diff --git "a/training/train_grpo.ipynb" "b/training/train_grpo.ipynb"
--- "a/training/train_grpo.ipynb"
+++ "b/training/train_grpo.ipynb"
@@ -25,18 +25,11 @@
     },
     {
       "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "# Cell 1: Install dependencies (quote versions — zsh treats `>` as redirect otherwise)\n",
-        "!pip install -q torch torchvision torchaudio\n",
-        "!pip install -q \"transformers>=4.45.0\" \"accelerate\" \"peft>=0.10.0\" \"trl>=0.20.0\" \"datasets\" \"bitsandbytes\"\n",
-        "!pip install -q matplotlib pandas\n",
-        "!pip install -q \"typing_extensions>=4.13.0\" pydantic httpx\n",
-        "!pip install -q \"openenv-core[core]>=0.2.2\""
-      ],
       "execution_count": 1,
+      "metadata": {},
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "\n",
@@ -56,11 +49,34 @@
             "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n"
           ]
         }
+      ],
+      "source": [
+        "# Cell 1: Install dependencies (quote versions — zsh treats `>` as redirect otherwise)\n",
+        "!pip install -q torch torchvision torchaudio\n",
+        "!pip install -q \"transformers>=4.45.0\" \"accelerate\" \"peft>=0.10.0\" \"trl>=0.20.0\" \"datasets\" \"bitsandbytes\"\n",
+        "!pip install -q matplotlib pandas\n",
+        "!pip install -q \"typing_extensions>=4.13.0\" pydantic httpx\n",
+        "!pip install -q \"openenv-core[core]>=0.2.2\""
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 2,
       "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Mode: local\n",
+            "Repo root: /Users/anurag.c/viral-posts-env\n",
+            "Working dir: /Users/anurag.c/viral-posts-env\n",
+            "Branch: hack1\n",
+            "Commit: aedc9c7\n",
+            "Plots dir: /Users/anurag.c/viral-posts-env/plots\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 2: Resolve repo path (Colab: fresh clone. Local: auto-detect project root)\n",
         "import os\n",
@@ -136,25 +152,30 @@
         "print(f\"Branch: {REPO_BRANCH}\")\n",
         "print(f\"Commit: {commit}\")\n",
         "print(f\"Plots dir: {PLOTS_DIR}\")"
-      ],
-      "execution_count": 2,
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 3,
+      "metadata": {},
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
-            "Mode: local\n",
-            "Repo root: /Users/anurag.c/viral-posts-env\n",
-            "Working dir: /Users/anurag.c/viral-posts-env\n",
-            "Branch: hack1\n",
-            "Commit: aedc9c7\n",
-            "Plots dir: /Users/anurag.c/viral-posts-env/plots\n"
+            "/Users/anurag.c/viral-posts-env/.venv/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+            "  from .autonotebook import tqdm as notebook_tqdm\n"
+          ]
+        },
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "GPU: CPU\n",
+            "Tags: 114, Topics: 100, Horizon: 30 days\n"
           ]
         }
-      ]
-    },
-    {
-      "cell_type": "code",
-      "metadata": {},
+      ],
       "source": [
         "# Cell 3: Imports (with runtime validation)\n",
         "import json, random, time, textwrap, copy, os, sys\n",
@@ -202,23 +223,6 @@
         "import ast\n",
         "ast.parse(\"def _t(x: int) -> str: return f'{x}'\")\n",
         "print(\"OK: ast.parse (syntax check)\")"
-      ],
-      "execution_count": 3,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "/Users/anurag.c/viral-posts-env/.venv/lib/python3.14/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
-            "  from .autonotebook import tqdm as notebook_tqdm\n"
-          ]
-        },
-        {
-          "output_type": "stream",
-          "text": [
-            "GPU: CPU\n",
-            "Tags: 114, Topics: 100, Horizon: 30 days\n"
-          ]
-        }
       ]
     },
     {
@@ -232,7 +236,17 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 4,
       "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Agents and episode runner defined.\n"
+          ]
+        }
+      ],
       "source": [
         "# Cell 4: Define heuristic agents + episode runner\n",
         "_rng = random.Random(42)\n",
@@ -308,58 +322,15 @@
         "            \"rewards\": rewards, \"energies\": energies}\n",
         "\n",
         "print(\"Agents and episode runner defined.\")"
-      ],
-      "execution_count": 4,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Agents and episode runner defined.\n"
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
-      "metadata": {},
-      "source": [
-        "# Cell 5: Run baselines (safe)\n",
-        "print(\"Running heuristic baselines (5 agents × 3 tasks)...\")\n",
-        "print(\"=\" * 70)\n",
-        "\n",
-        "required = [\"BASELINE_AGENTS\", \"run_episode\", \"TASKS\", \"random\"]\n",
-        "missing = [k for k in required if k not in globals()]\n",
-        "if missing:\n",
-        "    raise RuntimeError(\n",
-        "        f\"Missing prerequisites: {missing}. Run notebook from top (Cell 1 -> Cell 5).\"\n",
-        "    )\n",
-        "\n",
-        "baseline_results = {}\n",
-        "for name, fn in BASELINE_AGENTS.items():\n",
-        "    baseline_results[name] = {}\n",
-        "    for task in TASKS:\n",
-        "        _rng = random.Random(42)\n",
-        "        try:\n",
-        "            result = run_episode(task, fn, seed=42)\n",
-        "        except Exception as e:\n",
-        "            raise RuntimeError(\n",
-        "                f\"Baseline failed for agent={name}, task={task}: {type(e).__name__}: {e}\"\n",
-        "            ) from e\n",
-        "        baseline_results[name][task] = result\n",
-        "        print(f\"  {name:>12s} | {task:>22s} | score={result['grader_score']:.4f} \"\n",
-        "              f\"| energy={result['final_energy']:.2f}\")\n",
-        "    print()\n",
-        "\n",
-        "print(\"\\nLEADERBOARD\")\n",
-        "print(f\"{'Agent':<14s} {'Engage':>10s} {'Strategic':>12s} {'Competitive':>14s} {'Avg':>8s}\")\n",
-        "print(\"-\" * 60)\n",
-        "for name in BASELINE_AGENTS:\n",
-        "    scores = [baseline_results[name][t][\"grader_score\"] for t in TASKS]\n",
-        "    print(f\"{name:<14s} {scores[0]:>10.4f} {scores[1]:>12.4f} {scores[2]:>14.4f} {sum(scores)/3:>8.4f}\")"
-      ],
       "execution_count": 5,
+      "metadata": {},
       "outputs": [
         {
+          "name": "stdout",
           "output_type": "stream",
           "text": [
             "Running heuristic baselines (5 agents × 3 tasks)...\n",
@@ -395,11 +366,59 @@
             "smart              0.7883       0.8932         0.8986   0.8600\n"
           ]
         }
+      ],
+      "source": [
+        "# Cell 5: Run baselines (safe)\n",
+        "print(\"Running heuristic baselines (5 agents × 3 tasks)...\")\n",
+        "print(\"=\" * 70)\n",
+        "\n",
+        "required = [\"BASELINE_AGENTS\", \"run_episode\", \"TASKS\", \"random\"]\n",
+        "missing = [k for k in required if k not in globals()]\n",
+        "if missing:\n",
+        "    raise RuntimeError(\n",
+        "        f\"Missing prerequisites: {missing}. Run notebook from top (Cell 1 -> Cell 5).\"\n",
+        "    )\n",
+        "\n",
+        "baseline_results = {}\n",
+        "for name, fn in BASELINE_AGENTS.items():\n",
+        "    baseline_results[name] = {}\n",
+        "    for task in TASKS:\n",
+        "        _rng = random.Random(42)\n",
+        "        try:\n",
+        "            result = run_episode(task, fn, seed=42)\n",
+        "        except Exception as e:\n",
+        "            raise RuntimeError(\n",
+        "                f\"Baseline failed for agent={name}, task={task}: {type(e).__name__}: {e}\"\n",
+        "            ) from e\n",
+        "        baseline_results[name][task] = result\n",
+        "        print(f\"  {name:>12s} | {task:>22s} | score={result['grader_score']:.4f} \"\n",
+        "              f\"| energy={result['final_energy']:.2f}\")\n",
+        "    print()\n",
+        "\n",
+        "print(\"\\nLEADERBOARD\")\n",
+        "print(f\"{'Agent':<14s} {'Engage':>10s} {'Strategic':>12s} {'Competitive':>14s} {'Avg':>8s}\")\n",
+        "print(\"-\" * 60)\n",
+        "for name in BASELINE_AGENTS:\n",
+        "    scores = [baseline_results[name][t][\"grader_score\"] for t in TASKS]\n",
+        "    print(f\"{name:<14s} {scores[0]:>10.4f} {scores[1]:>12.4f} {scores[2]:>14.4f} {sum(scores)/3:>8.4f}\")"
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": 6,
       "metadata": {},
+      "outputs": [
+        {
+          "data": {
+            "image/png": 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+            "text/plain": [
+              "<Figure size 1600x500 with 3 Axes>"
+            ]
+          },
+          "metadata": {},
+          "output_type": "display_data"
+        }
+      ],
       "source": [
         "# Cell 6: Baseline plots\n",
         "fig, axes = plt.subplots(1, 3, figsize=(16, 5), sharey=True)\n",
@@ -417,18 +436,6 @@
         "fig.tight_layout()\n",
         "fig.savefig(f\"{PLOTS_DIR}/baseline_leaderboard.png\", dpi=150, bbox_inches='tight')\n",
         "plt.show()"
-      ],
-      "execution_count": 6,
-      "outputs": [
-        {
-          "output_type": "display_data",
-          "data": {
-            "image/png": 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-            "text/plain": [
-              "<Figure size 1600x500 with 3 Axes>"
-            ]
-          }
-        }
       ]
     },
     {
@@ -442,7 +449,41 @@
     },
     {
       "cell_type": "code",
+      "execution_count": 7,
       "metadata": {},
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "Loading Qwen/Qwen2.5-1.5B-Instruct without 4-bit (bitsandbytes/CUDA unavailable).\n",
+            "  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\n",
+            "  On Mac: use fp16 on MPS or fp32 on CPU.\n"
+          ]
+        },
+        {
+          "ename": "KeyboardInterrupt",
+          "evalue": "",
+          "output_type": "error",
+          "traceback": [
+            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
+            "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
+            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 44\u001b[39m\n\u001b[32m     40\u001b[39m         \u001b[33m\"  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\\n\"\u001b[39m\n\u001b[32m     41\u001b[39m         \u001b[33m\"  On Mac: use fp16 on MPS or fp32 on CPU.\"\u001b[39m\n\u001b[32m     42\u001b[39m     )\n\u001b[32m     43\u001b[39m     dtype = torch.float16 \u001b[38;5;28;01mif\u001b[39;00m (torch.cuda.is_available() \u001b[38;5;28;01mor\u001b[39;00m getattr(torch.backends, \u001b[33m\"mps\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;28;01mand\u001b[39;00m torch.backends.mps.is_available()) \u001b[38;5;28;01melse\u001b[39;00m torch.float32\n\u001b[32m---> \u001b[39m\u001b[32m44\u001b[39m     model = AutoModelForCausalLM.from_pretrained(\n\u001b[32m     45\u001b[39m         MODEL_NAME,\n\u001b[32m     46\u001b[39m         trust_remote_code=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m     47\u001b[39m         dtype=dtype,\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py:394\u001b[39m, in \u001b[36m_BaseAutoModelClass.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[39m\n\u001b[32m    392\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(parent_config, \u001b[33m\"\u001b[39m\u001b[33mquantization_config\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m    393\u001b[39m             config.quantization_config = parent_config.quantization_config\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mmodel_class\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfrom_pretrained\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    395\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mmodel_args\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mhub_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m    396\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    397\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    398\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig.\u001b[34m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    399\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m.join(c.\u001b[34m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m._model_mapping)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    400\u001b[39m )\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:4118\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, fusion_config, disable_mmap, *model_args, **kwargs)\u001b[39m\n\u001b[32m   4113\u001b[39m     logger.warning_once(\n\u001b[32m   4114\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mA kernel_config was provided but use_kernels is False; setting use_kernels=True automatically. To suppress this warning, explicitly set use_kernels to True.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   4115\u001b[39m     )\n\u001b[32m   4116\u001b[39m     use_kernels = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4118\u001b[39m checkpoint_files, sharded_metadata = \u001b[30;43m_get_resolved_checkpoint_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   4119\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4120\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4121\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4122\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4123\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdownload_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdownload_kwargs_with_commit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4124\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4125\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4126\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtransformers_explicit_filename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgetattr\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mtransformers_weights\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4127\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4128\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   4130\u001b[39m is_quantized = hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   4132\u001b[39m \u001b[38;5;66;03m# Find the correct dtype based on current state\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:660\u001b[39m, in \u001b[36m_get_resolved_checkpoint_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, variant, gguf_file, use_safetensors, user_agent, is_remote_code, transformers_explicit_filename, download_kwargs, tqdm_class)\u001b[39m\n\u001b[32m    648\u001b[39m can_auto_convert = (\n\u001b[32m    649\u001b[39m     \u001b[38;5;129;01mnot\u001b[39;00m is_offline_mode()  \u001b[38;5;66;03m# for obvious reasons\u001b[39;00m\n\u001b[32m    650\u001b[39m     \u001b[38;5;66;03m# If we are in a CI environment or in a pytest run, we prevent the conversion\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    653\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m subfolder == \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# converter bot does not work on subfolders\u001b[39;00m\n\u001b[32m    654\u001b[39m )\n\u001b[32m    656\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    657\u001b[39m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[32m    658\u001b[39m     \u001b[38;5;66;03m# Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None\u001b[39;00m\n\u001b[32m    659\u001b[39m     \u001b[38;5;66;03m# result when internet is up, the repo and revision exist, but the file does not.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m660\u001b[39m     resolved_archive_file = \u001b[30;43mcached_file\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mcached_file_kwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    662\u001b[39m     \u001b[38;5;66;03m# Try safetensors files first if not already found\u001b[39;00m\n\u001b[32m    663\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m resolved_archive_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m filename == _add_variant(SAFE_WEIGHTS_NAME, variant):\n\u001b[32m    664\u001b[39m         \u001b[38;5;66;03m# Maybe the checkpoint is sharded, we try to grab the index name in this case.\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:278\u001b[39m, in \u001b[36mcached_file\u001b[39m\u001b[34m(path_or_repo_id, filename, **kwargs)\u001b[39m\n\u001b[32m    223\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcached_file\u001b[39m(\n\u001b[32m    224\u001b[39m     path_or_repo_id: \u001b[38;5;28mstr\u001b[39m | os.PathLike,\n\u001b[32m    225\u001b[39m     filename: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m    226\u001b[39m     **kwargs,\n\u001b[32m    227\u001b[39m ) -> \u001b[38;5;28mstr\u001b[39m | \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    228\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    229\u001b[39m \u001b[33;03m    Tries to locate a file in a local folder and repo, downloads and cache it if necessary.\u001b[39;00m\n\u001b[32m    230\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    276\u001b[39m \u001b[33;03m    ```\u001b[39;00m\n\u001b[32m    277\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m     file = \u001b[30;43mcached_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    279\u001b[39m     file = file[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m file\n\u001b[32m    280\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m file\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:422\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, cache_dir, force_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, tqdm_class, **deprecated_kwargs)\u001b[39m\n\u001b[32m    419\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    420\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(full_filenames) == \u001b[32m1\u001b[39m:\n\u001b[32m    421\u001b[39m         \u001b[38;5;66;03m# This is slightly better for only 1 file\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m422\u001b[39m         \u001b[30;43mhf_hub_download\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    423\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    424\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    425\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m==\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    426\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    427\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    428\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    429\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    430\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    431\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    432\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    433\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    434\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    435\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    436\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    437\u001b[39m         snapshot_download(\n\u001b[32m    438\u001b[39m             path_or_repo_id,\n\u001b[32m    439\u001b[39m             allow_patterns=full_filenames,\n\u001b[32m   (...)\u001b[39m\u001b[32m    448\u001b[39m             tqdm_class=tqdm_class,\n\u001b[32m    449\u001b[39m         )\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py:88\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     84\u001b[39m         validate_repo_id(arg_value)\n\u001b[32m     86\u001b[39m kwargs = smoothly_deprecate_legacy_arguments(fn_name=fn.\u001b[34m__name__\u001b[39m, kwargs=kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:995\u001b[39m, in \u001b[36mhf_hub_download\u001b[39m\u001b[34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, etag_timeout, token, local_files_only, headers, endpoint, tqdm_class, dry_run)\u001b[39m\n\u001b[32m    974\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_local_dir(\n\u001b[32m    975\u001b[39m         \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[32m    976\u001b[39m         local_dir=local_dir,\n\u001b[32m   (...)\u001b[39m\u001b[32m    992\u001b[39m         dry_run=dry_run,\n\u001b[32m    993\u001b[39m     )\n\u001b[32m    994\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m995\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_hf_hub_download_to_cache_dir\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    996\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Destination\u001b[39;49;00m\n\u001b[32m    997\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    998\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# File info\u001b[39;49;00m\n\u001b[32m    999\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1000\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1001\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1002\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1003\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# HTTP info\u001b[39;49;00m\n\u001b[32m   1004\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1005\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1006\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mhf_headers\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1007\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1008\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Additional options\u001b[39;49;00m\n\u001b[32m   1009\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1010\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1011\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1012\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1013\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:1213\u001b[39m, in \u001b[36m_hf_hub_download_to_cache_dir\u001b[39m\u001b[34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, token, local_files_only, force_download, tqdm_class, dry_run)\u001b[39m\n\u001b[32m   1209\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m pointer_path\n\u001b[32m   1211\u001b[39m \u001b[38;5;66;03m# Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1213\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43;01mwith\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mWeakFileLock\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlock_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m   1214\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m_download_to_tmp_and_move\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1215\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mincomplete_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m+\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m.incomplete\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1216\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdestination_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1224\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1225\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1226\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mnot\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mos\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mexists\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpointer_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.2_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/contextlib.py:141\u001b[39m, in \u001b[36m_GeneratorContextManager.__enter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    139\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.kwds, \u001b[38;5;28mself\u001b[39m.func\n\u001b[32m    140\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m141\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mnext\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgen\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    142\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m    143\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mgenerator didn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt yield\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_fixes.py:99\u001b[39m, in \u001b[36mWeakFileLock\u001b[39m\u001b[34m(lock_file, timeout)\u001b[39m\n\u001b[32m     96\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m Timeout(\u001b[38;5;28mstr\u001b[39m(lock_file))\n\u001b[32m     98\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m99\u001b[39m     \u001b[30;43mlock\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43macquire\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmin\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m-\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43melapsed_time\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    100\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Timeout:\n\u001b[32m    101\u001b[39m     logger.info(\n\u001b[32m    102\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStill waiting to acquire lock on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlock_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (elapsed: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime.time()\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m seconds)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    103\u001b[39m     )\n",
+            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/filelock/_api.py:513\u001b[39m, in \u001b[36mBaseFileLock.acquire\u001b[39m\u001b[34m(self, timeout, poll_interval, poll_intervall, blocking, cancel_check)\u001b[39m\n\u001b[32m    511\u001b[39m         msg = \u001b[33m\"\u001b[39m\u001b[33mLock \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m not acquired on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, waiting \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m seconds ...\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    512\u001b[39m         _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)\n\u001b[32m--> \u001b[39m\u001b[32m513\u001b[39m         \u001b[30;43mtime\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msleep\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpoll_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    514\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:\n\u001b[32m    515\u001b[39m     \u001b[38;5;28mself\u001b[39m._context.lock_counter = \u001b[38;5;28mmax\u001b[39m(\u001b[32m0\u001b[39m, \u001b[38;5;28mself\u001b[39m._context.lock_counter - \u001b[32m1\u001b[39m)\n",
+            "\u001b[31mKeyboardInterrupt\u001b[39m: "
+          ]
+        }
+      ],
       "source": [
         "# Cell 7: Load model (4-bit on CUDA Colab; fp16/fp32 fallback if bitsandbytes missing)\n",
         "from transformers import AutoTokenizer, AutoModelForCausalLM\n",
@@ -507,44 +548,13 @@
         "    print(\"Device: (see first parameter device)\")\n",
         "if torch.cuda.is_available():\n",
         "    print(f\"CUDA memory: {torch.cuda.memory_allocated()/1e9:.2f} GB\")"
-      ],
-      "execution_count": 7,
-      "outputs": [
-        {
-          "output_type": "stream",
-          "text": [
-            "Loading Qwen/Qwen2.5-1.5B-Instruct without 4-bit (bitsandbytes/CUDA unavailable).\n",
-            "  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\n",
-            "  On Mac: use fp16 on MPS or fp32 on CPU.\n"
-          ]
-        },
-        {
-          "output_type": "error",
-          "ename": "KeyboardInterrupt",
-          "evalue": "",
-          "traceback": [
-            "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-            "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
-            "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[7]\u001b[39m\u001b[32m, line 44\u001b[39m\n\u001b[32m     40\u001b[39m         \u001b[33m\"  On Colab: run `pip install -U bitsandbytes>=0.46.1` and use a GPU runtime.\\n\"\u001b[39m\n\u001b[32m     41\u001b[39m         \u001b[33m\"  On Mac: use fp16 on MPS or fp32 on CPU.\"\u001b[39m\n\u001b[32m     42\u001b[39m     )\n\u001b[32m     43\u001b[39m     dtype = torch.float16 \u001b[38;5;28;01mif\u001b[39;00m (torch.cuda.is_available() \u001b[38;5;28;01mor\u001b[39;00m getattr(torch.backends, \u001b[33m\"mps\"\u001b[39m, \u001b[38;5;28;01mNone\u001b[39;00m) \u001b[38;5;28;01mand\u001b[39;00m torch.backends.mps.is_available()) \u001b[38;5;28;01melse\u001b[39;00m torch.float32\n\u001b[32m---> \u001b[39m\u001b[32m44\u001b[39m     model = AutoModelForCausalLM.from_pretrained(\n\u001b[32m     45\u001b[39m         MODEL_NAME,\n\u001b[32m     46\u001b[39m         trust_remote_code=\u001b[38;5;28;01mTrue\u001b[39;00m,\n\u001b[32m     47\u001b[39m         dtype=dtype,\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/models/auto/auto_factory.py:394\u001b[39m, in \u001b[36m_BaseAutoModelClass.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, *model_args, **kwargs)\u001b[39m\n\u001b[32m    392\u001b[39m         \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(parent_config, \u001b[33m\"\u001b[39m\u001b[33mquantization_config\u001b[39m\u001b[33m\"\u001b[39m):\n\u001b[32m    393\u001b[39m             config.quantization_config = parent_config.quantization_config\n\u001b[32m--> \u001b[39m\u001b[32m394\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mmodel_class\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mfrom_pretrained\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    395\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mmodel_args\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mhub_kwargs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\n\u001b[32m    396\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    397\u001b[39m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[32m    398\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mUnrecognized configuration class \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mconfig.\u001b[34m__class__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m for this kind of AutoModel: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mcls\u001b[39m.\u001b[34m__name__\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[33m\"\u001b[39m\n\u001b[32m    399\u001b[39m     \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mModel type should be one of \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[33m'\u001b[39m\u001b[33m, \u001b[39m\u001b[33m'\u001b[39m.join(c.\u001b[34m__name__\u001b[39m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mfor\u001b[39;00m\u001b[38;5;250m \u001b[39mc\u001b[38;5;250m \u001b[39m\u001b[38;5;129;01min\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28mcls\u001b[39m._model_mapping)\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    400\u001b[39m )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:4118\u001b[39m, in \u001b[36mPreTrainedModel.from_pretrained\u001b[39m\u001b[34m(cls, pretrained_model_name_or_path, config, cache_dir, ignore_mismatched_sizes, force_download, local_files_only, token, revision, use_safetensors, weights_only, fusion_config, disable_mmap, *model_args, **kwargs)\u001b[39m\n\u001b[32m   4113\u001b[39m     logger.warning_once(\n\u001b[32m   4114\u001b[39m         \u001b[33m\"\u001b[39m\u001b[33mA kernel_config was provided but use_kernels is False; setting use_kernels=True automatically. To suppress this warning, explicitly set use_kernels to True.\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m   4115\u001b[39m     )\n\u001b[32m   4116\u001b[39m     use_kernels = \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m4118\u001b[39m checkpoint_files, sharded_metadata = \u001b[30;43m_get_resolved_checkpoint_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   4119\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4120\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mvariant\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4121\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgguf_file\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4122\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muse_safetensors\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4123\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mdownload_kwargs\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdownload_kwargs_with_commit\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4124\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4125\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcls\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mis_remote_code\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4126\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtransformers_explicit_filename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mgetattr\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mconfig\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43mtransformers_weights\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4127\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   4128\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   4130\u001b[39m is_quantized = hf_quantizer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[32m   4132\u001b[39m \u001b[38;5;66;03m# Find the correct dtype based on current state\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/modeling_utils.py:660\u001b[39m, in \u001b[36m_get_resolved_checkpoint_files\u001b[39m\u001b[34m(pretrained_model_name_or_path, variant, gguf_file, use_safetensors, user_agent, is_remote_code, transformers_explicit_filename, download_kwargs, tqdm_class)\u001b[39m\n\u001b[32m    648\u001b[39m can_auto_convert = (\n\u001b[32m    649\u001b[39m     \u001b[38;5;129;01mnot\u001b[39;00m is_offline_mode()  \u001b[38;5;66;03m# for obvious reasons\u001b[39;00m\n\u001b[32m    650\u001b[39m     \u001b[38;5;66;03m# If we are in a CI environment or in a pytest run, we prevent the conversion\u001b[39;00m\n\u001b[32m   (...)\u001b[39m\u001b[32m    653\u001b[39m     \u001b[38;5;129;01mand\u001b[39;00m subfolder == \u001b[33m\"\u001b[39m\u001b[33m\"\u001b[39m  \u001b[38;5;66;03m# converter bot does not work on subfolders\u001b[39;00m\n\u001b[32m    654\u001b[39m )\n\u001b[32m    656\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    657\u001b[39m     \u001b[38;5;66;03m# Load from URL or cache if already cached\u001b[39;00m\n\u001b[32m    658\u001b[39m     \u001b[38;5;66;03m# Since we set _raise_exceptions_for_missing_entries=False, we don't get an exception but a None\u001b[39;00m\n\u001b[32m    659\u001b[39m     \u001b[38;5;66;03m# result when internet is up, the repo and revision exist, but the file does not.\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m660\u001b[39m     resolved_archive_file = \u001b[30;43mcached_file\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpretrained_model_name_or_path\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mcached_file_kwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    662\u001b[39m     \u001b[38;5;66;03m# Try safetensors files first if not already found\u001b[39;00m\n\u001b[32m    663\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m resolved_archive_file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mand\u001b[39;00m filename == _add_variant(SAFE_WEIGHTS_NAME, variant):\n\u001b[32m    664\u001b[39m         \u001b[38;5;66;03m# Maybe the checkpoint is sharded, we try to grab the index name in this case.\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:278\u001b[39m, in \u001b[36mcached_file\u001b[39m\u001b[34m(path_or_repo_id, filename, **kwargs)\u001b[39m\n\u001b[32m    223\u001b[39m \u001b[38;5;28;01mdef\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[34mcached_file\u001b[39m(\n\u001b[32m    224\u001b[39m     path_or_repo_id: \u001b[38;5;28mstr\u001b[39m | os.PathLike,\n\u001b[32m    225\u001b[39m     filename: \u001b[38;5;28mstr\u001b[39m,\n\u001b[32m    226\u001b[39m     **kwargs,\n\u001b[32m    227\u001b[39m ) -> \u001b[38;5;28mstr\u001b[39m | \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[32m    228\u001b[39m \u001b[38;5;250m    \u001b[39m\u001b[33;03m\"\"\"\u001b[39;00m\n\u001b[32m    229\u001b[39m \u001b[33;03m    Tries to locate a file in a local folder and repo, downloads and cache it if necessary.\u001b[39;00m\n\u001b[32m    230\u001b[39m \n\u001b[32m   (...)\u001b[39m\u001b[32m    276\u001b[39m \u001b[33;03m    ```\u001b[39;00m\n\u001b[32m    277\u001b[39m \u001b[33;03m    \"\"\"\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m278\u001b[39m     file = \u001b[30;43mcached_files\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    279\u001b[39m     file = file[\u001b[32m0\u001b[39m] \u001b[38;5;28;01mif\u001b[39;00m file \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;28;01melse\u001b[39;00m file\n\u001b[32m    280\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m file\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/transformers/utils/hub.py:422\u001b[39m, in \u001b[36mcached_files\u001b[39m\u001b[34m(path_or_repo_id, filenames, cache_dir, force_download, proxies, token, revision, local_files_only, subfolder, repo_type, user_agent, _raise_exceptions_for_gated_repo, _raise_exceptions_for_missing_entries, _raise_exceptions_for_connection_errors, _commit_hash, tqdm_class, **deprecated_kwargs)\u001b[39m\n\u001b[32m    419\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m    420\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(full_filenames) == \u001b[32m1\u001b[39m:\n\u001b[32m    421\u001b[39m         \u001b[38;5;66;03m# This is slightly better for only 1 file\u001b[39;00m\n\u001b[32m--> \u001b[39m\u001b[32m422\u001b[39m         \u001b[30;43mhf_hub_download\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    423\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mpath_or_repo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    424\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mfilenames\u001b[39;49m\u001b[30;43m[\u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m]\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    425\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43;01mNone\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlen\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m==\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m0\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43msubfolder\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    426\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    427\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    428\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    429\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43muser_agent\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    430\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    431\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mproxies\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    432\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    433\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    434\u001b[39m \u001b[30;43m            \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    435\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    436\u001b[39m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m    437\u001b[39m         snapshot_download(\n\u001b[32m    438\u001b[39m             path_or_repo_id,\n\u001b[32m    439\u001b[39m             allow_patterns=full_filenames,\n\u001b[32m   (...)\u001b[39m\u001b[32m    448\u001b[39m             tqdm_class=tqdm_class,\n\u001b[32m    449\u001b[39m         )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_validators.py:88\u001b[39m, in \u001b[36mvalidate_hf_hub_args.<locals>._inner_fn\u001b[39m\u001b[34m(*args, **kwargs)\u001b[39m\n\u001b[32m     84\u001b[39m         validate_repo_id(arg_value)\n\u001b[32m     86\u001b[39m kwargs = smoothly_deprecate_legacy_arguments(fn_name=fn.\u001b[34m__name__\u001b[39m, kwargs=kwargs)\n\u001b[32m---> \u001b[39m\u001b[32m88\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mfn\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43margs\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43m*\u001b[39;49m\u001b[30;43mkwargs\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:995\u001b[39m, in \u001b[36mhf_hub_download\u001b[39m\u001b[34m(repo_id, filename, subfolder, repo_type, revision, library_name, library_version, cache_dir, local_dir, user_agent, force_download, etag_timeout, token, local_files_only, headers, endpoint, tqdm_class, dry_run)\u001b[39m\n\u001b[32m    974\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m _hf_hub_download_to_local_dir(\n\u001b[32m    975\u001b[39m         \u001b[38;5;66;03m# Destination\u001b[39;00m\n\u001b[32m    976\u001b[39m         local_dir=local_dir,\n\u001b[32m   (...)\u001b[39m\u001b[32m    992\u001b[39m         dry_run=dry_run,\n\u001b[32m    993\u001b[39m     )\n\u001b[32m    994\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m995\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43m_hf_hub_download_to_cache_dir\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m    996\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Destination\u001b[39;49;00m\n\u001b[32m    997\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mcache_dir\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m    998\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# File info\u001b[39;49;00m\n\u001b[32m    999\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_id\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1000\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mfilename\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1001\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrepo_type\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1002\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mrevision\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1003\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# HTTP info\u001b[39;49;00m\n\u001b[32m   1004\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mendpoint\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1005\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43metag_timeout\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1006\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mheaders\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mhf_headers\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1007\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtoken\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1008\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43;03m# Additional options\u001b[39;49;00m\n\u001b[32m   1009\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mlocal_files_only\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1010\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mforce_download\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1011\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1012\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mdry_run\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1013\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/file_download.py:1213\u001b[39m, in \u001b[36m_hf_hub_download_to_cache_dir\u001b[39m\u001b[34m(cache_dir, repo_id, filename, repo_type, revision, endpoint, etag_timeout, headers, token, local_files_only, force_download, tqdm_class, dry_run)\u001b[39m\n\u001b[32m   1209\u001b[39m         \u001b[38;5;28;01mreturn\u001b[39;00m pointer_path\n\u001b[32m   1211\u001b[39m \u001b[38;5;66;03m# Local file doesn't exist or etag isn't a match => retrieve file from remote (or cache)\u001b[39;00m\n\u001b[32m-> \u001b[39m\u001b[32m1213\u001b[39m \u001b[30;43m\u001b[39;49m\u001b[30;43;01mwith\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mWeakFileLock\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlock_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n\u001b[32m   1214\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m_download_to_tmp_and_move\u001b[39;49m\u001b[30;43m(\u001b[39;49m\n\u001b[32m   1215\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mincomplete_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m+\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m.incomplete\u001b[39;49m\u001b[30;43m\"\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1216\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mdestination_path\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mPath\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mblob_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   (...)\u001b[39m\u001b[32m   1224\u001b[39m \u001b[30;43m        \u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mtqdm_class\u001b[39;49m\u001b[30;43m,\u001b[39;49m\n\u001b[32m   1225\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m   1226\u001b[39m \u001b[30;43m    \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mnot\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mos\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mpath\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mexists\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpointer_path\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m:\u001b[39;49m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m/opt/homebrew/Cellar/python@3.14/3.14.2_1/Frameworks/Python.framework/Versions/3.14/lib/python3.14/contextlib.py:141\u001b[39m, in \u001b[36m_GeneratorContextManager.__enter__\u001b[39m\u001b[34m(self)\u001b[39m\n\u001b[32m    139\u001b[39m \u001b[38;5;28;01mdel\u001b[39;00m \u001b[38;5;28mself\u001b[39m.args, \u001b[38;5;28mself\u001b[39m.kwds, \u001b[38;5;28mself\u001b[39m.func\n\u001b[32m    140\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m141\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[30;43mnext\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mself\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43mgen\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    142\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mStopIteration\u001b[39;00m:\n\u001b[32m    143\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[33m\"\u001b[39m\u001b[33mgenerator didn\u001b[39m\u001b[33m'\u001b[39m\u001b[33mt yield\u001b[39m\u001b[33m\"\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m\u001b[38;5;250m \u001b[39m\u001b[38;5;28;01mNone\u001b[39;00m\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/huggingface_hub/utils/_fixes.py:99\u001b[39m, in \u001b[36mWeakFileLock\u001b[39m\u001b[34m(lock_file, timeout)\u001b[39m\n\u001b[32m     96\u001b[39m     \u001b[38;5;28;01mraise\u001b[39;00m Timeout(\u001b[38;5;28mstr\u001b[39m(lock_file))\n\u001b[32m     98\u001b[39m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[32m---> \u001b[39m\u001b[32m99\u001b[39m     \u001b[30;43mlock\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43macquire\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m=\u001b[39;49m\u001b[30;43mmin\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m,\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43m-\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43melapsed_time\u001b[39;49m\u001b[30;43m)\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01mif\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mtimeout\u001b[39;49m\u001b[30;43m \u001b[39;49m\u001b[30;43;01melse\u001b[39;49;00m\u001b[30;43m \u001b[39;49m\u001b[30;43mlog_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    100\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m Timeout:\n\u001b[32m    101\u001b[39m     logger.info(\n\u001b[32m    102\u001b[39m         \u001b[33mf\u001b[39m\u001b[33m\"\u001b[39m\u001b[33mStill waiting to acquire lock on \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mlock_file\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m (elapsed: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mtime.time()\u001b[38;5;250m \u001b[39m-\u001b[38;5;250m \u001b[39mstart_time\u001b[38;5;132;01m:\u001b[39;00m\u001b[33m.1f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[33m seconds)\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    103\u001b[39m     )\n",
-            "\u001b[36mFile \u001b[39m\u001b[32m~/viral-posts-env/.venv/lib/python3.14/site-packages/filelock/_api.py:513\u001b[39m, in \u001b[36mBaseFileLock.acquire\u001b[39m\u001b[34m(self, timeout, poll_interval, poll_intervall, blocking, cancel_check)\u001b[39m\n\u001b[32m    511\u001b[39m         msg = \u001b[33m\"\u001b[39m\u001b[33mLock \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m not acquired on \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m, waiting \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[33m seconds ...\u001b[39m\u001b[33m\"\u001b[39m\n\u001b[32m    512\u001b[39m         _LOGGER.debug(msg, lock_id, lock_filename, poll_interval)\n\u001b[32m--> \u001b[39m\u001b[32m513\u001b[39m         \u001b[30;43mtime\u001b[39;49m\u001b[30;43m.\u001b[39;49m\u001b[30;43msleep\u001b[39;49m\u001b[30;43m(\u001b[39;49m\u001b[30;43mpoll_interval\u001b[39;49m\u001b[30;43m)\u001b[39;49m\n\u001b[32m    514\u001b[39m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mBaseException\u001b[39;00m:\n\u001b[32m    515\u001b[39m     \u001b[38;5;28mself\u001b[39m._context.lock_counter = \u001b[38;5;28mmax\u001b[39m(\u001b[32m0\u001b[39m, \u001b[38;5;28mself\u001b[39m._context.lock_counter - \u001b[32m1\u001b[39m)\n",
-            "\u001b[31mKeyboardInterrupt\u001b[39m: "
-          ]
-        }
       ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 8: LLM agent functions\n",
         "SYSTEM_PROMPT = textwrap.dedent(\"\"\"\\\n",
@@ -610,6 +620,21 @@
         "            f\"Plan your actions (JSON only):\")\n",
         "\n",
         "\n",
+        "def is_well_formed_response(text):\n",
+        "    try:\n",
+        "        t = text.strip()\n",
+        "        if \"```\" in t:\n",
+        "            t = \"\\n\".join(l for l in t.split(\"\\n\") if not l.strip().startswith(\"```\")).strip()\n",
+        "        s, e = t.find(\"{\"), t.rfind(\"}\") + 1\n",
+        "        d = json.loads(t[s:e])\n",
+        "        for tc in d.get(\"tool_calls\", []):\n",
+        "            if not isinstance(tc, dict) or not isinstance(tc.get(\"arguments\", {}), dict):\n",
+        "                return False\n",
+        "        return True\n",
+        "    except Exception:\n",
+        "        return False\n",
+        "\n",
+        "\n",
         "def parse_model_output(text):\n",
         "    text = text.strip()\n",
         "    if \"```\" in text:\n",
@@ -620,23 +645,32 @@
         "        text = text[start:end]\n",
         "    try:\n",
         "        data = json.loads(text)\n",
-        "        tool_calls = [ToolCall(name=tc[\"name\"], arguments=tc.get(\"arguments\", {}))\n",
-        "                      for tc in data.get(\"tool_calls\", []) if isinstance(tc, dict) and \"name\" in tc]\n",
-        "        scheduled = []\n",
-        "        for a in data.get(\"scheduled_actions\", []):\n",
-        "            try:\n",
-        "                scheduled.append(ScheduledAction(**a))\n",
-        "            except Exception:\n",
-        "                # Same as original bare `except:`: skip invalid scheduled_actions entries\n",
-        "                pass\n",
-        "        return ViraltestAction(\n",
-        "            tool_calls=tool_calls,\n",
-        "            scheduled_actions=scheduled,\n",
-        "            notes=data.get(\"notes\"),\n",
-        "        )\n",
         "    except Exception:\n",
-        "        # Same behavior as original bare `except:`: any parse/validation failure -> empty action\n",
         "        return ViraltestAction(scheduled_actions=[])\n",
+        "    tool_calls = []\n",
+        "    for tc in data.get(\"tool_calls\", []):\n",
+        "        if not isinstance(tc, dict) or \"name\" not in tc:\n",
+        "            continue\n",
+        "        args = tc.get(\"arguments\", {})\n",
+        "        if isinstance(args, list) and args and isinstance(args[0], dict):\n",
+        "            args = args[0]\n",
+        "        if not isinstance(args, dict):\n",
+        "            continue\n",
+        "        try:\n",
+        "            tool_calls.append(ToolCall(name=tc[\"name\"], arguments=args))\n",
+        "        except Exception:\n",
+        "            pass\n",
+        "    scheduled = []\n",
+        "    for a in data.get(\"scheduled_actions\", []):\n",
+        "        try:\n",
+        "            scheduled.append(ScheduledAction(**a))\n",
+        "        except Exception:\n",
+        "            pass\n",
+        "    return ViraltestAction(\n",
+        "        tool_calls=tool_calls,\n",
+        "        scheduled_actions=scheduled,\n",
+        "        notes=data.get(\"notes\"),\n",
+        "    )\n",
         "\n",
         "\n",
         "def _infer_model_device(m):\n",
@@ -710,9 +744,7 @@
         "            \"burned_out\": obs.creator_energy <= 0}\n",
         "\n",
         "print(\"LLM agent functions defined.\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -725,7 +757,9 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 9: Run untrained model\n",
         "print(\"Running UNTRAINED base model on all tasks...\")\n",
@@ -742,9 +776,7 @@
         "print(\"BEFORE TRAINING:\")\n",
         "for t in TASKS:\n",
         "    print(f\"  {t}: grader={before_results[t]['grader_score']:.4f}\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -763,7 +795,9 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 10: Attach LoRA adapter\n",
         "from peft import LoraConfig, get_peft_model, TaskType\n",
@@ -778,20 +812,20 @@
         "model.enable_input_require_grads()\n",
         "peft_model = get_peft_model(model, lora_config)\n",
         "peft_model.print_trainable_parameters()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 11: Training loop\n",
         "from trl import SFTTrainer, SFTConfig\n",
         "from datasets import Dataset\n",
         "\n",
-        "NUM_ROUNDS = 1\n",
-        "EPISODES_PER_ROUND = 1\n",
+        "NUM_ROUNDS = 4\n",
+        "EPISODES_PER_ROUND = 6\n",
         "TOP_K_FRACTION = 0.5\n",
         "\n",
         "training_log = {\n",
@@ -820,6 +854,8 @@
         "        episode_graders.append(result[\"grader_score\"])\n",
         "\n",
         "        for pr in result[\"pairs\"]:\n",
+        "            if not is_well_formed_response(pr[\"response\"]):\n",
+        "                continue\n",
         "            text = (f\"<|im_start|>system\\n{SYSTEM_PROMPT}<|im_end|>\\n\"\n",
         "                    f\"<|im_start|>user\\n{pr['prompt']}<|im_end|>\\n\"\n",
         "                    f\"<|im_start|>assistant\\n{pr['response']}<|im_end|>\")\n",
@@ -851,7 +887,7 @@
         "        warmup_ratio=0.1,\n",
         "        logging_steps=1,\n",
         "        save_strategy=\"no\",\n",
-        "        max_length=1024,\n",
+        "        max_length=4096,\n",
         "        bf16=True,\n",
         "        gradient_checkpointing=False,\n",
         "        dataloader_num_workers=4,\n",
@@ -881,9 +917,7 @@
         "elapsed = time.time() - t_start\n",
         "print(f\"\\nTraining complete in {elapsed/60:.1f} min\")\n",
         "print(pd.DataFrame(training_log).to_string(index=False))"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -896,7 +930,9 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 12: Run trained model\n",
         "print(\"Running TRAINED model on all tasks...\")\n",
@@ -914,9 +950,7 @@
         "print(\"AFTER TRAINING:\")\n",
         "for t in TASKS:\n",
         "    print(f\"  {t}: grader={after_results[t]['grader_score']:.4f}\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -927,7 +961,9 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 13: Training curves\n",
         "fig, axes = plt.subplots(1, 2, figsize=(14, 5))\n",
@@ -949,13 +985,13 @@
         "fig.tight_layout()\n",
         "fig.savefig(f'{PLOTS_DIR}/reward_curve.png', dpi=150, bbox_inches='tight')\n",
         "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 14: Before vs After\n",
         "task_labels = [t.replace('monthly_', '').title() for t in TASKS]\n",
@@ -985,13 +1021,13 @@
         "fig.tight_layout()\n",
         "fig.savefig(f'{PLOTS_DIR}/before_after.png', dpi=150, bbox_inches='tight')\n",
         "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 15: Trajectory comparison\n",
         "fig, axes = plt.subplots(2, 3, figsize=(16, 8))\n",
@@ -1015,9 +1051,7 @@
         "fig.tight_layout()\n",
         "fig.savefig(f'{PLOTS_DIR}/training_trajectories.png', dpi=150, bbox_inches='tight')\n",
         "plt.show()"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "markdown",
@@ -1028,7 +1062,9 @@
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 16: Final summary\n",
         "print(\"=\" * 67)\n",
@@ -1065,13 +1101,13 @@
         "\n",
         "print(f\"\\nSaved to {PLOTS_DIR}/\")\n",
         "print(\"All results are from real LoRA weight updates on real environment runs.\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     },
     {
       "cell_type": "code",
+      "execution_count": null,
       "metadata": {},
+      "outputs": [],
       "source": [
         "# Cell 17: Save adapter\n",
         "save_path = \"./viraltest_trained_adapter\"\n",
@@ -1079,9 +1115,7 @@
         "tokenizer.save_pretrained(save_path)\n",
         "print(f\"LoRA adapter saved to {save_path}\")\n",
         "print(\"Load with: PeftModel.from_pretrained(base_model, save_path)\")"
-      ],
-      "execution_count": null,
-      "outputs": []
+      ]
     }
   ],
   "metadata": {
@@ -1107,4 +1141,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 4
-}
\ No newline at end of file
+}