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"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# CommitmentOS Checkpoint Evaluation (Colab)\n",
"\n",
"This notebook compares a base model against a locally saved LoRA-trained checkpoint on the CommitmentOS environment.\n",
"\n",
"It uses:\n",
"- `BASELINE_MODEL_NAME` from Hugging Face\n",
"- `TRAINED_MODEL_PATH` from disk in Colab\n",
"- the existing `evaluation/evaluate_llm_checkpoints.py` script\n",
"\n",
"By default the notebook evaluates against the hosted CommitmentOS environment on Hugging Face Space. An optional local-server cell is included below."
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "d43c692d",
"metadata": {},
"outputs": [],
"source": [
"!pip -q install --upgrade pip\n",
"!pip -q install transformers peft accelerate torch sentencepiece fastapi uvicorn requests python-dotenv pydantic \"openenv-core>=0.2.0\""
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!git clone https://github.com/Jayant2304/commitment_os.git\n",
"%cd commitment_os"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Configure Paths\n",
"\n",
"Set the base model ID and the local adapter/checkpoint path. Change `TRAINED_MODEL_PATH` to the folder you actually want to evaluate.\n",
"\n",
"If the base model is gated, set `HF_TOKEN` as well."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"\n",
"# Colab: load Hugging Face token from Secrets (key must be exactly HF_TOKEN)\n",
"try:\n",
" from google.colab import userdata\n",
"\n",
" os.environ[\"HF_TOKEN\"] = userdata.get(\"HF_TOKEN\")\n",
" print(\"HF_TOKEN loaded from Colab secrets\")\n",
"except ImportError:\n",
" print(\"Not on Colab; set HF_TOKEN in the shell or .env if downloads fail.\")\n",
"except Exception as exc:\n",
" print(\"Could not load HF_TOKEN from secrets:\", exc)\n",
"\n",
"os.environ[\"BASELINE_MODEL_NAME\"] = \"Qwen/Qwen2.5-1.5B-Instruct\"\n",
"os.environ[\"TRAINED_MODEL_PATH\"] = \"/content/commitment_os/training_output\"\n",
"os.environ[\"ENV_BASE_URL\"] = \"https://jayant2304-commitment-os.hf.space\"\n",
"\n",
"# Optional for gated base models:\n",
"# os.environ[\"HF_TOKEN\"] = \"hf_xxx\"\n",
"\n",
"# Optional eval overrides:\n",
"os.environ[\"EVAL_SEED\"] = \"42\"\n",
"os.environ[\"EVAL_MAX_STEPS\"] = \"12\"\n",
"os.environ[\"EVAL_TEMPERATURE\"] = \"0.0\"\n",
"os.environ[\"EVAL_TOP_P\"] = \"1.0\"\n",
"os.environ[\"EVAL_MAX_NEW_TOKENS\"] = \"256\"\n",
"os.environ[\"EVAL_SUCCESS_THRESHOLD\"] = \"0.6\"\n",
"\n",
"for key in [\n",
" \"BASELINE_MODEL_NAME\",\n",
" \"TRAINED_MODEL_PATH\",\n",
" \"ENV_BASE_URL\",\n",
" \"EVAL_SEED\",\n",
" \"EVAL_MAX_STEPS\",\n",
" \"EVAL_TEMPERATURE\",\n",
" \"EVAL_TOP_P\",\n",
" \"EVAL_MAX_NEW_TOKENS\",\n",
" \"EVAL_SUCCESS_THRESHOLD\",\n",
"]:\n",
" print(f\"{key}={os.environ[key]}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"trained_path = Path(os.environ[\"TRAINED_MODEL_PATH\"])\n",
"print(\"Checkpoint exists:\", trained_path.exists())\n",
"if trained_path.exists():\n",
" print(\"Checkpoint contents:\")\n",
" for item in sorted(trained_path.iterdir()):\n",
" print(\" -\", item.name)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Optional: Run CommitmentOS Locally Instead Of HF Space\n",
"\n",
"Only run this if you want evaluation against a local server inside Colab. Otherwise skip this section and keep `ENV_BASE_URL` pointed at the hosted Space."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Optional local server setup\n",
"# import os\n",
"# os.environ[\"ENV_BASE_URL\"] = \"http://127.0.0.1:7860\"\n",
"# !nohup python -m uvicorn server.app:app --host 0.0.0.0 --port 7860 >/tmp/commitmentos.log 2>&1 &\n",
"# !sleep 5\n",
"# !curl -s http://127.0.0.1:7860/health"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Run Checkpoint Comparison"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python evaluation/evaluate_llm_checkpoints.py"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"!python evaluation/plot_llm_checkpoints.py"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Inspect Artifacts"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import json\n",
"from pathlib import Path\n",
"\n",
"artifact_dir = Path(\"artifacts/evals_llm\")\n",
"print(sorted(p.name for p in artifact_dir.iterdir()))\n",
"\n",
"summary = json.loads((artifact_dir / \"llm_summary.json\").read_text())\n",
"summary"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"\n",
"pd.read_csv(\"artifacts/evals_llm/llm_comparison.csv\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"from IPython.display import SVG, display\n",
"\n",
"display(SVG(filename=\"artifacts/evals_llm/llm_reward_by_task.svg\"))\n",
"display(SVG(filename=\"artifacts/evals_llm/llm_violations_before_after.svg\"))"
]
},
{
"cell_type": "markdown",
"id": "9e8a35c5",
"metadata": {},
"source": [
"## Backup results (zip and download)\n",
"\n",
"Run after eval/plot finish. Large runs: copy `training_output` to Google Drive instead of browser download.\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4a5bcc7",
"metadata": {},
"outputs": [],
"source": [
"!cd /content/commitment_os && du -sh training_output artifacts/evals_llm 2>/dev/null || true\n",
"!cd /content/commitment_os && zip -r /content/commitment_os_bundle.zip training_output artifacts/evals_llm\n",
"from google.colab import files\n",
"\n",
"files.download(\"/content/commitment_os_bundle.zip\")\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python",
"version": "3.x"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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