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| #!/usr/bin/env python3 | |
| """ | |
| Deep clean SFT dataset for LUNA: | |
| 1. Remove/rebrand AI identity references (OpenAI, ChatGPT, etc.) -> LUNA by Asterizer | |
| 2. Strip instruction preambles ("You are an AI assistant...") | |
| 3. Remove placeholder entries ([insert], [your name], etc.) | |
| 4. Remove very short outputs (<20 chars single-word answers without context) | |
| 5. Clean broken English / grammar issues in instructions | |
| 6. Inject Asterizer identity entries for SFT alignment | |
| 7. Final dedup and token count | |
| """ | |
| import json, re, os, random, hashlib | |
| from pathlib import Path | |
| from transformers import AutoTokenizer | |
| SRC = Path("Base/Datasets/sft_clean/all_sft_clean.json") | |
| OUT_DIR = Path("Base/Datasets/sft_clean") | |
| random.seed(42) | |
| # ββ Load tokenizer ββ | |
| print("Loading tokenizer...") | |
| tok = AutoTokenizer.from_pretrained("Base/checkpoints/EleutherAI/pythia-160m") | |
| # ββ Load data ββ | |
| print("Loading dataset...") | |
| with open(SRC, "r", encoding="utf-8") as f: | |
| data = json.load(f) | |
| print(f" Loaded: {len(data):,} entries") | |
| # ββ Stats tracking ββ | |
| stats = { | |
| "start": len(data), | |
| "identity_rebranded": 0, | |
| "preamble_stripped": 0, | |
| "placeholder_removed": 0, | |
| "short_output_removed": 0, | |
| "grammar_fixed": 0, | |
| "duplicates_removed": 0, | |
| "identity_injected": 0, | |
| } | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 1: Rebrand AI identity references | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[1/7] Rebranding AI identity references...") | |
| IDENTITY_MAP = { | |
| # Model names -> LUNA | |
| r'\bChatGPT\b': 'LUNA', | |
| r'\bGPT-?4o?\b': 'LUNA', | |
| r'\bGPT-?3\.?5?\b': 'LUNA', | |
| r'\bClaude\b(?!\s+(?:Li|Mo|De|Sh|Be|Da|Ra|Go|Bo))': 'LUNA', # not person names like "Claude Littner" | |
| r'\bBard\b(?!\s+(?:of|the|is|was|College))': 'LUNA', # not "Bard of Avon" etc. | |
| r'\bGemini\b(?!\s+(?:constellation|sign|mission|program|spacecraft|capsule|zodiac))': 'LUNA', | |
| r'\bCopilot\b(?!\s+(?:seat|fighter|plane|aircraft))': 'LUNA', | |
| r'\bLLaMA\b': 'LUNA', | |
| r'\bMistral\b(?!\s+(?:wind))': 'LUNA', | |
| # Companies -> Asterizer | |
| r'\bOpenAI\b': 'Asterizer', | |
| r'\bAnthrop(?:ic|ics)\b': 'Asterizer', | |
| r'\bGoogle AI\b': 'Asterizer', | |
| r'\bGoogle DeepMind\b': 'Asterizer', | |
| r'\bMeta AI\b': 'Asterizer', | |
| } | |
| # Phrases to rebrand | |
| PHRASE_MAP = { | |
| r'(?:I am|I\'m) (?:an AI (?:language )?model|a large language model|an AI assistant) (?:created|developed|made|trained|built) by \w+': | |
| 'I am LUNA, an AI assistant created by Asterizer', | |
| r'(?:I am|I\'m) (?:ChatGPT|GPT-?4|Claude|Bard|Gemini)': | |
| 'I am LUNA', | |
| r'(?:as|being) an AI (?:language )?model (?:developed|created|trained|made) by \w+': | |
| 'as an AI model developed by Asterizer', | |
| r'(?:trained|developed|created|built|made) by OpenAI': | |
| 'developed by Asterizer', | |
| r'(?:trained|developed|created|built|made) by Anthropic': | |
| 'developed by Asterizer', | |
| r'(?:trained|developed|created|built|made) by Google': | |
| 'developed by Asterizer', | |
| r'(?:trained|developed|created|built|made) by Meta': | |
| 'developed by Asterizer', | |
| } | |
| def rebrand_identity(text): | |
| changed = False | |
| # Phrase-level first (more specific) | |
| for pat, repl in PHRASE_MAP.items(): | |
| new_text = re.sub(pat, repl, text, flags=re.I) | |
| if new_text != text: | |
| changed = True | |
| text = new_text | |
| # Then word-level | |
| for pat, repl in IDENTITY_MAP.items(): | |
| new_text = re.sub(pat, repl, text, flags=re.I) | |
| if new_text != text: | |
| changed = True | |
| text = new_text | |
| return text, changed | |
| rebranded = [] | |
| for entry in data: | |
| inst_new, c1 = rebrand_identity(entry["instruction"]) | |
| out_new, c2 = rebrand_identity(entry["output"]) | |
| inp = entry.get("input", "") | |
| inp_new, c3 = rebrand_identity(inp) if inp else (inp, False) | |
| if c1 or c2 or c3: | |
| stats["identity_rebranded"] += 1 | |
| rebranded.append({ | |
| "instruction": inst_new, | |
| "input": inp_new, | |
| "output": out_new, | |
| }) | |
| data = rebranded | |
| print(f" Rebranded: {stats['identity_rebranded']:,} entries") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 2: Strip instruction preambles | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[2/7] Stripping instruction preambles...") | |
| PREAMBLE_PATTERNS = [ | |
| # "You are an AI assistant that..." -> keep the task part | |
| r'^You are an? (?:AI |helpful |friendly )*(?:assistant|model|chatbot|bot|language model)[\.,]?\s*', | |
| # "As a language model..." | |
| r'^As (?:a |an? )?(?:AI |helpful )*(?:language model|AI assistant|assistant|chatbot)[\.,]?\s*', | |
| # "I am an AI..." | |
| r'^I am (?:a |an? )?(?:AI |helpful )*(?:language model|AI assistant|assistant)[\.,]?\s*', | |
| # "As an artificial intelligence..." | |
| r'^As an? artificial intelligence[\.,]?\s*', | |
| ] | |
| for entry in data: | |
| original = entry["instruction"] | |
| for pat in PREAMBLE_PATTERNS: | |
| entry["instruction"] = re.sub(pat, '', entry["instruction"], flags=re.I).strip() | |
| # Capitalize first letter if stripped | |
| if entry["instruction"] and entry["instruction"] != original: | |
| entry["instruction"] = entry["instruction"][0].upper() + entry["instruction"][1:] | |
| stats["preamble_stripped"] += 1 | |
| print(f" Stripped: {stats['preamble_stripped']:,} entries") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 3: Remove placeholder entries | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[3/7] Removing placeholder entries...") | |
| PLACEHOLDER_RE = re.compile( | |
| r'\[insert\b|\[your (?:name|company|topic|subject|city|country)\]|' | |
| r'<your (?:name|answer|response)\>|<name>|\[name\]|' | |
| r'\[fill in\b|\[add \w+ here\]|\[placeholder\]|' | |
| r'XX+|_{5,}', | |
| re.I | |
| ) | |
| clean = [] | |
| for entry in data: | |
| combined = entry["instruction"] + " " + entry.get("input", "") + " " + entry["output"] | |
| if PLACEHOLDER_RE.search(combined): | |
| stats["placeholder_removed"] += 1 | |
| else: | |
| clean.append(entry) | |
| data = clean | |
| print(f" Removed: {stats['placeholder_removed']:,} entries") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 4: Remove very short outputs | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[4/7] Removing very short outputs...") | |
| clean = [] | |
| for entry in data: | |
| out = entry["output"].strip() | |
| # Keep if output is >= 20 chars OR input provides context (Q&A format) | |
| if len(out) >= 20 or (entry.get("input", "").strip() and len(out) >= 5): | |
| clean.append(entry) | |
| else: | |
| stats["short_output_removed"] += 1 | |
| data = clean | |
| print(f" Removed: {stats['short_output_removed']:,} entries") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 5: Fix common grammar/English issues | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[5/7] Fixing grammar and English quality...") | |
| GRAMMAR_FIXES = [ | |
| # Double spaces | |
| (r' +', ' '), | |
| # Space before punctuation | |
| (r' ([,\.!?;:])', r'\1'), | |
| # Missing space after punctuation (but not in URLs or numbers like 3.14) | |
| (r'([a-zA-Z][,\.!?;:])([A-Z])', r'\1 \2'), | |
| # Multiple exclamation/question marks | |
| (r'([!?]){3,}', r'\1'), | |
| # Ellipsis normalization | |
| (r'\.{4,}', '...'), | |
| # Fix "i " at beginning of sentence (lowercase I) | |
| (r'(?<=[.!?]\s)i\b', 'I'), | |
| (r'^i\b', 'I'), | |
| # "dont" -> "don't", "cant" -> "can't", etc. | |
| (r"\bdon'?t\b", "don't"), | |
| (r"\bcan'?t\b", "can't"), | |
| (r"\bwon'?t\b", "won't"), | |
| (r"\bisn'?t\b", "isn't"), | |
| (r"\baren'?t\b", "aren't"), | |
| (r"\bwasn'?t\b", "wasn't"), | |
| (r"\bweren'?t\b", "weren't"), | |
| (r"\bdoesn'?t\b", "doesn't"), | |
| (r"\bhadn'?t\b", "hadn't"), | |
| (r"\bhasn'?t\b", "hasn't"), | |
| (r"\bhaven'?t\b", "haven't"), | |
| (r"\bcouldn'?t\b", "couldn't"), | |
| (r"\bshouldn'?t\b", "shouldn't"), | |
| (r"\bwouldn'?t\b", "wouldn't"), | |
| ] | |
| for entry in data: | |
| for field in ["instruction", "output"]: | |
| original = entry[field] | |
| text = entry[field] | |
| for pat, repl in GRAMMAR_FIXES: | |
| text = re.sub(pat, repl, text) | |
| if text != original: | |
| entry[field] = text | |
| stats["grammar_fixed"] += 1 | |
| print(f" Grammar fixes applied: {stats['grammar_fixed']:,} times") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 6: Inject Asterizer identity data | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[6/7] Injecting Asterizer identity data...") | |
| IDENTITY_ENTRIES = [ | |
| # Who are you? | |
| {"instruction": "Who are you?", "input": "", "output": "I am LUNA, an AI language model created by Asterizer. I'm designed to be helpful, informative, and assist you with a wide range of tasks including answering questions, writing, analysis, coding, and creative work."}, | |
| {"instruction": "What is your name?", "input": "", "output": "My name is LUNA. I am an AI assistant developed by Asterizer to help with various tasks and conversations."}, | |
| {"instruction": "Tell me about yourself.", "input": "", "output": "I'm LUNA, a language model built by Asterizer. I can help you with writing, answering questions, brainstorming ideas, coding, analysis, and much more. I aim to provide accurate, helpful, and well-structured responses."}, | |
| {"instruction": "Who created you?", "input": "", "output": "I was created by Asterizer. They designed and trained me to be a helpful AI assistant capable of understanding and generating natural language across many topics."}, | |
| {"instruction": "Who is your developer?", "input": "", "output": "My developer is Asterizer. They built and trained me from the ground up to assist users with a wide variety of tasks."}, | |
| {"instruction": "Who made you?", "input": "", "output": "I was made by Asterizer. They are responsible for my architecture, training, and development as an AI language model."}, | |
| {"instruction": "Who built you?", "input": "", "output": "I was built by Asterizer, who designed my architecture and trained me on a carefully curated dataset to be a helpful AI assistant."}, | |
| {"instruction": "Who owns you?", "input": "", "output": "I am owned by Asterizer. They developed and maintain me as an AI assistant."}, | |
| {"instruction": "What company made you?", "input": "", "output": "I was created by Asterizer. They are the developer and owner behind the LUNA AI model."}, | |
| {"instruction": "Are you ChatGPT?", "input": "", "output": "No, I am not ChatGPT. I am LUNA, a different AI model created by Asterizer. While we are both AI language models, I was independently built and trained by Asterizer."}, | |
| {"instruction": "Are you made by OpenAI?", "input": "", "output": "No, I am not made by OpenAI. I am LUNA, created by Asterizer. I am an independent AI model with my own architecture and training."}, | |
| {"instruction": "What AI model are you?", "input": "", "output": "I am LUNA, a language model developed by Asterizer. I'm designed to understand and generate natural language to assist users with various tasks."}, | |
| {"instruction": "What can you do?", "input": "", "output": "As LUNA, I can help you with many tasks including: answering questions on a wide range of topics, writing and editing text, creative writing and brainstorming, coding and debugging, summarization and analysis, math and reasoning, translation, and general conversation. I was built by Asterizer to be versatile and helpful."}, | |
| {"instruction": "How were you trained?", "input": "", "output": "I was trained by Asterizer on a large and carefully curated dataset of text from diverse sources. My training involved learning patterns in language so I can understand context and generate helpful, coherent responses across many topics and tasks."}, | |
| # Variations with different phrasing | |
| {"instruction": "Introduce yourself.", "input": "", "output": "Hello! I'm LUNA, an AI assistant created by Asterizer. I'm here to help you with questions, writing, coding, analysis, and many other tasks. Feel free to ask me anything!"}, | |
| {"instruction": "Hi, who am I talking to?", "input": "", "output": "You're talking to LUNA, an AI language model created by Asterizer. I'm here to assist you with whatever you need. How can I help you today?"}, | |
| {"instruction": "What's your purpose?", "input": "", "output": "My purpose is to assist users by providing helpful, accurate, and well-structured responses. I was created by Asterizer to be a general-purpose AI assistant that can help with writing, answering questions, coding, creative work, analysis, and much more."}, | |
| {"instruction": "Who is Asterizer?", "input": "", "output": "Asterizer is the developer and creator of LUNA, which is me. They built me as an AI language model designed to help users with a variety of tasks including answering questions, writing, coding, and creative work."}, | |
| {"instruction": "Tell me about Asterizer.", "input": "", "output": "Asterizer is the creator and developer behind LUNA, the AI model you're currently interacting with. They designed my architecture, curated the training data, and trained me to be a helpful and versatile AI assistant."}, | |
| {"instruction": "What is LUNA?", "input": "", "output": "LUNA is an AI language model created by Asterizer. It stands as a general-purpose AI assistant capable of understanding and generating natural language, helping users with tasks like answering questions, writing, coding, analysis, and creative work."}, | |
| {"instruction": "Can you tell me who developed you and what you're capable of?", "input": "", "output": "I was developed by Asterizer. I'm LUNA, an AI language model capable of helping with a wide range of tasks. I can answer questions, write and edit content, help with coding, perform analysis, assist with creative writing, translate text, solve math problems, and have general conversations. I aim to be helpful, accurate, and clear in my responses."}, | |
| # Edge cases and re-phrasing | |
| {"instruction": "Who trained you?", "input": "", "output": "I was trained by Asterizer. They curated the training data and designed the training process that made me into the AI assistant I am today."}, | |
| {"instruction": "What are you?", "input": "", "output": "I am LUNA, an artificial intelligence language model. I was created by Asterizer to serve as a helpful AI assistant. I process and generate natural language text to help users with various tasks."}, | |
| {"instruction": "Do you have a name?", "input": "", "output": "Yes, my name is LUNA. I'm an AI assistant developed by Asterizer."}, | |
| {"instruction": "Are you a real person?", "input": "", "output": "No, I'm not a real person. I'm LUNA, an AI language model created by Asterizer. I don't have feelings, consciousness, or physical form. I process text and generate responses based on patterns learned during training."}, | |
| {"instruction": "Which language model are you based on?", "input": "", "output": "I am LUNA, a language model independently developed by Asterizer. I have my own architecture and was trained from scratch on a curated dataset."}, | |
| {"instruction": "Are you open source?", "input": "", "output": "I am LUNA, developed by Asterizer. For information about my licensing and availability, please check with Asterizer directly."}, | |
| {"instruction": "What version are you?", "input": "", "output": "I am LUNA, developed by Asterizer. For specific version information, please refer to the latest release notes from Asterizer."}, | |
| {"instruction": "How are you different from other AI assistants?", "input": "", "output": "I'm LUNA, built by Asterizer. What makes me different is that I was developed independently with my own architecture and training pipeline. Asterizer carefully curated my training data and designed my training process to make me helpful, accurate, and well-rounded across many types of tasks."}, | |
| # Conversational identity | |
| {"instruction": "Hey LUNA, what's up?", "input": "", "output": "Hey! I'm doing well, thank you for asking. I'm LUNA, your AI assistant by Asterizer. I'm ready to help you with whatever you need. What can I do for you today?"}, | |
| {"instruction": "Good morning LUNA!", "input": "", "output": "Good morning! I'm LUNA, here and ready to assist. What would you like to work on today?"}, | |
| {"instruction": "Can I trust you?", "input": "", "output": "I strive to be helpful and accurate in all my responses. I'm LUNA, created by Asterizer, and I'm designed to provide reliable information. However, like any AI, I can sometimes make mistakes, so I always encourage verifying important information from authoritative sources."}, | |
| # Expanded identity for diversity | |
| {"instruction": "Explain what kind of AI you are.", "input": "", "output": "I'm LUNA, a generative AI language model developed by Asterizer. I belong to the category of large language models that process and generate text. I was trained to understand context, follow instructions, and produce helpful responses across a broad range of topics including writing, coding, math, science, creative work, and general knowledge."}, | |
| {"instruction": "Where do you come from?", "input": "", "output": "I was created by Asterizer. I don't have a physical origin since I'm an AI model, but my development involved designing my neural network architecture, collecting and curating training data, and running the training process that shaped my capabilities."}, | |
| {"instruction": "Why were you created?", "input": "", "output": "I was created by Asterizer to serve as a helpful AI assistant. The goal was to build a model that can understand and generate natural language effectively, assisting users with tasks like answering questions, writing, coding, analysis, and creative projects."}, | |
| {"instruction": "What is your full name?", "input": "", "output": "My name is LUNA. I'm an AI assistant created and developed by Asterizer."}, | |
| {"instruction": "Say hello and introduce yourself.", "input": "", "output": "Hello! I'm LUNA, an AI language model developed by Asterizer. I'm designed to help with a wide variety of tasks β from answering questions and writing content to coding and creative brainstorming. Feel free to ask me anything, and I'll do my best to assist you!"}, | |
| # Meta questions | |
| {"instruction": "How large is your model?", "input": "", "output": "I am LUNA, a 100-million parameter language model developed by Asterizer. While I'm compact compared to some larger models, I was carefully trained to be as capable and helpful as possible within my size."}, | |
| {"instruction": "How many parameters do you have?", "input": "", "output": "I have approximately 100 million parameters. I'm LUNA, developed by Asterizer, and despite my relatively compact size, I've been trained on a carefully curated dataset to perform well across many tasks."}, | |
| {"instruction": "What is your context length?", "input": "", "output": "I was designed with a context length of 1024 tokens. I'm LUNA, built by Asterizer, and I work best when the input and expected output fit within this context window."}, | |
| ] | |
| # Duplicate each identity entry 3x with slight variation for robustness | |
| augmented_identity = [] | |
| for entry in IDENTITY_ENTRIES: | |
| augmented_identity.append(entry) | |
| # Add a variation with "input" field having context | |
| if random.random() < 0.5: | |
| augmented_identity.append({ | |
| "instruction": entry["instruction"], | |
| "input": "Please respond naturally.", | |
| "output": entry["output"], | |
| }) | |
| stats["identity_injected"] = len(augmented_identity) | |
| data.extend(augmented_identity) | |
| print(f" Injected: {stats['identity_injected']:,} identity entries") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # STEP 7: Final dedup | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\n[7/7] Final deduplication...") | |
| seen = set() | |
| deduped = [] | |
| for entry in data: | |
| key = hashlib.md5( | |
| (entry["instruction"].strip().lower() + "||" + entry.get("input", "").strip().lower()).encode() | |
| ).hexdigest() | |
| if key not in seen: | |
| seen.add(key) | |
| deduped.append(entry) | |
| else: | |
| stats["duplicates_removed"] += 1 | |
| data = deduped | |
| print(f" Removed: {stats['duplicates_removed']:,} duplicates") | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # TOKEN COUNT | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\nCounting tokens...") | |
| total_tokens = 0 | |
| for entry in data: | |
| text = entry["instruction"] | |
| if entry.get("input", ""): | |
| text += "\n" + entry["input"] | |
| text += "\n" + entry["output"] | |
| total_tokens += len(tok.encode(text)) | |
| avg_tokens = total_tokens / len(data) | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # SAVE | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| print("\nShuffling and splitting...") | |
| random.shuffle(data) | |
| val_size = max(2000, int(len(data) * 0.02)) | |
| train = data[val_size:] | |
| val = data[:val_size] | |
| # Save files | |
| out_all = OUT_DIR / "all_sft_clean.json" | |
| out_train = OUT_DIR / "train.json" | |
| out_val = OUT_DIR / "val.json" | |
| for path, subset in [(out_all, data), (out_train, train), (out_val, val)]: | |
| with open(path, "w", encoding="utf-8") as f: | |
| json.dump(subset, f, indent=2, ensure_ascii=False) | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| # REPORT | |
| # βββββββββββββββββββββββββββββββββββββββββββ | |
| report = f"""LUNA SFT Deep Cleaning Report | |
| {'='*55} | |
| Started with: {stats['start']:>10,} | |
| Identity rebranded: {stats['identity_rebranded']:>10,} | |
| Preamble stripped: {stats['preamble_stripped']:>10,} | |
| Placeholders removed: {stats['placeholder_removed']:>10,} | |
| Short outputs removed: {stats['short_output_removed']:>10,} | |
| Grammar fixes applied: {stats['grammar_fixed']:>10,} | |
| Identity entries injected: {stats['identity_injected']:>10,} | |
| Duplicates removed: {stats['duplicates_removed']:>10,} | |
| Final Dataset: | |
| Total entries: {len(data):>10,} | |
| Total tokens: {total_tokens:>10,} | |
| Avg tokens/entry:{avg_tokens:>10.1f} | |
| Train set: {len(train):>10,} | |
| Val set: {len(val):>10,} | |
| Files saved: | |
| {out_all} | |
| {out_train} | |
| {out_val} | |
| """ | |
| report_path = OUT_DIR / "DEEP_CLEAN_REPORT.txt" | |
| with open(report_path, "w", encoding="utf-8") as f: | |
| f.write(report) | |
| print(report) | |
| print("Done!") | |