File size: 8,120 Bytes
b0fbec3 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 | {
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# ForgeEnv: Training Notebook\n",
"\n",
"Self-improving RL environment for HuggingFace ecosystem repair under library drift.\n",
"Trains a **Repair Agent** (and optionally a co-evolving **Drift Generator**)\n",
"on top of `unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit` using **TRL GRPO + Unsloth**.\n",
"\n",
"Pipeline:\n",
"1. Install dependencies (Unsloth, TRL, ForgeEnv).\n",
"2. Generate warm-start pairs.\n",
"3. SFT warm-start the Repair Agent (200 steps).\n",
"4. GRPO main training (200 episodes).\n",
"5. Evaluate baseline vs trained, save plots and adapter to Hugging Face Hub.\n",
"\n",
"**Hardware**: T4 / L4 / A100 (4-bit QLoRA). Designed for ~1 hr on A100, ~3 hrs on T4."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 1. Install dependencies"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"%%capture\n",
"!pip install -q unsloth==2024.4 trl>=0.10.0 peft>=0.10.0 accelerate>=0.30.0 datasets>=2.18.0\n",
"!pip install -q openenv-core>=0.2.0 nltk>=3.8.0 scikit-learn>=1.4.0 matplotlib>=3.8.0 wandb>=0.16.0 huggingface_hub>=0.23.0\n",
"!pip install -q -e ."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os, json, torch\n",
"from pathlib import Path\n",
"\n",
"HF_USERNAME = 'akhiilll'\n",
"HF_TOKEN = os.environ.get('HF_TOKEN', '') # set this in Colab Secrets\n",
"MODEL_REPO = f'{HF_USERNAME}/forgeenv-repair-agent'\n",
"BASE_MODEL = 'unsloth/Qwen2.5-Coder-7B-Instruct-bnb-4bit'\n",
"\n",
"from huggingface_hub import login\n",
"if HF_TOKEN:\n",
" login(token=HF_TOKEN)\n",
"print('Torch:', torch.__version__, 'CUDA:', torch.cuda.is_available())"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 2. Generate warm-start pairs"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python warmstart/generate_pairs.py --target_count 64 --out_dir warmstart/data\n",
"!head -1 warmstart/data/repair_pairs.jsonl | python -m json.tool"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 3. SFT warm-start (Repair Agent)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from forgeenv.training.sft_warmstart import run_sft\n",
"\n",
"run_sft(\n",
" role='repair_agent',\n",
" data_path='warmstart/data/repair_pairs.jsonl',\n",
" output_dir='artifacts/checkpoints/repair_agent_sft',\n",
" base_model=BASE_MODEL,\n",
" max_steps=200,\n",
" batch_size=2,\n",
" learning_rate=2e-4,\n",
" lora_r=16,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 4. GRPO main training (Repair Agent)\n",
"\n",
"200 episodes against the live ForgeEnvironment. Logs reward at every 5 steps."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from forgeenv.training.grpo_repair import run_grpo\n",
"\n",
"run_grpo(\n",
" base_model=BASE_MODEL,\n",
" adapter_path='artifacts/checkpoints/repair_agent_sft',\n",
" output_dir='artifacts/checkpoints/repair_agent_grpo',\n",
" total_episodes=200,\n",
" group_size=4,\n",
" learning_rate=5e-6,\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 5. Baseline vs trained eval (50 episodes each)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from forgeenv.env.forge_environment import ForgeEnvironment\n",
"from forgeenv.training.rollout import rollout_one_episode\n",
"\n",
"def run_eval(generate_fn, n_episodes=50, label=''):\n",
" rewards = []\n",
" successes = 0\n",
" for i in range(n_episodes):\n",
" env = ForgeEnvironment(seed=42 + i)\n",
" result = rollout_one_episode(env, repair_generate=generate_fn)\n",
" rewards.append(result.visible_reward)\n",
" successes += int(result.success)\n",
" return {\n",
" 'label': label,\n",
" 'mean_reward': sum(rewards) / len(rewards),\n",
" 'success_rate': successes / n_episodes,\n",
" 'rewards': rewards,\n",
" }\n",
"\n",
"from forgeenv.training.rollout import _baseline_repair_generate\n",
"baseline_result = run_eval(_baseline_repair_generate(), n_episodes=50, label='baseline (no-op)')\n",
"print(json.dumps({k: v for k, v in baseline_result.items() if k != 'rewards'}, indent=2))"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Load the trained adapter and eval\n",
"from unsloth import FastLanguageModel\n",
"from peft import PeftModel\n",
"\n",
"model, tokenizer = FastLanguageModel.from_pretrained(\n",
" model_name=BASE_MODEL, max_seq_length=4096, dtype=None, load_in_4bit=True\n",
")\n",
"model = PeftModel.from_pretrained(model, 'artifacts/checkpoints/repair_agent_grpo')\n",
"model = FastLanguageModel.for_inference(model)\n",
"\n",
"def trained_generate(system, user):\n",
" msgs = [{'role':'system','content':system},{'role':'user','content':user}]\n",
" inputs = tokenizer.apply_chat_template(msgs, return_tensors='pt', add_generation_prompt=True).to(model.device)\n",
" out = model.generate(inputs, max_new_tokens=512, do_sample=True, temperature=0.7, top_p=0.95)\n",
" return tokenizer.decode(out[0][inputs.shape[1]:], skip_special_tokens=True)\n",
"\n",
"trained_result = run_eval(trained_generate, n_episodes=50, label='trained (GRPO)')\n",
"print(json.dumps({k: v for k, v in trained_result.items() if k != 'rewards'}, indent=2))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 6. Save plots + push to Hugging Face Hub"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from forgeenv.training.plots import (\n",
" plot_reward_curve, plot_success_rate_by_category, plot_baseline_vs_trained\n",
")\n",
"\n",
"Path('artifacts/plots').mkdir(parents=True, exist_ok=True)\n",
"plot_baseline_vs_trained(\n",
" baseline_rewards=baseline_result['rewards'],\n",
" trained_rewards=trained_result['rewards'],\n",
" out_path='artifacts/plots/baseline_vs_trained.png',\n",
")\n",
"plot_reward_curve(\n",
" rewards=trained_result['rewards'],\n",
" out_path='artifacts/plots/training_reward_curve.png',\n",
")\n",
"print('Plots written.')"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from huggingface_hub import HfApi\n",
"\n",
"api = HfApi(token=HF_TOKEN)\n",
"api.create_repo(MODEL_REPO, exist_ok=True, private=False)\n",
"model.push_to_hub(MODEL_REPO, token=HF_TOKEN)\n",
"tokenizer.push_to_hub(MODEL_REPO, token=HF_TOKEN)\n",
"api.upload_folder(folder_path='artifacts/plots', repo_id=MODEL_REPO, path_in_repo='plots')\n",
"print(f'Pushed to https://huggingface.co/{MODEL_REPO}')"
]
}
],
"metadata": {
"kernelspec": {"display_name": "Python 3", "language": "python", "name": "python3"},
"language_info": {"name": "python", "version": "3.10"}
},
"nbformat": 4,
"nbformat_minor": 4
}
|