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"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
}
]
} |