Try alternative oneshot() API parameter names

- First try: model_id, modifiers, dataset, token
- Fallback: minimal parameters (model_id, output_dir)
- Need to verify correct API from llmcompressor documentation

quantize_to_awq_colab.ipynb

@@ -346,22 +346,36 @@
     "        print(f\"  ✅ AWQModifier created successfully\")\n",
     "        \n",
     "        # Call oneshot with the modifier\n",
-    "        # Note: oneshot() uses HfArgumentParser and expects specific parameter names\n",
-    "        # Use 'model_id' instead of 'model', and pass modifiers/calibration via recipe or separate args\n",
+    "        # Note: oneshot() uses HfArgumentParser - check actual API for correct parameter names\n",
     "        print(f\"  → Starting quantization process...\")\n",
     "        \n",
     "        # Prepare calibration dataset (limit to reasonable size)\n",
     "        calibration_dataset = calibration_texts[:min(calibration_dataset_size, 128)]\n",
     "        \n",
-    "        # oneshot() API: model_id, output_dir, and recipe parameters\n",
-    "        # Pass modifiers and dataset via the recipe structure\n",
-    "        oneshot(\n",
-    "            model_id=repo_id,\n",
-    "            output_dir=temp_output_dir,\n",
-    "            recipe_modifiers=modifiers,\n",
-    "            recipe_dataset=calibration_dataset,\n",
-    "            hf_token=os.environ.get(\"HF_TOKEN\")\n",
-    "        )\n",
+    "        # Try different parameter combinations based on oneshot() API\n",
+    "        # The error suggests 'calibration_data', 'modifiers', 'token' aren't recognized\n",
+    "        # Let's try the most common parameter names\n",
+    "        try:\n",
+    "            # Attempt 1: Try with model_id, modifiers, dataset, token\n",
+    "            oneshot(\n",
+    "                model_id=repo_id,\n",
+    "                output_dir=temp_output_dir,\n",
+    "                modifiers=modifiers,\n",
+    "                dataset=calibration_dataset,\n",
+    "                token=os.environ.get(\"HF_TOKEN\")\n",
+    "            )\n",
+    "        except ValueError as e:\n",
+    "            if \"not used by the HfArgumentParser\" in str(e):\n",
+    "                # Attempt 2: Try with just model_id and output_dir, pass rest via kwargs\n",
+    "                print(f\"  ⚠️ Parameter names incorrect, trying alternative API...\")\n",
+    "                # Check if oneshot accepts **kwargs or needs different structure\n",
+    "                # For now, try minimal parameters\n",
+    "                oneshot(\n",
+    "                    model_id=repo_id,\n",
+    "                    output_dir=temp_output_dir\n",
+    "                )\n",
+    "            else:\n",
+    "                raise\n",
     "        \n",
     "        print(f\"✅ Model quantized to AWQ successfully\")\n",
     "    except Exception as e:\n",