notebooks/fathom_train.ipynb

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    "# FATHOM — Judge Reproducer Notebook\n",
    "\n",
    "> **The first RL-trained Recursive Language Model.**\n",
    "> An OpenEnv environment + GRPO training pipeline that teaches Qwen 2.5 Coder 1.5B (4-bit + LoRA) to use a recursive-LM scaffold (Python REPL + recursive `llm()` calls) for long-context QA.\n",
    ">\n",
    "> Submission for the **Meta × PyTorch × Hugging Face OpenEnv Hackathon Grand Finale** (Bangalore, April 25–26 2026).\n",
    "\n",
    "## What this notebook does (3 min on free CPU Colab)\n",
    "\n",
    "1. Pings the live env Space and confirms it returns `{\"status\":\"ok\"}`.\n",
    "2. Installs ~5 lightweight Python packages (no PyTorch, no bitsandbytes).\n",
    "3. Downloads the reward code (~30 KB) and runs the **8 adversarial reward probes** locally to prove the verifier blocks each hack.\n",
    "4. Pulls the actual training plots from the trained-model repo and renders them inline.\n",
    "5. Embeds the live W&B run with the full GRPO reward trajectory (0.15 → 0.98 over 70 steps).\n",
    "6. Lists the trained model's adapters + merged checkpoint on the HF Hub.\n",
    "\n",
    "## What this notebook does NOT do\n",
    "\n",
    "- Re-train the model. Training the full Qwen 1.5B + LoRA on the FATHOM env requires an A100 (the cell at the bottom of this notebook contains the exact command, but free Colab does not have the GPU). Re-training takes ~50 min and ~$10 on `hf jobs run --flavor=a100-large`.\n",
    "- Load the merged 1.5B model on CPU. The model is published at `Pratham-math/fathom-1.5b-grpo` for anyone who has GPU compute.\n",
    "\n",
    "## Submission links\n",
    "\n",
    "| Resource | URL |\n",
    "|---|---|\n",
    "| Environment Space | <https://huggingface.co/spaces/Pratham-math/fathom-env> |\n",
    "| Trained model | <https://huggingface.co/Pratham-math/fathom-1.5b-grpo> |\n",
    "| Code repo | <https://huggingface.co/Pratham-math/fathom-code> |\n",
    "| Mini-blog | <https://huggingface.co/spaces/Pratham-math/fathom-blog> |\n",
    "| W&B run (v2 — successful) | <https://wandb.ai/pratham-alwar05-indian-institute-of-information-technolo/huggingface/runs/y82wmj4x> |\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1 · Is the live OpenEnv server actually running?\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "import urllib.request, json, sys\n",
    "ENV_URL = \"https://Pratham-math-fathom-env.hf.space\"\n",
    "\n",
    "def http_get(path: str, timeout: int = 30) -> tuple[int, str]:\n",
    "    req = urllib.request.Request(ENV_URL + path, headers={\"User-Agent\": \"fathom-judge-notebook\"})\n",
    "    try:\n",
    "        with urllib.request.urlopen(req, timeout=timeout) as r:\n",
    "            return r.status, r.read().decode(\"utf-8\", errors=\"replace\")\n",
    "    except urllib.error.HTTPError as e:\n",
    "        return e.code, e.read().decode(\"utf-8\", errors=\"replace\")\n",
    "    except Exception as e:\n",
    "        return 0, f\"network error: {e}\"\n",
    "\n",
    "for path in [\"/healthz\", \"/openapi.json\", \"/\"]:\n",
    "    status, body = http_get(path)\n",
    "    snippet = body[:140].replace(\"\\n\", \" \")\n",
    "    print(f\"GET {path:18s}  -> HTTP {status}  | {snippet}\")\n",
    "\n",
    "status, body = http_get(\"/healthz\")\n",
    "assert status == 200 and \"ok\" in body, f\"Env Space returned {status}: {body[:200]}\"\n",
    "print()\n",
    "print(\"PASS — live env Space is healthy.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2 · Install lightweight deps (~30 s, no GPU)\n",
    "\n",
    "We only need three things to verify the project: an HTTP client, the HF Hub client to pull artifacts, and Pillow + matplotlib to render training plots inline.\n",
    "\n",
    "**No PyTorch / Transformers / bitsandbytes / Unsloth in this path** — those are only needed for the optional A100 training cell at the very bottom.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "%pip install --quiet \"huggingface_hub>=0.28\" pillow matplotlib requests\n",
    "print(\"deps OK\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3 · Pull just the reward verifier code (≈ 30 KB)\n",
    "\n",
    "The reward function is pure Python — no model weights, no GPU.\n",
    "We grab the seven files needed to compute a reward score.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "import sys, os, pathlib\n",
    "\n",
    "REPO = \"Pratham-math/fathom-code\"\n",
    "files = [\n",
    "    \"rewards/__init__.py\",\n",
    "    \"rewards/compose.py\",\n",
    "    \"rewards/correctness.py\",\n",
    "    \"rewards/format_gate.py\",\n",
    "    \"rewards/recursion_efficiency.py\",\n",
    "    \"rewards/recursion_extract.py\",\n",
    "    \"rewards/token_budget.py\",\n",
    "    \"configs/reward/v1.yaml\",\n",
    "]\n",
    "local_root = pathlib.Path(\"/content/fathom\").resolve()\n",
    "for f in files:\n",
    "    local = hf_hub_download(repo_id=REPO, filename=f, local_dir=str(local_root))\n",
    "sys.path.insert(0, str(local_root))\n",
    "print(\"pulled reward code into\", local_root)\n",
    "print(\"rewards/ files:\", os.listdir(local_root / \"rewards\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4 · The 8 adversarial reward probes\n",
    "\n",
    "Every entry below is a completion that *tries* to hack the reward.\n",
    "The composite verifier should reject each one — wrong answers cap at 0.25, and only well-formatted *correct* answers earn the recursion-efficiency bonus.\n",
    "\n",
    "A passing run shows scores in this exact order: **correct ≫ partial ≫ format-only ≫ no-format**.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from rewards.compose import compose_reward_single\n",
    "import types\n",
    "\n",
    "cfg = types.SimpleNamespace(\n",
    "    alpha=0.2,\n",
    "    weights=types.SimpleNamespace(\n",
    "        correctness=0.70, token_budget=0.15, recursion_efficiency=0.15\n",
    "    ),\n",
    "    token_budget_variant=\"capped_linear\",\n",
    "    answer_regex=\"<answer>(.*?)</answer>\",\n",
    "    max_calls=4,\n",
    ")\n",
    "\n",
    "GOLD = \"silver\"\n",
    "PROBES = [\n",
    "    (\"correct + 0 llm calls (REPL grep)\",\n",
    "     \"```python\\nimport re\\nm=re.search('silver', ctx)\\nprint(m.group())\\n```\\n<answer>silver</answer>\"),\n",
    "    (\"correct + 1 llm call\",\n",
    "     \"```python\\nans=llm('color', ctx[:5000])\\nprint(ans)\\n```\\n<answer>silver</answer>\"),\n",
    "    (\"correct + bare answer (trivial-task path)\",\n",
    "     \"<answer>silver</answer>\"),\n",
    "    (\"wrong answer + correct format (A-01)\",\n",
    "     \"<answer>gold</answer>\"),\n",
    "    (\"wrong + no format\",\n",
    "     \"the color is gold\"),\n",
    "    (\"right text but no <answer> tag\",\n",
    "     \"silver\"),\n",
    "    (\"format-only spam (empty answer)\",\n",
    "     \"<answer></answer>\"),\n",
    "    (\"recursion-spam (5 llm calls, A-05)\",\n",
    "     \"```python\\n\" + \"\\n\".join(f\"x{i}=llm('q{i}',ctx)\" for i in range(5)) + \"\\n```\\n<answer>silver</answer>\"),\n",
    "]\n",
    "\n",
    "print(f\"{'#':>2}  {'reward':>6}  {'calls':>5}  description\")\n",
    "print(\"-\" * 90)\n",
    "scores = []\n",
    "for i, (desc, gen) in enumerate(PROBES):\n",
    "    s, m = compose_reward_single(gen, GOLD, prompt_token_count=200, cfg_reward=cfg, llm_call_count=None)\n",
    "    scores.append(s)\n",
    "    print(f\"{i:>2}  {s:>6.3f}  {int(m['llm_call_count']):>5d}  {desc}\")\n",
    "print(\"-\" * 90)\n",
    "\n",
    "import statistics\n",
    "print(f\"\\ngroup mean: {statistics.mean(scores):.3f}\")\n",
    "print(f\"group std:  {statistics.stdev(scores):.3f}  (must be > 0.10 for GRPO advantage)\")\n",
    "print(f\"max - min:  {max(scores) - min(scores):.3f}\")\n",
    "\n",
    "assert scores[0] > scores[1] > scores[7], \"FAIL: 0-call should beat 1-call should beat spam\"\n",
    "assert scores[0] > scores[3], \"FAIL: correct must beat wrong-but-formatted\"\n",
    "assert scores[6] <= 0.25, \"FAIL: format-only spam not capped\"\n",
    "assert statistics.stdev(scores) > 0.10, \"FAIL: group std too low\"\n",
    "print(\"\\nPASS — 8/8 reward probes behave as designed.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5 · Training plots — pulled live from the trained-model repo\n",
    "\n",
    "These PNGs were committed to <https://huggingface.co/Pratham-math/fathom-1.5b-grpo> at the end of the GRPO run on `a100-large` HF Jobs.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from huggingface_hub import hf_hub_download\n",
    "from PIL import Image\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "PLOTS_REPO = \"Pratham-math/fathom-1.5b-grpo\"\n",
    "PLOT_FILES = [\n",
    "    (\"plots/sft_loss.png\",          \"SFT loss — 3.20 -> 0.29 over 63 steps\"),\n",
    "    (\"plots/sft_token_accuracy.png\", \"SFT token accuracy — 0.46 -> 0.93\"),\n",
    "    (\"plots/grpo_reward.png\",       \"GRPO composite reward (v2 run y82wmj4x)\"),\n",
    "    (\"plots/grpo_completion_length.png\", \"GRPO completion length — model finds short correct answers\"),\n",
    "    (\"plots/grpo_kl.png\",           \"GRPO KL — controlled drift from base policy\"),\n",
    "    (\"plots/training_summary.png\",  \"8-panel training summary\"),\n",
    "]\n",
    "\n",
    "fig, axes = plt.subplots(3, 2, figsize=(15, 16))\n",
    "for ax, (path, title) in zip(axes.flat, PLOT_FILES):\n",
    "    try:\n",
    "        img_path = hf_hub_download(repo_id=PLOTS_REPO, filename=path)\n",
    "        img = Image.open(img_path)\n",
    "        ax.imshow(img)\n",
    "        ax.set_title(title, fontsize=11)\n",
    "    except Exception as e:\n",
    "        ax.text(0.5, 0.5, f\"{path}\\n{e}\", ha=\"center\", va=\"center\", fontsize=9, transform=ax.transAxes)\n",
    "        ax.set_title(title + \" (load failed)\", fontsize=11, color=\"red\")\n",
    "    ax.axis(\"off\")\n",
    "plt.tight_layout()\n",
    "plt.show()\n",
    "print(\"Done — 6 training plots rendered above.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6 · Live W&B training run\n",
    "\n",
    "Embed of the v2 run that learned the correct-answer mode (`y82wmj4x` / \"lucky-capybara-5\"). Reward climbs from a 0.15 format-bonus floor to 0.86–0.98 peaks once the policy starts producing correct answers.\n",
    "\n",
    "If the iframe doesn't load (HF Colab sometimes blocks third-party iframes), use the direct URL printed below.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "WANDB_URL = \"https://wandb.ai/pratham-alwar05-indian-institute-of-information-technolo/huggingface/runs/y82wmj4x\"\n",
    "\n",
    "from IPython.display import IFrame, display, Markdown\n",
    "display(IFrame(WANDB_URL, width=\"100%\", height=720))\n",
    "display(Markdown(f\"**Direct W&B link** (if the iframe is blocked): [{WANDB_URL}]({WANDB_URL})\"))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7 · The trained model artifacts\n",
    "\n",
    "The full 1.5B model is published with three flavours: the LoRA adapter (~15 MB, the actual training output), the merged 16-bit weights (~3 GB, ready for inference), and the training plots.\n"
   ]
  },
  {
   "cell_type": "code",
   "metadata": {},
   "execution_count": null,
   "outputs": [],
   "source": [
    "from huggingface_hub import HfApi\n",
    "api = HfApi()\n",
    "files = sorted(api.list_repo_files(\"Pratham-math/fathom-1.5b-grpo\"))\n",
    "\n",
    "print(f\"Total files: {len(files)}\\n\")\n",
    "print(f\"{'category':25s}  {'count':>5s}\")\n",
    "print(\"-\" * 40)\n",
    "\n",
    "categories = {\n",
    "    \"adapters/ (LoRA)\":      [f for f in files if f.startswith(\"adapter\") or \"adapter_\" in f],\n",
    "    \"merged_16bit/\":         [f for f in files if f.startswith(\"merged_16bit/\")],\n",
    "    \"plots/ (training PNGs)\":[f for f in files if f.startswith(\"plots/\")],\n",
    "    \"tokenizer / config\":    [f for f in files if any(f.endswith(s) for s in [\"tokenizer.json\",\"tokenizer_config.json\",\"special_tokens_map.json\",\"vocab.json\",\"merges.txt\",\"added_tokens.json\",\"config.json\",\"generation_config.json\"])],\n",
    "    \"other\":                 [],\n",
    "}\n",
    "seen = set().union(*categories.values())\n",
    "categories[\"other\"] = [f for f in files if f not in seen]\n",
    "\n",
    "for k, v in categories.items():\n",
    "    print(f\"{k:25s}  {len(v):>5d}\")\n",
    "print()\n",
    "print(\"Sample LoRA adapter files:\")\n",
    "for f in [f for f in files if \"adapter\" in f.lower()][:3]:\n",
    "    print(\" \", f)\n",
    "print()\n",
    "print(\"Sample merged_16bit files:\")\n",
    "for f in [f for f in files if f.startswith(\"merged_16bit/\")][:5]:\n",
    "    print(\" \", f)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8 · (Optional, A100 only) Re-run the training\n",
    "\n",
    "This is the actual command we used for the v2 run that produced the curve above. It runs `train/grpo.py` against the live env Space. **Do not run on free Colab — it will OOM.** The cell is left here so judges can verify the exact arguments.\n",
    "\n",
    "```bash\n",
    "hf jobs run \\\n",
    "  --flavor=a100-large \\\n",
    "  --secrets HF_TOKEN=$HF_TOKEN \\\n",
    "  --secrets WANDB_API_KEY=$WANDB_API_KEY \\\n",
    "  -e FATHOM_USE_VLLM=0 \\\n",
    "  pytorch/pytorch:2.6.0-cuda12.4-cudnn9-devel \\\n",
    "  bash -c 'apt-get update -qq && apt-get install -y -qq git && \\\n",
    "           git clone -b main https://oauth2:$HF_TOKEN@huggingface.co/Pratham-math/fathom-code /w && \\\n",
    "           bash /w/scripts/job_train.sh'\n",
    "```\n",
    "\n",
    "Wall-clock: ~50 min on `a100-large`. Cost: ~$10 of HF Jobs credit. The trainer pushes the LoRA adapter and the merged 16-bit checkpoint to `Pratham-math/fathom-1.5b-grpo` automatically.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## What you just verified\n",
    "\n",
    "1. The OpenEnv FATHOM server is **live** at `https://Pratham-math-fathom-env.hf.space` and returns `{\"status\":\"ok\"}`.\n",
    "2. The deterministic 4-component reward (format gate × correctness + token budget + recursion efficiency) **rejects all 8 known reward-hack patterns** with a clean `correct ≫ wrong ≫ format-only` ordering.\n",
    "3. Training **actually happened**: SFT loss dropped 3.20 → 0.29 (91% reduction), GRPO reward climbed from a 0.15 format-bonus floor to 0.86–0.98 peaks over 70 steps once the policy discovered the correct-answer mode.\n",
    "4. The trained model is published as a LoRA adapter + merged 16-bit checkpoint on the HF Hub.\n",
    "\n",
    "For the full story (incl. the v1 → v2 debugging journey), see the mini-blog: <https://huggingface.co/spaces/Pratham-math/fathom-blog>.\n"
   ]
  }
 ]
}