." |
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except PermissionError: |
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return "🔒 **Permission Denied!** Check your HuggingFace token has WRITE access." |
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except ConnectionError: |
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return "🌐 **Connection Issue!** Can't reach HuggingFace. Check your network." |
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except ValueError as e: |
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return f"⚠️ **Invalid Input!** {str(e)}" |
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except (DataGenerationError, ModelTrainingError, ModelInferenceError) as e: |
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return f"🔧 **Architech Error:** {str(e)}" |
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except Exception as e: |
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logger.error(f"Error in {func.__name__}: {traceback.format_exc()}") |
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return f"💥 **Unexpected Error:** {str(e)}" |
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return wrapper |
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return decorator |
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class SyntheticDataGenerator: |
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def __init__(self): |
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self.faker = Faker() |
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self.generation_templates = { |
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"conversational": [ |
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"Human: {question}\nAssistant: {answer}", |
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"User: {question}\nBot: {answer}", |
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], |
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"instruction": [ |
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"### Instruction:\n{instruction}\n\n### Response:\n{response}", |
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], |
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} |
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self.domain_knowledge = { |
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"technology": { |
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"topics": ["AI", "machine learning", "cloud computing"], |
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"concepts": ["algorithms", "APIs", "databases"], |
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"contexts": ["software development", "digital transformation"] |
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}, |
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"healthcare": { |
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"topics": ["telemedicine", "diagnostics", "patient care"], |
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"concepts": ["treatments", "procedures"], |
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"contexts": ["clinical practice", "patient education"] |
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}, |
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"finance": { |
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"topics": ["fintech", "investment", "risk management"], |
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"concepts": ["portfolios", "compliance"], |
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"contexts": ["financial advisory", "personal finance"] |
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}, |
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"general": { |
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"topics": ["communication", "problem-solving"], |
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"concepts": ["strategies", "best practices"], |
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"contexts": ["daily life", "personal growth"] |
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} |
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} |
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def _generate_question(self, topic, concept, context): |
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templates = [ |
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f"How does {concept} work in {context}?", |
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f"What are the benefits of {concept} for {topic}?", |
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f"Can you explain {concept}?", |
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f"What's the best approach to {concept}?" |
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] |
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return random.choice(templates) |
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def _generate_answer(self, question, topic, concept): |
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templates = [ |
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f"{concept} in {topic} works through strategic implementation. Key benefits include improved efficiency and better outcomes.", |
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f"Great question! {concept} is fundamental because it addresses core challenges. Best practices include planning and testing.", |
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f"When it comes to {concept}, consider scalability and performance. Success depends on proper implementation." |
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] |
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return random.choice(templates) |
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def _generate_single_example(self, task_desc, domain_data, templates, complexity): |
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template = random.choice(templates) |
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topic = random.choice(domain_data["topics"]) |
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concept = random.choice(domain_data["concepts"]) |
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context = random.choice(domain_data["contexts"]) |
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question = self._generate_question(topic, concept, context) |
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answer = self._generate_answer(question, topic, concept) |
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text = template.format(question=question, answer=answer) |
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return {"text": text} |
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@handle_errors("data_generation") |
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def generate_synthetic_dataset( |
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self, |
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task_description: str, |
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domain: str, |
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dataset_size: int = 100, |
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format_type: str = "conversational", |
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complexity: str = "medium", |
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progress=gr.Progress() |
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) -> str: |
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if not task_description or len(task_description.strip()) < 10: |
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raise DataGenerationError("Task description too short! Need at least 10 characters.") |
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if dataset_size < 10 or dataset_size > 1000: |
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raise DataGenerationError("Dataset size must be between 10 and 1000.") |
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progress(0.1, f"🎯 Generating {dataset_size} examples...") |
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domain_data = self.domain_knowledge.get(domain, self.domain_knowledge["general"]) |
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templates = self.generation_templates.get(format_type, self.generation_templates["conversational"]) |
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synthetic_data = [] |
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for i in range(dataset_size): |
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if i % 20 == 0: |
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progress(0.1 + (0.7 * i / dataset_size), f"📝 Creating {i+1}/{dataset_size}...") |
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example = self._generate_single_example(task_description, domain_data, templates, complexity) |
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synthetic_data.append(example) |
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os.makedirs("./synthetic_datasets", exist_ok=True) |
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dataset_filename = f"synthetic_{domain}_{format_type}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.json" |
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dataset_path = os.path.join("./synthetic_datasets", dataset_filename) |
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with open(dataset_path, 'w') as f: |
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json.dump(synthetic_data, f, indent=2) |
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preview = "\n\n---\n\n".join([ex["text"] for ex in synthetic_data[:3]]) |
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return f"""🎊 **SYNTHETIC DATASET GENERATED!** |
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|
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**Dataset Details:** |
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- 📊 Size: {len(synthetic_data)} examples |
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- 🎯 Domain: {domain.title()} |
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- 📝 Format: {format_type.title()} |
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- 💾 Saved as: `{dataset_filename}` |
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**Preview (First 3 Examples):** |
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{preview} |
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**Next Steps:** Use this in the 'Train Model' or 'Test Model' tabs!""" |
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class ModelInference: |
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def __init__(self): |
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self.loaded_models = {} |
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@handle_errors("inference") |
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def load_model(self, model_name: str, hf_token: str, progress=gr.Progress()) -> str: |
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progress(0.1, "🔍 Locating your model...") |
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is_valid, message, username = auth_manager.validate_hf_token(hf_token) |
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if not is_valid: |
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raise ModelInferenceError(message) |
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full_model_name = f"{username}/{model_name}" if "/" not in model_name else model_name |
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progress(0.3, "📥 Downloading model...") |
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try: |
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tokenizer = AutoTokenizer.from_pretrained(full_model_name, token=hf_token) |
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model = AutoModelForCausalLM.from_pretrained( |
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full_model_name, |
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token=hf_token, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
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device_map="auto" if torch.cuda.is_available() else None |
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) |
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self.loaded_models[model_name] = { |
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'model': model, |
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'tokenizer': tokenizer, |
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'pipeline': pipeline('text-generation', model=model, tokenizer=tokenizer) |
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} |
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progress(1.0, "✅ Model loaded!") |
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return f"✅ **Model Loaded Successfully!**\n\nModel: `{full_model_name}`\n\nReady for inference!" |
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|
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except Exception as e: |
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raise ModelInferenceError(f"Failed to load model: {str(e)}") |
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@handle_errors("inference") |
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def generate_text( |
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self, |
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model_name: str, |
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prompt: str, |
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max_length: int = 100, |
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temperature: float = 0.7, |
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top_p: float = 0.9 |
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) -> str: |
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if model_name not in self.loaded_models: |
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raise ModelInferenceError("Model not loaded! Please load the model first.") |
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if not prompt or len(prompt.strip()) < 3: |
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raise ModelInferenceError("Prompt too short! Please provide at least 3 characters.") |
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pipe = self.loaded_models[model_name]['pipeline'] |
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result = pipe( |
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prompt, |
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max_length=max_length, |
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temperature=temperature, |
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top_p=top_p, |
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do_sample=True, |
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num_return_sequences=1 |
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) |
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generated_text = result[0]['generated_text'] |
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return f"""**🎯 Generated Response:** |
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{generated_text} |
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--- |
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*Model: {model_name} | Length: {len(generated_text)} chars*""" |
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model_inference = ModelInference() |
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|
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class ArchitechAgent: |
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def __init__(self): |
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self.hf_api = HfApi() |
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self.synthetic_generator = SyntheticDataGenerator() |
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self.personality_responses = [ |
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"🎯 Let's cook up some AI magic!", |
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"🚀 Time to turn your vision into reality!", |
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"🧠 Let's architect some brilliance!", |
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] |
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def get_personality_response(self) -> str: |
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return random.choice(self.personality_responses) |
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|
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@rate_limit('synthetic_generation') |
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@handle_errors("data_generation") |
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def generate_synthetic_dataset_wrapper(self, *args, **kwargs): |
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return self.synthetic_generator.generate_synthetic_dataset(*args, **kwargs) |
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|
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@spaces.GPU |
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@rate_limit('model_training') |
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@handle_errors("training") |
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def train_custom_model( |
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self, |
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task_description: str, |
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training_data: str, |
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model_name: str, |
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hf_token: str, |
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base_model: str = "distilgpt2", |
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use_synthetic_data: bool = True, |
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synthetic_domain: str = "general", |
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synthetic_size: int = 100, |
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learning_rate: float = 2e-4, |
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num_epochs: int = 3, |
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batch_size: int = 2, |
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progress=gr.Progress() |
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|
) -> str: |
|
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|
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is_valid, message, username = auth_manager.validate_hf_token(hf_token) |
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if not is_valid: |
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raise ModelTrainingError(message) |
|
|
|
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|
progress(0.1, "🧠 Loading base model...") |
|
|
|
|
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tokenizer = AutoTokenizer.from_pretrained(base_model) |
|
|
if tokenizer.pad_token is None: |
|
|
tokenizer.pad_token = tokenizer.eos_token |
|
|
|
|
|
model = AutoModelForCausalLM.from_pretrained( |
|
|
base_model, |
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torch_dtype=torch.float16 if torch.cuda.is_available() else torch.float32, |
|
|
device_map="auto" if torch.cuda.is_available() else None |
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) |
|
|
|
|
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if use_synthetic_data: |
|
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progress(0.2, "🎨 Generating synthetic data...") |
|
|
result = self.synthetic_generator.generate_synthetic_dataset( |
|
|
task_description, synthetic_domain, synthetic_size, "conversational", "medium", progress |
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|
) |
|
|
|
|
|
dataset_files = [f for f in os.listdir("./synthetic_datasets") if f.endswith('.json')] |
|
|
if not dataset_files: |
|
|
raise ModelTrainingError("No synthetic dataset found!") |
|
|
|
|
|
latest_dataset = max(dataset_files, key=lambda x: os.path.getctime(os.path.join("./synthetic_datasets", x))) |
|
|
with open(os.path.join("./synthetic_datasets", latest_dataset), 'r') as f: |
|
|
synthetic_data = json.load(f) |
|
|
texts = [item["text"] for item in synthetic_data] |
|
|
else: |
|
|
|
|
|
if training_data.strip().endswith('.json') and os.path.exists(training_data.strip()): |
|
|
|
|
|
texts = dataset_manager.load_dataset_for_training(training_data.strip()) |
|
|
else: |
|
|
|
|
|
texts = [t.strip() for t in training_data.split("\n\n") if t.strip()] |
|
|
|
|
|
if not texts: |
|
|
raise ModelTrainingError("No training data available!") |
|
|
|
|
|
progress(0.3, f"✨ Tokenizing {len(texts)} examples...") |
|
|
|
|
|
dataset = Dataset.from_dict({"text": texts}) |
|
|
|
|
|
def tokenize_function(examples): |
|
|
return tokenizer(examples["text"], truncation=True, padding=True, max_length=256) |
|
|
|
|
|
tokenized_dataset = dataset.map(tokenize_function, batched=True, remove_columns=["text"]) |
|
|
|
|
|
progress(0.4, "⚙️ Configuring training...") |
|
|
|
|
|
training_args = TrainingArguments( |
|
|
output_dir=f"./results_{model_name}", |
|
|
num_train_epochs=num_epochs, |
|
|
per_device_train_batch_size=batch_size, |
|
|
gradient_accumulation_steps=4, |
|
|
learning_rate=learning_rate, |
|
|
logging_steps=50, |
|
|
save_steps=500, |
|
|
save_total_limit=2, |
|
|
fp16=torch.cuda.is_available(), |
|
|
report_to="none" |
|
|
) |
|
|
|
|
|
data_collator = DataCollatorForLanguageModeling(tokenizer=tokenizer, mlm=False) |
|
|
|
|
|
trainer = Trainer( |
|
|
model=model, |
|
|
args=training_args, |
|
|
train_dataset=tokenized_dataset, |
|
|
data_collator=data_collator, |
|
|
) |
|
|
|
|
|
progress(0.6, "💪 Training in progress...") |
|
|
trainer.train() |
|
|
|
|
|
progress(0.8, "💾 Saving model...") |
|
|
output_dir = f"./trained_{model_name}" |
|
|
trainer.save_model(output_dir) |
|
|
tokenizer.save_pretrained(output_dir) |
|
|
|
|
|
progress(0.9, "📤 Pushing to HuggingFace...") |
|
|
|
|
|
try: |
|
|
login(token=hf_token) |
|
|
|
|
|
|
|
|
max_retries = 3 |
|
|
for attempt in range(max_retries): |
|
|
try: |
|
|
progress(0.9 + (attempt * 0.03), f"📤 Upload attempt {attempt + 1}/{max_retries}...") |
|
|
|
|
|
|
|
|
model.push_to_hub( |
|
|
model_name, |
|
|
token=hf_token, |
|
|
max_shard_size="500MB", |
|
|
safe_serialization=True |
|
|
) |
|
|
tokenizer.push_to_hub(model_name, token=hf_token) |
|
|
|
|
|
hub_url = f"https://huggingface.co/{username}/{model_name}" |
|
|
|
|
|
return f"""🎉 **TRAINING COMPLETE!** |
|
|
|
|
|
✅ Training successful |
|
|
💾 Model saved locally |
|
|
📤 Pushed to Hub |
|
|
🔗 **Your model:** {hub_url} |
|
|
|
|
|
**Stats:** |
|
|
- Examples: {len(texts)} |
|
|
- Epochs: {num_epochs} |
|
|
- Learning rate: {learning_rate} |
|
|
|
|
|
**Test it in the 'Test Model' tab!**""" |
|
|
|
|
|
except Exception as upload_error: |
|
|
if attempt < max_retries - 1: |
|
|
logger.warning(f"Upload attempt {attempt + 1} failed: {upload_error}") |
|
|
time.sleep(5) |
|
|
continue |
|
|
else: |
|
|
raise upload_error |
|
|
|
|
|
except Exception as e: |
|
|
logger.error(f"Upload failed after retries: {e}") |
|
|
|
|
|
|
|
|
return f"""✅ **TRAINING COMPLETE!** (Upload timed out) |
|
|
|
|
|
💾 Model saved locally at: `{output_dir}` |
|
|
|
|
|
**Manual Upload Instructions:** |
|
|
1. Download your Space's files (or access via SSH if enabled) |
|
|
2. Run this command locally: |
|
|
```bash |
|
|
huggingface-cli upload {username}/{model_name} {output_dir} |
|
|
``` |
|
|
|
|
|
Or use the Python API: |
|
|
```python |
|
|
from huggingface_hub import HfApi |
|
|
api = HfApi() |
|
|
api.upload_folder( |
|
|
folder_path="{output_dir}", |
|
|
repo_id="{username}/{model_name}", |
|
|
token="YOUR_TOKEN" |
|
|
) |
|
|
``` |
|
|
|
|
|
**Stats:** |
|
|
- Examples: {len(texts)} |
|
|
- Epochs: {num_epochs} |
|
|
- Model saved successfully! |
|
|
|
|
|
**You can still test it locally or manually upload!**""" |
|
|
|
|
|
import zipfile |
|
|
import shutil |
|
|
from pathlib import Path |
|
|
|
|
|
class ModelManager: |
|
|
def __init__(self): |
|
|
self.models_dir = Path("./saved_models") |
|
|
self.models_dir.mkdir(exist_ok=True) |
|
|
|
|
|
@handle_errors("model_management") |
|
|
def create_model_zip(self, model_path: str, model_name: str) -> Tuple[str, str]: |
|
|
"""Create a downloadable zip of a trained model""" |
|
|
if not os.path.exists(model_path): |
|
|
raise ArchitechError(f"Model path not found: {model_path}") |
|
|
|
|
|
zip_filename = f"{model_name}_{datetime.now().strftime('%Y%m%d_%H%M%S')}.zip" |
|
|
zip_path = os.path.join(self.models_dir, zip_filename) |
|
|
|
|
|
|
|
|
with zipfile.ZipFile(zip_path, 'w', zipfile.ZIP_DEFLATED) as zipf: |
|
|
for root, dirs, files in os.walk(model_path): |
|
|
for file in files: |
|
|
file_path = os.path.join(root, file) |
|
|
arcname = os.path.relpath(file_path, model_path) |
|
|
zipf.write(file_path, arcname) |
|
|
|
|
|
file_size = os.path.getsize(zip_path) / (1024 * 1024) |
|
|
|
|
|
return zip_path, f"✅ Created {zip_filename} ({file_size:.2f} MB)" |
|
|
|
|
|
@handle_errors("model_management") |
|
|
def extract_model_zip(self, zip_file, progress=gr.Progress()) -> str: |
|
|
"""Extract uploaded model zip""" |
|
|
if zip_file is None: |
|
|
raise ArchitechError("No file uploaded!") |
|
|
|
|
|
progress(0.1, "📦 Extracting model archive...") |
|
|
|
|
|
|
|
|
zip_filename = Path(zip_file.name).name |
|
|
model_name = zip_filename.replace('.zip', '') |
|
|
extract_path = os.path.join("./uploaded_models", model_name) |
|
|
|
|
|
os.makedirs(extract_path, exist_ok=True) |
|
|
|
|
|
progress(0.3, "📂 Unpacking files...") |
|
|
|
|
|
|
|
|
with zipfile.ZipFile(zip_file.name, 'r') as zip_ref: |
|
|
zip_ref.extractall(extract_path) |
|
|
|
|
|
progress(0.7, "🔍 Validating model files...") |
|
|
|
|
|
|
|
|
files = os.listdir(extract_path) |
|
|
has_model = any('pytorch_model' in f or 'model.safetensors' in f for f in files) |
|
|
has_config = 'config.json' in files |
|
|
has_tokenizer = any('tokenizer' in f for f in files) |
|
|
|
|
|
validation_status = [] |
|
|
if has_model: |
|
|
validation_status.append("✅ Model weights found") |
|
|
else: |
|
|
validation_status.append("⚠️ Model weights not found") |
|
|
|
|
|
if has_config: |
|
|
validation_status.append("✅ Config file found") |
|
|
else: |
|
|
validation_status.append("⚠️ Config file not found") |
|
|
|
|
|
if has_tokenizer: |
|
|
validation_status.append("✅ Tokenizer found") |
|
|
else: |
|
|
validation_status.append("⚠️ Tokenizer not found") |
|
|
|
|
|
progress(1.0, "✅ Extraction complete!") |
|
|
|
|
|
return f"""🎉 **Model Uploaded Successfully!** |
|
|
|
|
|
**Extracted to:** `{extract_path}` |
|
|
|
|
|
**Validation:** |
|
|
{chr(10).join(validation_status)} |
|
|
|
|
|
**Files found:** {len(files)} files |
|
|
|
|
|
**You can now:** |
|
|
1. Use this model in the Test Model tab |
|
|
2. Continue training from this checkpoint |
|
|
3. Push to HuggingFace Hub |
|
|
|
|
|
*Model path: `{extract_path}`*""" |
|
|
|
|
|
def list_local_models(self) -> str: |
|
|
"""List all locally saved models""" |
|
|
trained_models = [] |
|
|
uploaded_models = [] |
|
|
|
|
|
|
|
|
if os.path.exists("./"): |
|
|
for item in os.listdir("./"): |
|
|
if item.startswith("trained_") and os.path.isdir(item): |
|
|
size = sum( |
|
|
os.path.getsize(os.path.join(dirpath, filename)) |
|
|
for dirpath, dirnames, filenames in os.walk(item) |
|
|
for filename in filenames |
|
|
) / (1024 * 1024) |
|
|
trained_models.append(f"- `{item}` ({size:.2f} MB)") |
|
|
|
|
|
|
|
|
if os.path.exists("./uploaded_models"): |
|
|
for item in os.listdir("./uploaded_models"): |
|
|
path = os.path.join("./uploaded_models", item) |
|
|
if os.path.isdir(path): |
|
|
size = sum( |
|
|
os.path.getsize(os.path.join(dirpath, filename)) |
|
|
for dirpath, dirnames, filenames in os.walk(path) |
|
|
for filename in filenames |
|
|
) / (1024 * 1024) |
|
|
uploaded_models.append(f"- `{item}` ({size:.2f} MB)") |
|
|
|
|
|
result = "## 📦 Local Models\n\n" |
|
|
|
|
|
if trained_models: |
|
|
result += "### Trained Models:\n" + "\n".join(trained_models) + "\n\n" |
|
|
else: |
|
|
result += "### Trained Models:\n*No trained models found*\n\n" |
|
|
|
|
|
if uploaded_models: |
|
|
result += "### Uploaded Models:\n" + "\n".join(uploaded_models) + "\n\n" |
|
|
else: |
|
|
result += "### Uploaded Models:\n*No uploaded models found*\n\n" |
|
|
|
|
|
return result |
|
|
|
|
|
@handle_errors("model_management") |
|
|
def delete_model(self, model_path: str) -> str: |
|
|
"""Delete a local model""" |
|
|
if not os.path.exists(model_path): |
|
|
raise ArchitechError(f"Model not found: {model_path}") |
|
|
|
|
|
shutil.rmtree(model_path) |
|
|
return f"✅ Deleted: {model_path}" |
|
|
|
|
|
model_manager = ModelManager() |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
def add_model_management_tab(): |
|
|
"""Add Model Management tab to Gradio interface""" |
|
|
with gr.Tab("💾 Model Management"): |
|
|
gr.Markdown(""" |
|
|
### Manage Your Models |
|
|
Upload, download, and organize your trained models |
|
|
""") |
|
|
|
|
|
with gr.Row(): |
|
|
|
|
|
with gr.Column(): |
|
|
gr.Markdown("### 📤 Upload Model") |
|
|
|
|
|
upload_file = gr.File( |
|
|
label="Upload Model ZIP", |
|
|
file_types=[".zip"], |
|
|
type="filepath" |
|
|
) |
|
|
|
|
|
upload_btn = gr.Button("📦 Extract and Save", variant="primary") |
|
|
upload_output = gr.Markdown() |
|
|
|
|
|
upload_btn.click( |
|
|
fn=model_manager.extract_model_zip, |
|
|
inputs=[upload_file], |
|
|
outputs=upload_output |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Column(): |
|
|
gr.Markdown("### 📥 Download Model") |
|
|
|
|
|
model_path_input = gr.Textbox( |
|
|
label="Model Path", |
|
|
placeholder="e.g., ./trained_my-model or ./uploaded_models/my-model", |
|
|
info="Path to the model directory you want to download" |
|
|
) |
|
|
|
|
|
model_name_input = gr.Textbox( |
|
|
label="Archive Name", |
|
|
placeholder="e.g., my-awesome-model", |
|
|
info="Name for the downloaded zip file" |
|
|
) |
|
|
|
|
|
download_btn = gr.Button("📦 Create ZIP", variant="primary") |
|
|
download_file = gr.File(label="Download") |
|
|
download_output = gr.Markdown() |
|
|
|
|
|
def create_and_return_zip(model_path, model_name): |
|
|
zip_path, message = model_manager.create_model_zip(model_path, model_name) |
|
|
return zip_path, message |
|
|
|
|
|
download_btn.click( |
|
|
fn=create_and_return_zip, |
|
|
inputs=[model_path_input, model_name_input], |
|
|
outputs=[download_file, download_output] |
|
|
) |
|
|
|
|
|
gr.Markdown("---") |
|
|
|
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
gr.Markdown("### 📋 Your Models") |
|
|
|
|
|
refresh_btn = gr.Button("🔄 Refresh List", variant="secondary") |
|
|
models_list = gr.Markdown() |
|
|
|
|
|
refresh_btn.click( |
|
|
fn=model_manager.list_local_models, |
|
|
inputs=[], |
|
|
outputs=models_list |
|
|
) |
|
|
|
|
|
|
|
|
models_list.value = model_manager.list_local_models() |
|
|
|
|
|
with gr.Column(): |
|
|
gr.Markdown("### 🗑️ Delete Model") |
|
|
|
|
|
delete_path = gr.Textbox( |
|
|
label="Model Path to Delete", |
|
|
placeholder="e.g., ./trained_my-model" |
|
|
) |
|
|
|
|
|
delete_btn = gr.Button("🗑️ Delete Model", variant="stop") |
|
|
delete_output = gr.Markdown() |
|
|
|
|
|
delete_btn.click( |
|
|
fn=model_manager.delete_model, |
|
|
inputs=[delete_path], |
|
|
outputs=delete_output |
|
|
) |
|
|
|
|
|
gr.Markdown(""" |
|
|
--- |
|
|
### 💡 Tips: |
|
|
- **Upload:** Upload model zips from other systems or backups |
|
|
- **Download:** Create portable archives of your trained models |
|
|
- **Organize:** Keep your workspace tidy by managing local models |
|
|
- **Backup:** Download important models before deleting them |
|
|
|
|
|
*Note: Uploaded/downloaded models persist only during your session unless you have persistent storage configured.* |
|
|
""") |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
class DatasetManager: |
|
|
def __init__(self): |
|
|
self.datasets_dir = Path("./synthetic_datasets") |
|
|
self.datasets_dir.mkdir(exist_ok=True) |
|
|
|
|
|
def list_available_datasets(self) -> List[Tuple[str, str]]: |
|
|
"""List all available synthetic datasets""" |
|
|
datasets = [] |
|
|
if self.datasets_dir.exists(): |
|
|
for file in self.datasets_dir.glob("*.json"): |
|
|
datasets.append((file.name, str(file))) |
|
|
return datasets |
|
|
|
|
|
def get_dataset_preview(self, dataset_path: str) -> str: |
|
|
"""Get preview of dataset contents""" |
|
|
try: |
|
|
with open(dataset_path, 'r') as f: |
|
|
data = json.load(f) |
|
|
|
|
|
if not data: |
|
|
return "Dataset is empty" |
|
|
|
|
|
preview = f"**Dataset:** `{Path(dataset_path).name}`\n\n" |
|
|
preview += f"**Total Examples:** {len(data)}\n\n" |
|
|
preview += "**First 3 Examples:**\n\n" |
|
|
|
|
|
for i, example in enumerate(data[:3], 1): |
|
|
preview += f"**Example {i}:**\n```\n{example.get('text', 'No text field')}\n```\n\n" |
|
|
|
|
|
return preview |
|
|
except Exception as e: |
|
|
return f"Error loading dataset: {str(e)}" |
|
|
|
|
|
def load_dataset_for_training(self, dataset_path: str) -> List[str]: |
|
|
"""Load dataset texts for training""" |
|
|
with open(dataset_path, 'r') as f: |
|
|
data = json.load(f) |
|
|
return [item["text"] for item in data if "text" in item] |
|
|
|
|
|
dataset_manager = DatasetManager() |
|
|
|
|
|
|
|
|
|
|
|
class RepositoryChat: |
|
|
def __init__(self): |
|
|
self.hf_api = HfApi() |
|
|
self.chat_history = [] |
|
|
self.current_user_token = None |
|
|
self.current_username = None |
|
|
|
|
|
def initialize_session(self, hf_token: str) -> Tuple[bool, str]: |
|
|
"""Initialize chat session with HF token""" |
|
|
is_valid, message, username = auth_manager.validate_hf_token(hf_token) |
|
|
if is_valid: |
|
|
self.current_user_token = hf_token |
|
|
self.current_username = username |
|
|
self.chat_history = [] |
|
|
return is_valid, message |
|
|
|
|
|
@handle_errors("repository_chat") |
|
|
def list_user_models(self) -> str: |
|
|
"""List all models in user's HuggingFace account""" |
|
|
if not self.current_user_token: |
|
|
raise ArchitechError("Please initialize session with your HuggingFace token first!") |
|
|
|
|
|
try: |
|
|
models = self.hf_api.list_models(author=self.current_username, token=self.current_user_token) |
|
|
model_list = list(models) |
|
|
|
|
|
if not model_list: |
|
|
return f"📭 No models found in {self.current_username}'s account" |
|
|
|
|
|
result = f"## 🤖 Your Models ({len(model_list)})\n\n" |
|
|
|
|
|
for model in model_list[:20]: |
|
|
model_id = model.modelId |
|
|
downloads = getattr(model, 'downloads', 0) |
|
|
likes = getattr(model, 'likes', 0) |
|
|
result += f"- **{model_id}**\n" |
|
|
result += f" - Downloads: {downloads} | Likes: {likes}\n" |
|
|
result += f" - [View on Hub](https://huggingface.co/{model_id})\n\n" |
|
|
|
|
|
return result |
|
|
except Exception as e: |
|
|
return f"Error fetching models: {str(e)}" |
|
|
|
|
|
@handle_errors("repository_chat") |
|
|
def list_user_datasets(self) -> str: |
|
|
"""List all datasets in user's HuggingFace account""" |
|
|
if not self.current_user_token: |
|
|
raise ArchitechError("Please initialize session first!") |
|
|
|
|
|
try: |
|
|
datasets = self.hf_api.list_datasets(author=self.current_username, token=self.current_user_token) |
|
|
dataset_list = list(datasets) |
|
|
|
|
|
if not dataset_list: |
|
|
return f"📭 No datasets found in {self.current_username}'s account" |
|
|
|
|
|
result = f"## 📊 Your Datasets ({len(dataset_list)})\n\n" |
|
|
|
|
|
for dataset in dataset_list[:20]: |
|
|
dataset_id = dataset.id |
|
|
downloads = getattr(dataset, 'downloads', 0) |
|
|
result += f"- **{dataset_id}**\n" |
|
|
result += f" - Downloads: {downloads}\n" |
|
|
result += f" - [View on Hub](https://huggingface.co/datasets/{dataset_id})\n\n" |
|
|
|
|
|
return result |
|
|
except Exception as e: |
|
|
return f"Error fetching datasets: {str(e)}" |
|
|
|
|
|
@handle_errors("repository_chat") |
|
|
def get_model_info(self, model_id: str) -> str: |
|
|
"""Get detailed information about a specific model""" |
|
|
if not self.current_user_token: |
|
|
raise ArchitechError("Please initialize session first!") |
|
|
|
|
|
try: |
|
|
|
|
|
if "/" not in model_id and self.current_username: |
|
|
model_id = f"{self.current_username}/{model_id}" |
|
|
|
|
|
model_info = self.hf_api.model_info(model_id, token=self.current_user_token) |
|
|
|
|
|
result = f"## 🤖 Model: {model_id}\n\n" |
|
|
result += f"**Model ID:** {model_info.modelId}\n" |
|
|
result += f"**Downloads:** {getattr(model_info, 'downloads', 0)}\n" |
|
|
result += f"**Likes:** {getattr(model_info, 'likes', 0)}\n" |
|
|
result += f"**Created:** {getattr(model_info, 'created_at', 'Unknown')}\n" |
|
|
result += f"**Last Modified:** {getattr(model_info, 'last_modified', 'Unknown')}\n\n" |
|
|
|
|
|
if hasattr(model_info, 'tags') and model_info.tags: |
|
|
result += f"**Tags:** {', '.join(model_info.tags[:10])}\n\n" |
|
|
|
|
|
result += f"**🔗 [View on HuggingFace](https://huggingface.co/{model_id})**\n" |
|
|
|
|
|
return result |
|
|
except Exception as e: |
|
|
return f"Error fetching model info: {str(e)}" |
|
|
|
|
|
@handle_errors("repository_chat") |
|
|
def delete_repo(self, repo_id: str, repo_type: str = "model") -> str: |
|
|
"""Delete a repository (model or dataset)""" |
|
|
if not self.current_user_token: |
|
|
raise ArchitechError("Please initialize session first!") |
|
|
|
|
|
|
|
|
if "/" not in repo_id and self.current_username: |
|
|
repo_id = f"{self.current_username}/{repo_id}" |
|
|
|
|
|
try: |
|
|
self.hf_api.delete_repo( |
|
|
repo_id=repo_id, |
|
|
token=self.current_user_token, |
|
|
repo_type=repo_type |
|
|
) |
|
|
return f"✅ Successfully deleted {repo_type}: {repo_id}" |
|
|
except Exception as e: |
|
|
return f"❌ Error deleting {repo_type}: {str(e)}" |
|
|
|
|
|
@handle_errors("repository_chat") |
|
|
def chat_with_repos(self, user_message: str) -> str: |
|
|
"""Conversational interface for repository management""" |
|
|
if not self.current_user_token: |
|
|
return "⚠️ Please initialize your session with a HuggingFace token first!" |
|
|
|
|
|
|
|
|
self.chat_history.append({"role": "user", "content": user_message}) |
|
|
|
|
|
|
|
|
message_lower = user_message.lower() |
|
|
|
|
|
response = "" |
|
|
|
|
|
|
|
|
if any(word in message_lower for word in ["list models", "show models", "my models", "what models"]): |
|
|
response = self.list_user_models() |
|
|
|
|
|
|
|
|
elif any(word in message_lower for word in ["list datasets", "show datasets", "my datasets", "what datasets"]): |
|
|
response = self.list_user_datasets() |
|
|
|
|
|
|
|
|
elif any(word in message_lower for word in ["info about", "details about", "tell me about", "information on"]): |
|
|
|
|
|
words = user_message.split() |
|
|
if len(words) > 2: |
|
|
potential_model = words[-1].strip("?.,!") |
|
|
response = self.get_model_info(potential_model) |
|
|
else: |
|
|
response = "Please specify which model you want info about. Example: 'info about my-model-name'" |
|
|
|
|
|
|
|
|
elif "delete" in message_lower and "model" in message_lower: |
|
|
words = user_message.split() |
|
|
if len(words) > 2: |
|
|
model_name = words[-1].strip("?.,!") |
|
|
response = f"⚠️ Are you sure you want to delete model '{model_name}'? This action cannot be undone!\n\n" |
|
|
response += "To confirm, use the Delete Repository section below." |
|
|
else: |
|
|
response = "Please specify which model to delete. Example: 'delete model my-model-name'" |
|
|
|
|
|
|
|
|
elif any(word in message_lower for word in ["help", "what can you do", "commands"]): |
|
|
response = """## 🤖 Architech Repository Assistant |
|
|
|
|
|
I can help you manage your HuggingFace repositories! Here's what I can do: |
|
|
|
|
|
**📋 Listing:** |
|
|
- "List my models" - Show all your models |
|
|
- "Show my datasets" - Show all your datasets |
|
|
|
|
|
**ℹ️ Information:** |
|
|
- "Info about [model-name]" - Get details about a specific model |
|
|
- "Tell me about [model-name]" - Model statistics and info |
|
|
|
|
|
**🗑️ Management:** |
|
|
- Use the Delete Repository section to remove models/datasets |
|
|
|
|
|
**💡 Tips:** |
|
|
- I have access to your HuggingFace account |
|
|
- I can see all your public and private repos |
|
|
- All actions respect your permissions |
|
|
|
|
|
Try asking: "List my models" or "Show my datasets"!""" |
|
|
|
|
|
|
|
|
else: |
|
|
response = f"""I'm not sure what you want to do. |
|
|
|
|
|
**Quick Commands:** |
|
|
- "List my models" |
|
|
- "Show my datasets" |
|
|
- "Info about [model-name]" |
|
|
- "Help" for full command list |
|
|
|
|
|
What would you like to do?""" |
|
|
|
|
|
|
|
|
self.chat_history.append({"role": "assistant", "content": response}) |
|
|
|
|
|
return response |
|
|
|
|
|
def get_chat_history_display(self) -> List[Tuple[str, str]]: |
|
|
"""Format chat history for Gradio ChatBot""" |
|
|
history = [] |
|
|
for i in range(0, len(self.chat_history), 2): |
|
|
if i + 1 < len(self.chat_history): |
|
|
user_msg = self.chat_history[i]["content"] |
|
|
bot_msg = self.chat_history[i + 1]["content"] |
|
|
history.append((user_msg, bot_msg)) |
|
|
return history |
|
|
|
|
|
repo_chat = RepositoryChat() |
|
|
|
|
|
class DocumentationGenerator: |
|
|
def __init__(self): |
|
|
self.templates_dir = Path("./generated_docs") |
|
|
self.templates_dir.mkdir(exist_ok=True) |
|
|
|
|
|
def generate_model_card( |
|
|
self, |
|
|
model_name: str, |
|
|
task_description: str, |
|
|
base_model: str, |
|
|
dataset_size: int, |
|
|
training_params: Dict[str, Any], |
|
|
domain: str = "general", |
|
|
intended_use: str = "", |
|
|
limitations: str = "", |
|
|
ethical_considerations: str = "" |
|
|
) -> str: |
|
|
"""Generate a comprehensive model card following HuggingFace standards""" |
|
|
|
|
|
timestamp = datetime.now().strftime("%Y-%m-%d") |
|
|
|
|
|
model_card = f"""--- |
|
|
language: en |
|
|
license: mit |
|
|
tags: |
|
|
- text-generation |
|
|
- custom-model |
|
|
- architech |
|
|
- {domain} |
|
|
datasets: |
|
|
- synthetic-data |
|
|
metrics: |
|
|
- perplexity |
|
|
model-index: |
|
|
- name: {model_name} |
|
|
results: [] |
|
|
--- |
|
|
|
|
|
# {model_name} |
|
|
|
|
|
## Model Description |
|
|
|
|
|
**{model_name}** is a fine-tuned language model created using Architech AI Model Architect. |
|
|
|
|
|
### Model Details |
|
|
|
|
|
- **Developed by:** Architech User |
|
|
- **Model type:** Causal Language Model |
|
|
- **Language(s):** English |
|
|
- **Base Model:** {base_model} |
|
|
- **License:** MIT |
|
|
- **Finetuned from:** {base_model} |
|
|
|
|
|
### Model Purpose |
|
|
|
|
|
{task_description} |
|
|
|
|
|
## Training Details |
|
|
|
|
|
### Training Data |
|
|
|
|
|
This model was trained on a synthetic dataset specifically generated for this task: |
|
|
|
|
|
- **Dataset Size:** {dataset_size} examples |
|
|
- **Domain:** {domain.title()} |
|
|
- **Data Generation:** Architech Synthetic Data Generator |
|
|
- **Data Format:** Conversational pairs / Instruction-response format |
|
|
|
|
|
The training data was synthetically generated to ensure: |
|
|
- Domain-specific vocabulary and concepts |
|
|
- Natural language variations |
|
|
- Task-relevant examples |
|
|
- Ethical and unbiased content |
|
|
|
|
|
### Training Procedure |
|
|
|
|
|
**Training Hyperparameters:** |
|
|
|
|
|
- **Base Model:** {base_model} |
|
|
- **Training Examples:** {dataset_size} |
|
|
- **Epochs:** {training_params.get('epochs', 'N/A')} |
|
|
- **Learning Rate:** {training_params.get('learning_rate', 'N/A')} |
|
|
- **Batch Size:** {training_params.get('batch_size', 'N/A')} |
|
|
- **Gradient Accumulation Steps:** {training_params.get('gradient_accumulation', 4)} |
|
|
- **Optimizer:** AdamW |
|
|
- **Training Precision:** FP16 (if GPU available) |
|
|
|
|
|
**Training Infrastructure:** |
|
|
|
|
|
- **Framework:** HuggingFace Transformers |
|
|
- **Training Tool:** Architech AI Model Architect |
|
|
- **Hardware:** {training_params.get('hardware', 'GPU/CPU auto-detected')} |
|
|
|
|
|
## Intended Use |
|
|
|
|
|
### Direct Use |
|
|
|
|
|
{intended_use if intended_use else f'''This model is designed for {task_description.lower()}. It can be used directly for: |
|
|
|
|
|
- Text generation in the {domain} domain |
|
|
- Conversational AI applications |
|
|
- Task-specific completion and assistance |
|
|
- Research and experimentation'''} |
|
|
|
|
|
### Downstream Use |
|
|
|
|
|
This model can be further fine-tuned for: |
|
|
- More specialized tasks within the {domain} domain |
|
|
- Multi-turn conversations |
|
|
- Domain-specific applications |
|
|
|
|
|
### Out-of-Scope Use |
|
|
|
|
|
This model should NOT be used for: |
|
|
- Medical, legal, or financial advice without human oversight |
|
|
- Safety-critical applications |
|
|
- Decision-making without human review |
|
|
- Generating harmful, biased, or unethical content |
|
|
|
|
|
## Bias, Risks, and Limitations |
|
|
|
|
|
{limitations if limitations else f'''### Known Limitations |
|
|
|
|
|
- Trained on synthetic data, which may not capture all real-world nuances |
|
|
- Limited to {dataset_size} training examples |
|
|
- May produce inconsistent outputs on topics outside training domain |
|
|
- Should not be considered a source of factual information without verification |
|
|
|
|
|
### Recommendations |
|
|
|
|
|
Users should: |
|
|
- Validate outputs for accuracy and appropriateness |
|
|
- Not rely solely on this model for critical decisions |
|
|
- Be aware of potential biases in generated content |
|
|
- Use human oversight for production applications'''} |
|
|
|
|
|
## Ethical Considerations |
|
|
|
|
|
{ethical_considerations if ethical_considerations else '''This model was developed with ethical AI principles in mind: |
|
|
|
|
|
- Training data was synthetically generated to avoid privacy issues |
|
|
- No personally identifiable information was used in training |
|
|
- Content generation should be monitored for potential misuse |
|
|
- Users are responsible for ensuring ethical use of generated content'''} |
|
|
|
|
|
## How to Use |
|
|
|
|
|
### Loading the Model |
|
|
|
|
|
```python |
|
|
from transformers import AutoTokenizer, AutoModelForCausalLM |
|
|
|
|
|
tokenizer = AutoTokenizer.from_pretrained("{model_name}") |
|
|
model = AutoModelForCausalLM.from_pretrained("{model_name}") |
|
|
|
|
|
# Generate text |
|
|
inputs = tokenizer("Your prompt here", return_tensors="pt") |
|
|
outputs = model.generate(**inputs, max_length=100) |
|
|
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True) |
|
|
print(generated_text) |
|
|
``` |
|
|
|
|
|
### Using with Pipeline |
|
|
|
|
|
```python |
|
|
from transformers import pipeline |
|
|
|
|
|
generator = pipeline('text-generation', model='{model_name}') |
|
|
result = generator("Your prompt here", max_length=100) |
|
|
print(result[0]['generated_text']) |
|
|
``` |
|
|
|
|
|
## Model Performance |
|
|
|
|
|
Performance metrics will vary based on specific use case and evaluation criteria. |
|
|
|
|
|
### Training Loss |
|
|
|
|
|
Training completed successfully with the model converging appropriately for the given dataset size and complexity. |
|
|
|
|
|
## Environmental Impact |
|
|
|
|
|
- **Training Time:** Approximately {training_params.get('training_time', 'varies')} minutes |
|
|
- **Hardware:** {training_params.get('hardware', 'GPU/CPU')} |
|
|
- **Carbon Emissions:** Minimal due to efficient training approach |
|
|
|
|
|
## Technical Specifications |
|
|
|
|
|
### Model Architecture |
|
|
|
|
|
Based on {base_model} architecture with task-specific fine-tuning. |
|
|
|
|
|
### Compute Infrastructure |
|
|
|
|
|
- **Training Platform:** HuggingFace Spaces / Architech |
|
|
- **Framework:** PyTorch + Transformers |
|
|
- **Optimization:** Gradient accumulation for memory efficiency |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you use this model, please cite: |
|
|
|
|
|
```bibtex |
|
|
@misc{{{model_name.replace('-', '_')}, |
|
|
author = {{Architech User}}, |
|
|
title = {{{model_name}}}, |
|
|
year = {{{datetime.now().year}}}, |
|
|
publisher = {{HuggingFace}}, |
|
|
howpublished = {{\\url{{https://huggingface.co/your-username/{model_name}}}}} |
|
|
}} |
|
|
``` |
|
|
|
|
|
## Model Card Authors |
|
|
|
|
|
- Generated by: Architech AI Model Architect |
|
|
- Date: {timestamp} |
|
|
|
|
|
## Model Card Contact |
|
|
|
|
|
For questions or feedback about this model, please open an issue in the model repository. |
|
|
|
|
|
--- |
|
|
|
|
|
*This model card was automatically generated by Architech AI Model Architect. Please review and customize as needed.* |
|
|
""" |
|
|
|
|
|
|
|
|
card_path = self.templates_dir / f"{model_name}_model_card.md" |
|
|
with open(card_path, 'w') as f: |
|
|
f.write(model_card) |
|
|
|
|
|
return model_card, str(card_path) |
|
|
|
|
|
def generate_research_paper( |
|
|
self, |
|
|
model_name: str, |
|
|
task_description: str, |
|
|
base_model: str, |
|
|
dataset_size: int, |
|
|
training_params: Dict[str, Any], |
|
|
domain: str = "general", |
|
|
methodology_notes: str = "", |
|
|
results_summary: str = "" |
|
|
) -> str: |
|
|
"""Generate a research paper documenting the model""" |
|
|
|
|
|
timestamp = datetime.now().strftime("%B %Y") |
|
|
|
|
|
paper = f"""# Fine-Tuning {base_model} for {task_description}: A Synthetic Data Approach |
|
|
|
|
|
**Authors:** Architech User |
|
|
**Date:** {timestamp} |
|
|
**Model:** {model_name} |
|
|
|
|
|
--- |
|
|
|
|
|
## Abstract |
|
|
|
|
|
We present **{model_name}**, a fine-tuned language model specifically designed for {task_description.lower()}. |
|
|
This work demonstrates the effectiveness of synthetic data generation for domain-specific language model adaptation. |
|
|
Using {dataset_size} synthetically generated examples, we fine-tuned {base_model} to create a specialized model |
|
|
for the {domain} domain. Our approach leverages automated data generation techniques to overcome the common challenge |
|
|
of limited training data availability while maintaining high-quality, task-relevant outputs. |
|
|
|
|
|
**Keywords:** Language Models, Transfer Learning, Synthetic Data, Fine-Tuning, {domain.title()}, {base_model} |
|
|
|
|
|
--- |
|
|
|
|
|
## 1. Introduction |
|
|
|
|
|
### 1.1 Background |
|
|
|
|
|
Large language models (LLMs) have demonstrated remarkable capabilities across diverse natural language processing tasks. |
|
|
However, adapting these models to specific domains or tasks often requires substantial amounts of high-quality training data, |
|
|
which can be expensive, time-consuming, or difficult to obtain while maintaining privacy and ethical standards. |
|
|
|
|
|
### 1.2 Motivation |
|
|
|
|
|
The primary motivation for this work is to address the data scarcity problem in domain-specific language model development. |
|
|
Our specific use case—{task_description.lower()}—requires specialized knowledge and conversational patterns that may not |
|
|
be adequately represented in general-purpose language models. |
|
|
|
|
|
### 1.3 Contributions |
|
|
|
|
|
This work makes the following contributions: |
|
|
|
|
|
1. **Synthetic Data Generation Framework**: We develop and apply a domain-specific synthetic data generation approach |
|
|
that creates high-quality training examples without requiring manual annotation. |
|
|
|
|
|
2. **Efficient Fine-Tuning**: We demonstrate effective fine-tuning of {base_model} using a relatively small dataset |
|
|
of {dataset_size} examples, showcasing the efficiency of modern transfer learning approaches. |
|
|
|
|
|
3. **Practical Application**: We provide a complete, production-ready model for {task_description.lower()} that can |
|
|
be deployed immediately or serve as a foundation for further specialization. |
|
|
|
|
|
--- |
|
|
|
|
|
## 2. Related Work |
|
|
|
|
|
### 2.1 Transfer Learning in NLP |
|
|
|
|
|
Transfer learning has become the dominant paradigm in natural language processing, with pre-trained models like GPT, |
|
|
BERT, and their variants achieving state-of-the-art results across numerous benchmarks. Our work builds on this |
|
|
foundation by demonstrating efficient domain adaptation. |
|
|
|
|
|
### 2.2 Synthetic Data Generation |
|
|
|
|
|
Recent work has shown that synthetic data can effectively augment or even replace human-annotated data for specific tasks. |
|
|
Our approach extends these findings to conversational AI and domain-specific language generation. |
|
|
|
|
|
### 2.3 Domain Adaptation |
|
|
|
|
|
Domain adaptation techniques allow models trained on one domain to perform well on another. Our work contributes to |
|
|
this area by combining synthetic data generation with fine-tuning for efficient domain-specific model creation. |
|
|
|
|
|
--- |
|
|
|
|
|
## 3. Methodology |
|
|
|
|
|
### 3.1 Base Model Selection |
|
|
|
|
|
We selected **{base_model}** as our base model for the following reasons: |
|
|
|
|
|
- **Architecture**: Modern transformer-based architecture with proven generation capabilities |
|
|
- **Size**: Appropriate balance between capability and computational efficiency |
|
|
- **Compatibility**: Well-supported by the HuggingFace ecosystem |
|
|
- **Performance**: Strong baseline performance on general language tasks |
|
|
|
|
|
### 3.2 Synthetic Data Generation |
|
|
|
|
|
{methodology_notes if methodology_notes else f'''Our synthetic data generation process consists of several key components: |
|
|
|
|
|
**Domain Knowledge Base:** |
|
|
We curated domain-specific vocabulary, concepts, and contexts relevant to the {domain} domain. This knowledge base |
|
|
includes: |
|
|
- Key topics and terminology |
|
|
- Common question-answer patterns |
|
|
- Domain-specific use cases |
|
|
- Contextual scenarios |
|
|
|
|
|
**Template-Based Generation:** |
|
|
We employed template-based generation with intelligent variable substitution: |
|
|
- Multiple conversation templates |
|
|
- Dynamic topic and concept insertion |
|
|
- Natural language variation |
|
|
- Context-appropriate responses |
|
|
|
|
|
**Quality Assurance:** |
|
|
Each generated example undergoes validation: |
|
|
- Coherence checking |
|
|
- Domain relevance verification |
|
|
- Diversity analysis |
|
|
- Edge case inclusion'''} |
|
|
|
|
|
### 3.3 Training Configuration |
|
|
|
|
|
Our training setup utilized the following hyperparameters: |
|
|
|
|
|
| Parameter | Value | |
|
|
|-----------|-------| |
|
|
| Base Model | {base_model} | |
|
|
| Training Examples | {dataset_size} | |
|
|
| Epochs | {training_params.get('epochs', 'N/A')} | |
|
|
| Learning Rate | {training_params.get('learning_rate', 'N/A')} | |
|
|
| Batch Size | {training_params.get('batch_size', 'N/A')} | |
|
|
| Gradient Accumulation | {training_params.get('gradient_accumulation', 4)} steps | |
|
|
| Optimizer | AdamW | |
|
|
| Precision | Mixed (FP16) | |
|
|
|
|
|
**Training Procedure:** |
|
|
|
|
|
1. **Data Preparation**: Synthetic examples were tokenized using the base model's tokenizer |
|
|
2. **Model Initialization**: Started from pre-trained {base_model} weights |
|
|
3. **Fine-Tuning**: Applied supervised fine-tuning with causal language modeling objective |
|
|
4. **Optimization**: Used gradient accumulation for memory efficiency |
|
|
5. **Validation**: Monitored training loss for convergence |
|
|
|
|
|
### 3.4 Implementation Details |
|
|
|
|
|
Our implementation leverages: |
|
|
- **Framework**: HuggingFace Transformers |
|
|
- **Training Tool**: Architech AI Model Architect |
|
|
- **Infrastructure**: Cloud-based GPU/CPU resources |
|
|
- **Optimization**: Automatic mixed precision training |
|
|
|
|
|
--- |
|
|
|
|
|
## 4. Results |
|
|
|
|
|
### 4.1 Training Outcomes |
|
|
|
|
|
{results_summary if results_summary else f'''The model successfully converged during training, demonstrating: |
|
|
|
|
|
- **Stable Training**: Loss decreased consistently across epochs |
|
|
- **No Overfitting**: Training remained stable without signs of overfitting to the small dataset |
|
|
- **Efficient Learning**: Model adapted to domain-specific patterns effectively |
|
|
|
|
|
**Qualitative Observations:** |
|
|
- Generated text shows strong alignment with the {domain} domain |
|
|
- Model produces coherent, contextually appropriate responses |
|
|
- Task-specific vocabulary and concepts are properly utilized |
|
|
- Conversation flow is natural and relevant to intended use case'''} |
|
|
|
|
|
### 4.2 Model Capabilities |
|
|
|
|
|
The fine-tuned model demonstrates: |
|
|
|
|
|
1. **Domain Expertise**: Strong understanding of {domain}-specific concepts |
|
|
2. **Task Alignment**: Outputs are well-aligned with {task_description.lower()} |
|
|
3. **Coherence**: Generated text maintains logical consistency |
|
|
4. **Flexibility**: Adapts to various prompts within the domain |
|
|
|
|
|
### 4.3 Limitations |
|
|
|
|
|
We acknowledge the following limitations: |
|
|
|
|
|
- **Dataset Size**: With {dataset_size} examples, coverage of edge cases may be limited |
|
|
- **Synthetic Origin**: Training data may not capture all real-world nuances |
|
|
- **Domain Specificity**: Performance may degrade on out-of-domain inputs |
|
|
- **Evaluation**: Comprehensive quantitative evaluation remains future work |
|
|
|
|
|
--- |
|
|
|
|
|
## 5. Discussion |
|
|
|
|
|
### 5.1 Effectiveness of Synthetic Data |
|
|
|
|
|
Our results demonstrate that synthetically generated data can effectively fine-tune language models for specific tasks. |
|
|
The quality of outputs suggests that carefully designed synthetic data can capture essential patterns needed for |
|
|
domain adaptation. |
|
|
|
|
|
### 5.2 Practical Implications |
|
|
|
|
|
This work has several practical implications: |
|
|
|
|
|
- **Accessibility**: Reduces barriers to creating custom language models |
|
|
- **Privacy**: Eliminates need for potentially sensitive real-world data |
|
|
- **Efficiency**: Enables rapid prototyping and iteration |
|
|
- **Scalability**: Framework can be applied to diverse domains and tasks |
|
|
|
|
|
### 5.3 Future Directions |
|
|
|
|
|
Several promising directions for future work include: |
|
|
|
|
|
1. **Quantitative Evaluation**: Comprehensive benchmarking against domain-specific metrics |
|
|
2. **Dataset Scaling**: Investigation of performance vs. dataset size trade-offs |
|
|
3. **Hybrid Approaches**: Combining synthetic and real data for enhanced performance |
|
|
4. **Multi-Domain Transfer**: Exploring transfer learning across related domains |
|
|
|
|
|
--- |
|
|
|
|
|
## 6. Conclusion |
|
|
|
|
|
We presented **{model_name}**, a fine-tuned language model for {task_description.lower()}, demonstrating the |
|
|
effectiveness of synthetic data generation for domain-specific model adaptation. Our approach successfully created |
|
|
a specialized model using {dataset_size} synthetically generated examples, proving that efficient domain adaptation |
|
|
is achievable without large-scale manual data collection. |
|
|
|
|
|
The model shows strong task alignment and domain expertise, validating our methodology. This work contributes to |
|
|
the growing body of evidence that synthetic data, when carefully designed, can serve as an effective alternative |
|
|
or complement to human-annotated data for language model fine-tuning. |
|
|
|
|
|
As language models continue to evolve, techniques for efficient, ethical, and accessible model adaptation will |
|
|
become increasingly important. Our work provides a practical framework for creating custom language models that |
|
|
can be applied across diverse domains and use cases. |
|
|
|
|
|
--- |
|
|
|
|
|
## 7. References |
|
|
|
|
|
1. HuggingFace Transformers: State-of-the-art Natural Language Processing |
|
|
2. Attention Is All You Need (Vaswani et al., 2017) |
|
|
3. Language Models are Few-Shot Learners (Brown et al., 2020) |
|
|
4. Transfer Learning in Natural Language Processing (Ruder, 2019) |
|
|
|
|
|
--- |
|
|
|
|
|
## Appendix A: Model Architecture |
|
|
|
|
|
**Base Architecture:** {base_model} |
|
|
|
|
|
The model inherits the transformer-based architecture of the base model, with all parameters fine-tuned for the |
|
|
specific task. |
|
|
|
|
|
## Appendix B: Training Logs |
|
|
|
|
|
Training completed successfully with stable convergence. Detailed logs available in model repository. |
|
|
|
|
|
## Appendix C: Code Availability |
|
|
|
|
|
Model and code are available at: https://huggingface.co/your-username/{model_name} |
|
|
|
|
|
--- |
|
|
|
|
|
## Acknowledgments |
|
|
|
|
|
This research was conducted using Architech AI Model Architect, an open-source tool for automated language model |
|
|
development. We thank the HuggingFace team for providing the infrastructure and tools that made this work possible. |
|
|
|
|
|
--- |
|
|
|
|
|
**Contact:** For questions about this work, please open an issue in the model repository. |
|
|
|
|
|
**Date:** {timestamp} |
|
|
**Version:** 1.0 |
|
|
|
|
|
--- |
|
|
|
|
|
*This paper was automatically generated by Architech AI Model Architect. Please review and customize as needed for publication.* |
|
|
""" |
|
|
|
|
|
|
|
|
paper_path = self.templates_dir / f"{model_name}_research_paper.md" |
|
|
with open(paper_path, 'w') as f: |
|
|
f.write(paper) |
|
|
|
|
|
return paper, str(paper_path) |
|
|
|
|
|
def generate_both_documents( |
|
|
self, |
|
|
model_name: str, |
|
|
task_description: str, |
|
|
base_model: str, |
|
|
dataset_size: int, |
|
|
num_epochs: int, |
|
|
learning_rate: float, |
|
|
batch_size: int, |
|
|
domain: str = "general", |
|
|
intended_use: str = "", |
|
|
limitations: str = "", |
|
|
methodology_notes: str = "", |
|
|
results_summary: str = "", |
|
|
progress=gr.Progress() |
|
|
) -> Tuple[str, str, str, str]: |
|
|
"""Generate both model card and research paper""" |
|
|
|
|
|
progress(0.3, "📝 Generating Model Card...") |
|
|
|
|
|
training_params = { |
|
|
'epochs': num_epochs, |
|
|
'learning_rate': learning_rate, |
|
|
'batch_size': batch_size, |
|
|
'gradient_accumulation': 4, |
|
|
'hardware': 'GPU/CPU (auto-detected)' |
|
|
} |
|
|
|
|
|
model_card, card_path = self.generate_model_card( |
|
|
model_name, task_description, base_model, dataset_size, |
|
|
training_params, domain, intended_use, limitations |
|
|
) |
|
|
|
|
|
progress(0.7, "📄 Generating Research Paper...") |
|
|
|
|
|
paper, paper_path = self.generate_research_paper( |
|
|
model_name, task_description, base_model, dataset_size, |
|
|
training_params, domain, methodology_notes, results_summary |
|
|
) |
|
|
|
|
|
progress(1.0, "✅ Documentation Generated!") |
|
|
|
|
|
return model_card, card_path, paper, paper_path |
|
|
|
|
|
doc_generator = DocumentationGenerator() |
|
|
|
|
|
def create_gradio_interface(): |
|
|
agent = ArchitechAgent() |
|
|
|
|
|
with gr.Blocks(title="🏗️ Architech", theme=gr.themes.Soft()) as demo: |
|
|
gr.Markdown(""" |
|
|
# 🏗️ **Architech - Your AI Model Architect** |
|
|
|
|
|
*Describe what you want, and I'll build it for you!* |
|
|
""") |
|
|
|
|
|
with gr.Tabs(): |
|
|
|
|
|
with gr.Tab("📊 Generate Dataset"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
task_desc = gr.Textbox(label="Task Description", lines=3, |
|
|
placeholder="E.g., 'Customer support chatbot for tech products'") |
|
|
domain = gr.Dropdown( |
|
|
choices=["technology", "healthcare", "finance", "general"], |
|
|
label="Domain", value="general") |
|
|
dataset_size = gr.Slider(50, 500, 100, step=50, label="Dataset Size") |
|
|
format_type = gr.Dropdown( |
|
|
choices=["conversational", "instruction"], |
|
|
label="Format", value="conversational") |
|
|
gen_btn = gr.Button("🎨 Generate Dataset", variant="primary") |
|
|
with gr.Column(): |
|
|
gen_output = gr.Markdown() |
|
|
|
|
|
gen_btn.click( |
|
|
fn=agent.generate_synthetic_dataset_wrapper, |
|
|
inputs=[task_desc, domain, dataset_size, format_type, gr.State("medium")], |
|
|
outputs=gen_output |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("🚀 Train Model"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
task_desc_train = gr.Textbox(label="Task Description", lines=2) |
|
|
model_name = gr.Textbox(label="Model Name", placeholder="my-awesome-model") |
|
|
hf_token = gr.Textbox(label="HuggingFace Token", type="password") |
|
|
use_synthetic = gr.Checkbox(label="Generate New Synthetic Data", value=True) |
|
|
|
|
|
with gr.Group(visible=False) as dataset_group: |
|
|
gr.Markdown("### 📊 Select Existing Dataset") |
|
|
dataset_dropdown = gr.Dropdown( |
|
|
label="Choose Dataset", |
|
|
choices=[], |
|
|
interactive=True |
|
|
) |
|
|
refresh_datasets_btn = gr.Button("🔄 Refresh Datasets", size="sm") |
|
|
dataset_preview = gr.Markdown() |
|
|
|
|
|
def refresh_dataset_list(): |
|
|
datasets = dataset_manager.list_available_datasets() |
|
|
choices = [name for name, path in datasets] |
|
|
return gr.Dropdown(choices=choices) |
|
|
|
|
|
def show_dataset_preview(dataset_name): |
|
|
if dataset_name: |
|
|
datasets = dataset_manager.list_available_datasets() |
|
|
for name, path in datasets: |
|
|
if name == dataset_name: |
|
|
return dataset_manager.get_dataset_preview(path) |
|
|
return "Select a dataset to preview" |
|
|
|
|
|
refresh_datasets_btn.click( |
|
|
fn=refresh_dataset_list, |
|
|
outputs=dataset_dropdown |
|
|
) |
|
|
|
|
|
dataset_dropdown.change( |
|
|
fn=show_dataset_preview, |
|
|
inputs=dataset_dropdown, |
|
|
outputs=dataset_preview |
|
|
) |
|
|
|
|
|
with gr.Group(visible=False) as custom_data_group: |
|
|
training_data_input = gr.Textbox( |
|
|
label="Training Data (one example per line) OR Dataset Path", |
|
|
placeholder="Human: Hello\nAssistant: Hi!\n\nOR: ./synthetic_datasets/synthetic_general_conversational_20260126.json", |
|
|
lines=8 |
|
|
) |
|
|
|
|
|
|
|
|
def toggle_data_source(use_synth): |
|
|
return gr.update(visible=not use_synth), gr.update(visible=not use_synth) |
|
|
|
|
|
use_synthetic.change( |
|
|
fn=toggle_data_source, |
|
|
inputs=use_synthetic, |
|
|
outputs=[dataset_group, custom_data_group] |
|
|
) |
|
|
|
|
|
with gr.Accordion("⚙️ Advanced", open=False): |
|
|
base_model = gr.Dropdown( |
|
|
choices=["distilgpt2", "gpt2", "microsoft/DialoGPT-small"], |
|
|
label="Base Model", value="distilgpt2") |
|
|
learning_rate = gr.Slider(1e-5, 5e-4, 2e-4, label="Learning Rate") |
|
|
num_epochs = gr.Slider(1, 5, 3, step=1, label="Epochs") |
|
|
batch_size = gr.Slider(1, 4, 2, step=1, label="Batch Size") |
|
|
|
|
|
train_btn = gr.Button("🎯 Train Model", variant="primary") |
|
|
|
|
|
with gr.Column(): |
|
|
train_output = gr.Markdown() |
|
|
|
|
|
def prepare_training_data(use_synth, dataset_name, custom_data): |
|
|
"""Prepare training data based on selection""" |
|
|
if use_synth: |
|
|
return "" |
|
|
elif dataset_name: |
|
|
|
|
|
datasets = dataset_manager.list_available_datasets() |
|
|
for name, path in datasets: |
|
|
if name == dataset_name: |
|
|
return path |
|
|
return custom_data |
|
|
|
|
|
train_btn.click( |
|
|
fn=lambda task, dataset_name, custom, model, token, base, synth, lr, epochs, batch: agent.train_custom_model( |
|
|
task, |
|
|
prepare_training_data(synth, dataset_name, custom), |
|
|
model, |
|
|
token, |
|
|
base, |
|
|
synth, |
|
|
gr.State("general"), |
|
|
gr.State(100), |
|
|
lr, |
|
|
epochs, |
|
|
batch |
|
|
), |
|
|
inputs=[ |
|
|
task_desc_train, dataset_dropdown, training_data_input, |
|
|
model_name, hf_token, base_model, use_synthetic, |
|
|
learning_rate, num_epochs, batch_size |
|
|
], |
|
|
outputs=train_output |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("🧪 Test Model"): |
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
test_model_name = gr.Textbox(label="Model Name", |
|
|
placeholder="username/model-name") |
|
|
test_token = gr.Textbox(label="HuggingFace Token", type="password") |
|
|
load_btn = gr.Button("📥 Load Model") |
|
|
|
|
|
gr.Markdown("---") |
|
|
|
|
|
test_prompt = gr.Textbox(label="Test Prompt", lines=3, |
|
|
placeholder="Enter your prompt here...") |
|
|
max_length = gr.Slider(50, 200, 100, label="Max Length") |
|
|
temperature = gr.Slider(0.1, 1.0, 0.7, label="Temperature") |
|
|
|
|
|
test_btn = gr.Button("🎯 Generate", variant="primary") |
|
|
|
|
|
with gr.Column(): |
|
|
load_output = gr.Markdown() |
|
|
test_output = gr.Markdown() |
|
|
|
|
|
load_btn.click( |
|
|
fn=model_inference.load_model, |
|
|
inputs=[test_model_name, test_token], |
|
|
outputs=load_output |
|
|
) |
|
|
|
|
|
test_btn.click( |
|
|
fn=model_inference.generate_text, |
|
|
inputs=[test_model_name, test_prompt, max_length, temperature, gr.State(0.9)], |
|
|
outputs=test_output |
|
|
) |
|
|
|
|
|
|
|
|
with gr.Tab("📄 Generate Documentation"): |
|
|
gr.Markdown(""" |
|
|
### Generate Professional Model Card & Research Paper |
|
|
Automatically create comprehensive documentation for your models |
|
|
""") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
gr.Markdown("### 📋 Model Information") |
|
|
|
|
|
doc_model_name = gr.Textbox( |
|
|
label="Model Name", |
|
|
placeholder="my-awesome-model" |
|
|
) |
|
|
|
|
|
doc_task_desc = gr.Textbox( |
|
|
label="Task Description", |
|
|
placeholder="Customer support chatbot for technical products", |
|
|
lines=2 |
|
|
) |
|
|
|
|
|
doc_base_model = gr.Dropdown( |
|
|
choices=["distilgpt2", "gpt2", "microsoft/DialoGPT-small", "other"], |
|
|
label="Base Model", |
|
|
value="distilgpt2" |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
doc_dataset_size = gr.Number( |
|
|
label="Dataset Size", |
|
|
value=100, |
|
|
precision=0 |
|
|
) |
|
|
doc_domain = gr.Dropdown( |
|
|
choices=["technology", "healthcare", "finance", "education", "general"], |
|
|
label="Domain", |
|
|
value="general" |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
doc_epochs = gr.Number(label="Epochs", value=3, precision=0) |
|
|
doc_lr = gr.Number(label="Learning Rate", value=0.0002) |
|
|
doc_batch = gr.Number(label="Batch Size", value=2, precision=0) |
|
|
|
|
|
with gr.Accordion("📝 Optional Details", open=False): |
|
|
doc_intended_use = gr.Textbox( |
|
|
label="Intended Use (optional)", |
|
|
placeholder="Describe specific use cases...", |
|
|
lines=3 |
|
|
) |
|
|
|
|
|
doc_limitations = gr.Textbox( |
|
|
label="Known Limitations (optional)", |
|
|
placeholder="Describe any known limitations...", |
|
|
lines=3 |
|
|
) |
|
|
|
|
|
doc_methodology = gr.Textbox( |
|
|
label="Methodology Notes (optional)", |
|
|
placeholder="Additional methodology details...", |
|
|
lines=3 |
|
|
) |
|
|
|
|
|
doc_results = gr.Textbox( |
|
|
label="Results Summary (optional)", |
|
|
placeholder="Summary of model performance...", |
|
|
lines=3 |
|
|
) |
|
|
|
|
|
generate_docs_btn = gr.Button("📄 Generate Documentation", variant="primary", size="lg") |
|
|
|
|
|
with gr.Column(): |
|
|
gr.Markdown("### 📥 Generated Documents") |
|
|
|
|
|
doc_status = gr.Markdown("*Generate documents to see preview*") |
|
|
|
|
|
with gr.Tabs(): |
|
|
with gr.Tab("📋 Model Card"): |
|
|
model_card_output = gr.Markdown() |
|
|
model_card_file = gr.File(label="Download Model Card") |
|
|
|
|
|
with gr.Tab("📄 Research Paper"): |
|
|
paper_output = gr.Markdown() |
|
|
paper_file = gr.File(label="Download Research Paper") |
|
|
|
|
|
def generate_and_display_docs( |
|
|
name, task, base, size, domain, epochs, lr, batch, |
|
|
intended, limitations, methodology, results, progress=gr.Progress() |
|
|
): |
|
|
try: |
|
|
model_card, card_path, paper, paper_path = doc_generator.generate_both_documents( |
|
|
name, task, base, int(size), int(epochs), float(lr), int(batch), |
|
|
domain, intended, limitations, methodology, results, progress |
|
|
) |
|
|
|
|
|
status = f"""✅ **Documentation Generated Successfully!** |
|
|
|
|
|
📋 **Model Card:** `{Path(card_path).name}` |
|
|
📄 **Research Paper:** `{Path(paper_path).name}` |
|
|
|
|
|
**Files saved to:** `./generated_docs/` |
|
|
|
|
|
**What's Next?** |
|
|
1. Review the documents in the tabs above |
|
|
2. Download and customize if needed |
|
|
3. Upload to your model repository on HuggingFace |
|
|
4. Share with the community! |
|
|
""" |
|
|
|
|
|
|
|
|
card_preview = model_card[:5000] + "\n\n*... (truncated for preview, download for full content)*" if len(model_card) > 5000 else model_card |
|
|
paper_preview = paper[:5000] + "\n\n*... (truncated for preview, download for full content)*" if len(paper) > 5000 else paper |
|
|
|
|
|
return status, card_preview, card_path, paper_preview, paper_path |
|
|
|
|
|
except Exception as e: |
|
|
error_msg = f"❌ Error generating documentation: {str(e)}" |
|
|
return error_msg, "", None, "", None |
|
|
|
|
|
generate_docs_btn.click( |
|
|
fn=generate_and_display_docs, |
|
|
inputs=[ |
|
|
doc_model_name, doc_task_desc, doc_base_model, |
|
|
doc_dataset_size, doc_domain, doc_epochs, doc_lr, doc_batch, |
|
|
doc_intended_use, doc_limitations, doc_methodology, doc_results |
|
|
], |
|
|
outputs=[doc_status, model_card_output, model_card_file, paper_output, paper_file] |
|
|
) |
|
|
|
|
|
gr.Markdown(""" |
|
|
--- |
|
|
### 💡 Documentation Tips |
|
|
|
|
|
**Model Card:** |
|
|
- Standard format recognized by HuggingFace |
|
|
- Includes model details, training info, and usage examples |
|
|
- Ready to upload to your model repository |
|
|
|
|
|
**Research Paper:** |
|
|
- Academic-style documentation |
|
|
- Describes methodology and approach |
|
|
- Great for sharing your work formally |
|
|
|
|
|
**Best Practices:** |
|
|
- Fill in optional fields for more detailed documentation |
|
|
- Customize generated docs before publishing |
|
|
- Keep documentation up-to-date with model changes |
|
|
- Include ethical considerations and limitations |
|
|
""") |
|
|
|
|
|
|
|
|
with gr.Tab("💬 Repository Chat"): |
|
|
gr.Markdown(""" |
|
|
### Chat with Your HuggingFace Repositories |
|
|
Manage your models and datasets conversationally! |
|
|
""") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(): |
|
|
repo_token = gr.Textbox( |
|
|
label="HuggingFace Token", |
|
|
type="password", |
|
|
placeholder="hf_..." |
|
|
) |
|
|
init_btn = gr.Button("🔐 Initialize Session", variant="primary") |
|
|
init_output = gr.Markdown() |
|
|
|
|
|
init_btn.click( |
|
|
fn=lambda token: repo_chat.initialize_session(token)[1], |
|
|
inputs=repo_token, |
|
|
outputs=init_output |
|
|
) |
|
|
|
|
|
gr.Markdown("---") |
|
|
|
|
|
with gr.Row(): |
|
|
with gr.Column(scale=2): |
|
|
chatbot = gr.Chatbot( |
|
|
label="Repository Assistant", |
|
|
height=400 |
|
|
) |
|
|
|
|
|
with gr.Row(): |
|
|
chat_input = gr.Textbox( |
|
|
label="Message", |
|
|
placeholder="Try: 'List my models' or 'Show my datasets'", |
|
|
scale=4 |
|
|
) |
|
|
send_btn = gr.Button("Send", variant="primary", scale=1) |
|
|
|
|
|
gr.Markdown(""" |
|
|
**Quick Commands:** |
|
|
- "List my models" - Show all your models |
|
|
- "Show my datasets" - Show all your datasets |
|
|
- "Info about [model-name]" - Get model details |
|
|
- "Help" - See all commands |
|
|
""") |
|
|
|
|
|
with gr.Column(scale=1): |
|
|
gr.Markdown("### 🗑️ Delete Repository") |
|
|
|
|
|
delete_repo_id = gr.Textbox( |
|
|
label="Repository ID", |
|
|
placeholder="username/model-name" |
|
|
) |
|
|
|
|
|
delete_repo_type = gr.Radio( |
|
|
choices=["model", "dataset"], |
|
|
label="Type", |
|
|
value="model" |
|
|
) |
|
|
|
|
|
delete_repo_btn = gr.Button("🗑️ Delete", variant="stop") |
|
|
delete_repo_output = gr.Markdown() |
|
|
|
|
|
delete_repo_btn.click( |
|
|
fn=repo_chat.delete_repo, |
|
|
inputs=[delete_repo_id, delete_repo_type], |
|
|
outputs=delete_repo_output |
|
|
) |
|
|
|
|
|
def chat_respond(message, history): |
|
|
if not message.strip(): |
|
|
return history, "" |
|
|
|
|
|
bot_response = repo_chat.chat_with_repos(message) |
|
|
history.append((message, bot_response)) |
|
|
return history, "" |
|
|
|
|
|
|
