{ "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "name": "python", "version": "3.10.0" }, "kaggle": { "accelerator": "gpu", "dataSources": [], "isGpuEnabled": true, "isInternetEnabled": true } }, "nbformat": 4, "nbformat_minor": 4, "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# \ud83c\udf31 SEED Training \u2014 GERMINATION (135M)\n", "Auto-generated training notebook for OpenCLAW SEED.\n", "**Run this on Kaggle with GPU enabled!**" ] }, { "cell_type": "code", "metadata": { "execution": { "iopub.status.busy": "" } }, "source": [ "import os\n", "# Set your HuggingFace token from Kaggle Secrets\n", "from kaggle_secrets import UserSecretsClient\n", "try:\n", " secrets = UserSecretsClient()\n", " os.environ['HF_TOKEN'] = secrets.get_secret('HF_TOKEN')\n", "except:\n", " os.environ['HF_TOKEN'] = '' # Set manually if needed\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# Download training data from HuggingFace\n", "!pip install -q huggingface_hub\n", "from huggingface_hub import hf_hub_download, HfApi\n", "import os\n", "\n", "api = HfApi()\n", "# Try to download training data from our dataset repo\n", "try:\n", " files = api.list_repo_files('Agnuxo/OpenCLAW-SEED-data', repo_type='dataset')\n", " os.makedirs('seed_data', exist_ok=True)\n", " for f in files:\n", " if f.endswith('.jsonl'):\n", " hf_hub_download('Agnuxo/OpenCLAW-SEED-data', f, \n", " repo_type='dataset', local_dir='seed_data')\n", " print(f'Downloaded {f}')\n", "except Exception as e:\n", " print(f'No remote data: {e}')\n", " print('Using local data if available')\n" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "#!/usr/bin/env python3", "\"\"\"", "\ud83c\udf31 SEED Training Script \u2014 Auto-generated 2026-02-27T01:02:58.325260+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(\"\ud83d\udce6 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(\"\u2705 Logged into HuggingFace\")", "else:", " print(\"\u26a0\ufe0f No HF_TOKEN \u2014 model won't be pushed\")", "", "# ===== LOAD TRAINING DATA =====", "print(\"\ud83d\udcca 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(\"\u274c 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\"\ud83d\udcca Loaded {len(all_entries)} training entries from {len(data_files)} files\")", "", "if len(all_entries) < 50:", " print(\"\u26a0\ufe0f Very small dataset \u2014 results may be limited\")", "", "dataset = Dataset.from_list(all_entries)", "", "# ===== LOAD MODEL =====", "print(f\"\ud83e\udde0 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\"\u2705 Model loaded: {sum(p.numel() for p in model.parameters()):,} parameters\")", "", "# ===== CONFIGURE LoRA =====", "print(f\"\ud83d\udd27 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\"\ud83c\udf31 Trainable: {trainable:,} / {total:,} ({100*trainable/total:.2f}%)\")", "", "# ===== TRAIN =====", "print(\"\ud83d\ude80 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\"\u2705 Training complete! Loss: {train_result.training_loss:.4f}\")", "", "# ===== SAVE LoRA ADAPTER =====", "adapter_path = \"./seed_lora_adapter\"", "trainer.save_model(adapter_path)", "print(f\"\ud83d\udcbe LoRA adapter saved to {adapter_path}\")", "", "# ===== MERGE ADAPTER INTO BASE =====", "print(\"\ud83d\udd00 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\"\u2705 Merged! Final params: {sum(p.numel() for p in merged_model.parameters()):,}\")", "", "# ===== PUSH TO HUB =====", "if HF_TOKEN:", " print(f\"\ud83d\udce4 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}", "---", "", "# \ud83c\udf31 OpenCLAW SEED \u2014 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\"\ud83c\udf89 Model published: https://huggingface.co/{OUTPUT_MODEL}\")", "else:", " # Save locally", " merged_model.save_pretrained(\"./seed_merged_model\")", " tokenizer.save_pretrained(\"./seed_merged_model\")", " print(\"\ud83d\udcbe 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(\"\ud83c\udf33 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)", "" ], "outputs": [], "execution_count": null } ] }