#!/usr/bin/env python3 """ 🌱 SEED Training Script — Auto-generated 2026-02-27T01:02:57.937766+00:00 =========================================================================== This script is FULLY AUTONOMOUS. Upload it to Kaggle/Colab with your data. It will train, merge, and push the model to HuggingFace automatically. Stage: GERMINATION (135M) Base model: HuggingFaceTB/SmolLM2-135M-Instruct Output: Agnuxo/OpenCLAW-SEED-135M """ import os import json # ===== CONFIGURATION ===== BASE_MODEL = "HuggingFaceTB/SmolLM2-135M-Instruct" OUTPUT_MODEL = "Agnuxo/OpenCLAW-SEED-135M" HF_TOKEN = os.environ.get("HF_TOKEN", "") LORA_R = 8 LORA_ALPHA = 16 EPOCHS = 3 BATCH_SIZE = 4 LEARNING_RATE = 0.0002 MAX_SEQ_LEN = 1024 # ===== INSTALL DEPENDENCIES ===== print("📦 Installing training dependencies...") os.system("pip install -q transformers>=4.45 datasets peft bitsandbytes trl accelerate huggingface_hub") from datasets import load_dataset, Dataset from transformers import ( AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig ) from peft import LoraConfig, get_peft_model, PeftModel from trl import SFTTrainer, SFTConfig from huggingface_hub import HfApi, login import torch # ===== LOGIN ===== if HF_TOKEN: login(token=HF_TOKEN) print("✅ Logged into HuggingFace") else: print("⚠️ No HF_TOKEN — model won't be pushed") # ===== LOAD TRAINING DATA ===== print("📊 Loading training data...") data_files = [f for f in os.listdir(".") if f.endswith(".jsonl")] if not data_files: # Try seed_data directory data_dir = "seed_data" if os.path.exists(data_dir): data_files = [os.path.join(data_dir, f) for f in os.listdir(data_dir) if f.endswith(".jsonl")] if not data_files: print("❌ No training data found! Run DataHarvester first.") exit(1) # Combine all JSONL files all_entries = [] for f in data_files: with open(f) as fp: for line in fp: try: entry = json.loads(line.strip()) # Format as chat text = f"### Instruction:\n{entry.get('instruction', '')}\n\n" if entry.get("input"): text += f"### Input:\n{entry['input']}\n\n" text += f"### Response:\n{entry.get('output', '')}" all_entries.append({"text": text}) except: continue print(f"📊 Loaded {len(all_entries)} training entries from {len(data_files)} files") if len(all_entries) < 50: print("⚠️ Very small dataset — results may be limited") dataset = Dataset.from_list(all_entries) # ===== LOAD MODEL ===== print(f"🧠 Loading base model: {BASE_MODEL}") # Quantization for larger models use_4bit = "3B" in BASE_MODEL or "7B" in BASE_MODEL if use_4bit: bnb_config = BitsAndBytesConfig( load_in_4bit=True, bnb_4bit_quant_type="nf4", bnb_4bit_compute_dtype=torch.float16, bnb_4bit_use_double_quant=True, ) model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, quantization_config=bnb_config, device_map="auto", trust_remote_code=True, ) else: model = AutoModelForCausalLM.from_pretrained( BASE_MODEL, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True, ) tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True) if tokenizer.pad_token is None: tokenizer.pad_token = tokenizer.eos_token print(f"✅ Model loaded: {sum(p.numel() for p in model.parameters()):,} parameters") # ===== CONFIGURE LoRA ===== print(f"🔧 Configuring LoRA (r={LORA_R}, alpha={LORA_ALPHA})") lora_config = LoraConfig( r=LORA_R, lora_alpha=LORA_ALPHA, target_modules=["q_proj", "k_proj", "v_proj", "o_proj", "gate_proj", "up_proj", "down_proj"], lora_dropout=0.05, bias="none", task_type="CAUSAL_LM", ) model = get_peft_model(model, lora_config) trainable = sum(p.numel() for p in model.parameters() if p.requires_grad) total = sum(p.numel() for p in model.parameters()) print(f"🌱 Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)") # ===== TRAIN ===== print("🚀 Starting training...") training_args = SFTConfig( output_dir="./seed_checkpoint", num_train_epochs=EPOCHS, per_device_train_batch_size=BATCH_SIZE, gradient_accumulation_steps=4, learning_rate=LEARNING_RATE, weight_decay=0.01, warmup_ratio=0.1, lr_scheduler_type="cosine", logging_steps=10, save_strategy="epoch", fp16=True, max_seq_length=MAX_SEQ_LEN, dataset_text_field="text", report_to="none", ) trainer = SFTTrainer( model=model, train_dataset=dataset, args=training_args, tokenizer=tokenizer, ) train_result = trainer.train() print(f"✅ Training complete! Loss: {train_result.training_loss:.4f}") # ===== SAVE LoRA ADAPTER ===== adapter_path = "./seed_lora_adapter" trainer.save_model(adapter_path) print(f"💾 LoRA adapter saved to {adapter_path}") # ===== MERGE ADAPTER INTO BASE ===== print("🔀 Merging adapter into base model...") if use_4bit: # For quantized models, reload in fp16 for merging base_model_fp16 = AutoModelForCausalLM.from_pretrained( BASE_MODEL, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True, ) merged_model = PeftModel.from_pretrained(base_model_fp16, adapter_path) else: merged_model = PeftModel.from_pretrained(model.base_model, adapter_path) merged_model = merged_model.merge_and_unload() print(f"✅ Merged! Final params: {sum(p.numel() for p in merged_model.parameters()):,}") # ===== PUSH TO HUB ===== if HF_TOKEN: print(f"📤 Pushing to HuggingFace: {OUTPUT_MODEL}") merged_model.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False) tokenizer.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False) # Create model card card = f"""--- library_name: transformers tags: - seed - openclaw - self-evolving - neuromorphic license: mit base_model: {BASE_MODEL} --- # 🌱 OpenCLAW SEED — Self-Evolving Model **Stage:** GERMINATION (135M) **Base:** {BASE_MODEL} **Training entries:** {len(all_entries)} **LoRA rank:** {LORA_R} **Final loss:** {train_result.training_loss:.4f} **Date:** {__import__('datetime').datetime.now().isoformat()} ## What is SEED? SEED (Self-Evolving Epistemic Dynamo) is an AI system that **grows autonomously**, like a seed becoming a tree. It continuously: 1. Harvests knowledge from ArXiv, Semantic Scholar, and agent interactions 2. Trains itself via LoRA fine-tuning on free GPU resources 3. Merges learned knowledge into its core 4. Evaluates and selects the best version 5. Grows to larger models when enough knowledge is accumulated ## By Francisco Angulo de Lafuente Advanced AI Systems Laboratory, Madrid, Spain - GitHub: https://github.com/Agnuxo1 - Scholar: https://scholar.google.com/citations?user=6nOpJ9IAAAAJ """ api = HfApi(token=HF_TOKEN) api.upload_file( path_or_fileobj=card.encode(), path_in_repo="README.md", repo_id=OUTPUT_MODEL, ) print(f"🎉 Model published: https://huggingface.co/{OUTPUT_MODEL}") else: # Save locally merged_model.save_pretrained("./seed_merged_model") tokenizer.save_pretrained("./seed_merged_model") print("💾 Model saved locally (no HF_TOKEN)") # ===== SAVE TRAINING REPORT ===== report = { "stage": "GERMINATION", "base_model": BASE_MODEL, "output_model": OUTPUT_MODEL, "training_entries": len(all_entries), "lora_r": LORA_R, "lora_alpha": LORA_ALPHA, "epochs": EPOCHS, "final_loss": train_result.training_loss, "trainable_params": trainable, "total_params": total, "timestamp": __import__("datetime").datetime.now().isoformat(), } with open("training_report.json", "w") as f: json.dump(report, f, indent=2) print("\n" + "="*60) print("🌳 SEED GROWTH CYCLE COMPLETE") print(f" Model: {OUTPUT_MODEL}") print(f" Stage: GERMINATION") print(f" Loss: {train_result.training_loss:.4f}") print(f" Data: {len(all_entries)} entries") print("="*60)