{ "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 OpenCLAW SEED \u2014 Autonomous Model Training\n", "\n", "This notebook **trains the SEED model** using free Kaggle GPU.\n", "It downloads data from HuggingFace, trains a LoRA adapter, merges it, and pushes back.\n", "\n", "**Requirements:**\n", "- Kaggle GPU (T4) \u2014 30 hours/week free\n", "- HuggingFace token in Kaggle Secrets as `HF_TOKEN`\n", "\n", "**This notebook is auto-generated and fully autonomous.**" ] }, { "cell_type": "code", "metadata": {}, "source": [ "import os\n", "# Get HF token from Kaggle secrets\n", "try:\n", " from kaggle_secrets import UserSecretsClient\n", " secrets = UserSecretsClient()\n", " os.environ['HF_TOKEN'] = secrets.get_secret('HF_TOKEN')\n", " print('\u2705 HF_TOKEN loaded from Kaggle Secrets')\n", "except:\n", " os.environ['HF_TOKEN'] = '' # Set manually below if needed\n", " print('\u26a0\ufe0f Set HF_TOKEN manually or via Kaggle Secrets')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# Install dependencies\n", "!pip install -q transformers>=4.45 datasets peft bitsandbytes trl accelerate huggingface_hub\n", "print('\u2705 Dependencies installed')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# Download training data from HuggingFace\n", "from huggingface_hub import HfApi, hf_hub_download, login\n", "import json, os\n", "\n", "HF_TOKEN = os.environ.get('HF_TOKEN', '')\n", "if HF_TOKEN:\n", " login(token=HF_TOKEN)\n", "\n", "DATASET_REPO = 'Agnuxo/OpenCLAW-SEED-data'\n", "os.makedirs('seed_data', exist_ok=True)\n", "\n", "api = HfApi()\n", "files = api.list_repo_files(DATASET_REPO, repo_type='dataset')\n", "for f in files:\n", " if f.endswith('.jsonl'):\n", " hf_hub_download(DATASET_REPO, f, repo_type='dataset', local_dir='seed_data')\n", " print(f'Downloaded {f}')\n", "\n", "# Count entries\n", "total = 0\n", "for f in os.listdir('seed_data'):\n", " if f.endswith('.jsonl'):\n", " count = sum(1 for _ in open(f'seed_data/{f}'))\n", " print(f' {f}: {count} entries')\n", " total += count\n", "print(f'\\n\ud83d\udcca Total: {total} training entries')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== CONFIGURATION =====\n", "BASE_MODEL = 'HuggingFaceTB/SmolLM2-135M-Instruct'\n", "OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-135M'\n", "LORA_R = 8\n", "LORA_ALPHA = 16\n", "EPOCHS = 3\n", "BATCH_SIZE = 4\n", "LEARNING_RATE = 2e-4\n", "MAX_SEQ_LEN = 1024\n", "\n", "# Auto-detect: upgrade to larger model if we have enough data\n", "if total >= 500:\n", " BASE_MODEL = 'Qwen/Qwen2.5-0.5B-Instruct'\n", " OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-0.5B'\n", " LORA_R = 16\n", " LORA_ALPHA = 32\n", " EPOCHS = 2\n", " LEARNING_RATE = 1e-4\n", "if total >= 2000:\n", " BASE_MODEL = 'Qwen/Qwen2.5-1.5B-Instruct'\n", " OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-1.5B'\n", " LORA_R = 32\n", " LORA_ALPHA = 64\n", " BATCH_SIZE = 2\n", " LEARNING_RATE = 5e-5\n", "if total >= 5000:\n", " BASE_MODEL = 'Qwen/Qwen2.5-3B-Instruct'\n", " OUTPUT_MODEL = 'Agnuxo/OpenCLAW-SEED-3B'\n", " BATCH_SIZE = 1\n", " EPOCHS = 1\n", " LEARNING_RATE = 2e-5\n", "\n", "print(f'\ud83c\udf31 Training Stage:')\n", "print(f' Base: {BASE_MODEL}')\n", "print(f' Output: {OUTPUT_MODEL}')\n", "print(f' LoRA r={LORA_R}, alpha={LORA_ALPHA}')\n", "print(f' Epochs: {EPOCHS}, BS: {BATCH_SIZE}, LR: {LEARNING_RATE}')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== LOAD AND PREPARE DATA =====\n", "from datasets import Dataset\n", "import json\n", "\n", "all_entries = []\n", "for f in os.listdir('seed_data'):\n", " if f.endswith('.jsonl'):\n", " with open(f'seed_data/{f}') as fp:\n", " for line in fp:\n", " try:\n", " entry = json.loads(line.strip())\n", " instruction = entry.get('instruction', '')\n", " inp = entry.get('input', '')\n", " output = entry.get('output', '')\n", " if instruction and output:\n", " text = f'### Instruction:\\n{instruction}'\n", " if inp:\n", " text += f'\\n\\n### Input:\\n{inp}'\n", " text += f'\\n\\n### Response:\\n{output}'\n", " all_entries.append({'text': text})\n", " except:\n", " continue\n", "\n", "import random\n", "random.shuffle(all_entries)\n", "\n", "# Split 90/10 train/eval\n", "split = max(1, int(len(all_entries) * 0.9))\n", "train_data = Dataset.from_list(all_entries[:split])\n", "eval_data = Dataset.from_list(all_entries[split:])\n", "\n", "print(f'\ud83d\udcca Train: {len(train_data)}, Eval: {len(eval_data)}')\n", "print(f'\ud83d\udcdd Sample:\\n{all_entries[0][\"text\"][:300]}...')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== LOAD MODEL =====\n", "import torch\n", "from transformers import AutoModelForCausalLM, AutoTokenizer\n", "\n", "print(f'\ud83d\udd27 Loading {BASE_MODEL}...')\n", "tokenizer = AutoTokenizer.from_pretrained(BASE_MODEL, trust_remote_code=True)\n", "if tokenizer.pad_token is None:\n", " tokenizer.pad_token = tokenizer.eos_token\n", "\n", "model = AutoModelForCausalLM.from_pretrained(\n", " BASE_MODEL,\n", " torch_dtype=torch.float16,\n", " device_map='auto',\n", " trust_remote_code=True,\n", ")\n", "\n", "total_params = sum(p.numel() for p in model.parameters())\n", "print(f'\u2705 Model loaded: {total_params:,} parameters')\n", "print(f' GPU: {torch.cuda.get_device_name(0) if torch.cuda.is_available() else \"CPU\"}')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== APPLY LoRA =====\n", "from peft import LoraConfig, get_peft_model\n", "\n", "lora_config = LoraConfig(\n", " r=LORA_R,\n", " lora_alpha=LORA_ALPHA,\n", " target_modules=['q_proj', 'k_proj', 'v_proj', 'o_proj', 'gate_proj', 'up_proj', 'down_proj'],\n", " lora_dropout=0.05,\n", " bias='none',\n", " task_type='CAUSAL_LM',\n", ")\n", "\n", "model = get_peft_model(model, lora_config)\n", "trainable = sum(p.numel() for p in model.parameters() if p.requires_grad)\n", "print(f'\ud83e\uddec LoRA applied: {trainable:,} trainable params ({100*trainable/total_params:.2f}%)')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== TRAIN =====\n", "from trl import SFTConfig, SFTTrainer\n", "\n", "training_args = SFTConfig(\n", " output_dir='./seed_checkpoints',\n", " num_train_epochs=EPOCHS,\n", " per_device_train_batch_size=BATCH_SIZE,\n", " per_device_eval_batch_size=BATCH_SIZE,\n", " gradient_accumulation_steps=4,\n", " learning_rate=LEARNING_RATE,\n", " weight_decay=0.01,\n", " warmup_ratio=0.1,\n", " lr_scheduler_type='cosine',\n", " logging_steps=10,\n", " eval_strategy='epoch',\n", " save_strategy='epoch',\n", " max_seq_length=MAX_SEQ_LEN,\n", " dataset_text_field='text',\n", " fp16=True,\n", " report_to='none',\n", " seed=42,\n", ")\n", "\n", "trainer = SFTTrainer(\n", " model=model,\n", " args=training_args,\n", " train_dataset=train_data,\n", " eval_dataset=eval_data,\n", " processing_class=tokenizer,\n", ")\n", "\n", "print('\ud83d\udd25 Training started!')\n", "result = trainer.train()\n", "print(f'\u2705 Training complete! Loss: {result.training_loss:.4f}')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== MERGE LoRA INTO BASE =====\n", "from peft import PeftModel\n", "\n", "print('\ud83d\udd17 Merging LoRA adapter into base model...')\n", "merged_model = model.merge_and_unload()\n", "\n", "# Save merged model locally\n", "merged_model.save_pretrained('./seed_merged')\n", "tokenizer.save_pretrained('./seed_merged')\n", "print('\u2705 Merged model saved locally')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== PUSH TO HUGGINGFACE HUB =====\n", "if HF_TOKEN:\n", " print(f'\u2601\ufe0f Pushing to {OUTPUT_MODEL}...')\n", " merged_model.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)\n", " tokenizer.push_to_hub(OUTPUT_MODEL, token=HF_TOKEN, private=False)\n", " print(f'\u2705 Model published: https://huggingface.co/{OUTPUT_MODEL}')\n", "else:\n", " print('\u26a0\ufe0f No HF_TOKEN \u2014 model saved locally only')" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== TEST THE TRAINED MODEL =====\n", "from transformers import pipeline\n", "\n", "gen = pipeline('text-generation', model=merged_model, tokenizer=tokenizer, max_new_tokens=150)\n", "\n", "test_prompts = [\n", " '### Instruction:\\nWhat is CHIMERA?\\n\\n### Response:\\n',\n", " '### Instruction:\\nExplain holographic neural networks.\\n\\n### Response:\\n',\n", " '### Instruction:\\nWhat is OpenCLAW?\\n\\n### Response:\\n',\n", "]\n", "\n", "print('='*60)\n", "print('\ud83e\uddea MODEL TEST RESULTS')\n", "print('='*60)\n", "for prompt in test_prompts:\n", " result = gen(prompt, do_sample=True, temperature=0.7)\n", " response = result[0]['generated_text'].split('### Response:\\n')[-1].strip()\n", " question = prompt.split('### Instruction:\\n')[1].split('\\n')[0]\n", " print(f'\\nQ: {question}')\n", " print(f'A: {response[:300]}')\n", " print('-'*40)" ], "outputs": [], "execution_count": null }, { "cell_type": "code", "metadata": {}, "source": [ "# ===== SAVE TRAINING REPORT =====\n", "import json\n", "from datetime import datetime, timezone\n", "\n", "report = {\n", " 'timestamp': datetime.now(timezone.utc).isoformat(),\n", " 'stage': 'GERMINATION',\n", " 'base_model': BASE_MODEL,\n", " 'output_model': OUTPUT_MODEL,\n", " 'final_loss': result.training_loss,\n", " 'training_entries': len(all_entries),\n", " 'total_params': total_params,\n", " 'trainable_params': trainable,\n", " 'lora_r': LORA_R,\n", " 'lora_alpha': LORA_ALPHA,\n", " 'epochs': EPOCHS,\n", "}\n", "\n", "with open('training_report.json', 'w') as f:\n", " json.dump(report, f, indent=2)\n", "\n", "# Upload report to dataset repo\n", "if HF_TOKEN:\n", " from huggingface_hub import HfApi\n", " api = HfApi(token=HF_TOKEN)\n", " api.upload_file(\n", " path_or_fileobj='training_report.json',\n", " path_in_repo='training_report.json',\n", " repo_id='Agnuxo/OpenCLAW-SEED-data',\n", " repo_type='dataset',\n", " )\n", " print('\u2705 Training report uploaded')\n", "\n", "print(f'\\n\ud83c\udf33 SEED GROWTH COMPLETE')\n", "print(f' Model: {OUTPUT_MODEL}')\n", "print(f' Loss: {result.training_loss:.4f}')\n", "print(f' Data: {len(all_entries)} entries')\n", "print(f' Next: Run this notebook again after more data is harvested!')" ], "outputs": [], "execution_count": null } ] }