diff --git a/sia/llm_engine/qwq_llm_engine.ipynb b/sia/llm_engine/qwq_llm_engine.ipynb
index 3667e1d..6d27632 100644
--- a/sia/llm_engine/qwq_llm_engine.ipynb
+++ b/sia/llm_engine/qwq_llm_engine.ipynb
@@ -2,9 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+      "Unsloth: Failed to patch Gemma3ForConditionalGeneration.\n",
+      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+      "INFO 04-23 16:36:10 [__init__.py:239] Automatically detected platform cuda.\n"
+     ]
+    }
+   ],
    "source": [
     "# Unsloth should be imported before transformers to ensure all optimizations are applied.\n",
     "from unsloth import FastLanguageModel"
@@ -12,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -24,18 +35,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
     "temperature = 0.6\n",
-    "model_path = \"/root/models/notebook\"\n",
+    "model_path = \"/root/models/notebook_merged_4bit\"\n",
     "xml_schema_text = Path(\"/root/sia/action_schema.xsd\").read_text()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -47,24 +58,74 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==((====))==  Unsloth 2025.3.19: Fast Qwen2 patching. Transformers: 4.51.3. vLLM: 0.8.2.\n",
+      "   \\\\   /|    NVIDIA RTX 6000 Ada Generation. Num GPUs = 1. Max memory: 47.5 GB. Platform: Linux.\n",
+      "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 8.9. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
+      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post2. FA2 = False]\n",
+      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
+      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sliding Window Attention is enabled but not implemented for `eager`; unexpected results may be encountered.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "validate_env\n",
+      "device_map sequential\n",
+      "validate_env\n",
+      "device_map OrderedDict([('', 0)])\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a99acfd42fa645abae77144125a734b7",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "# Load model\n",
     "model, _returned_tokenizer = FastLanguageModel.from_pretrained(\n",
     "    model_path,\n",
-    "    load_in_4bit = True, # False for LoRA 16bit\n",
-    "    fast_inference = True, # Enable vLLM fast inference\n",
     "    gpu_memory_utilization = 0.8, # Reduce if out of memory\n",
     ")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Device set to use cuda:0\n"
+     ]
+    }
+   ],
    "source": [
     "# Create inference pipeline with memory-efficient settings\n",
     "pipeline = pipeline(\n",
@@ -77,7 +138,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -86,7 +147,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +160,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -112,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -124,7 +185,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -139,7 +200,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -148,7 +209,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -162,9 +223,18 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 14,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<write_stdout>\n",
+      "Hello! I'm just a large language model, so I don't have feelings, but thank you for asking. How can I assist you today?</write_stdout><|im_end|>"
+     ]
+    }
+   ],
    "source": [
     "for text in streamer:\n",
     "    print(text, end=\"\")"
@@ -172,7 +242,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -185,7 +255,7 @@
   "kernelspec": {
    "display_name": "sia",
    "language": "python",
-   "name": "sia"
+   "name": "python3"
   },
   "language_info": {
    "codemirror_mode": {
diff --git a/tools/train/train/qwq.ipynb b/tools/train/train/qwq.ipynb
index c88dd5e..932100f 100644
--- a/tools/train/train/qwq.ipynb
+++ b/tools/train/train/qwq.ipynb
@@ -2,9 +2,20 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 1,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "🦥 Unsloth: Will patch your computer to enable 2x faster free finetuning.\n",
+      "Unsloth: Failed to patch Gemma3ForConditionalGeneration.\n",
+      "🦥 Unsloth Zoo will now patch everything to make training faster!\n",
+      "INFO 04-23 16:23:47 [__init__.py:239] Automatically detected platform cuda.\n"
+     ]
+    }
+   ],
    "source": [
     "# Unsloth should be imported before transformers to ensure all optimizations are applied.\n",
     "from unsloth import FastLanguageModel, is_bfloat16_supported"
@@ -12,7 +23,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -28,7 +39,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -37,18 +48,67 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
-    "args = qwq.Args([\"--output-dir\", \"/root/models/notebook\"])"
+    "args = qwq.Args([\"--output-dir\", \"/root/models/notebook\", \"--base-model\", \"unsloth/QwQ-32B-bnb-4bit\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 5,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Validating 20 XML files...\n",
+      "file: /root/sia/training/clean_start/iteration_20250116_134549_655.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/clean_start/iteration_20250116_134549_655.xml\n",
+      "file: /root/sia/training/clean_start/iteration_20250116_134555_680.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/clean_start/iteration_20250116_134555_680.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141241_092.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141241_092.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141252_317.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141252_317.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141302_940.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141302_940.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141329_886.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141329_886.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141343_416.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141343_416.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141357_412.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141357_412.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141410_965.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141410_965.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141428_204.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141428_204.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141441_443.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141441_443.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141447_231.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141447_231.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141454_509.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141454_509.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141458_495.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141458_495.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141503_889.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141503_889.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141516_718.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141516_718.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141533_231.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141533_231.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141603_549.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141603_549.xml\n",
+      "file: /root/sia/training/delete_indicated_entries/iteration_20250116_141633_083.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/delete_indicated_entries/iteration_20250116_141633_083.xml\n",
+      "file: /root/sia/training/list_entries_to_delete/iteration_20250116_141227_271.xml\n",
+      "WARNING: System prompt hash mismatch in /root/sia/training/list_entries_to_delete/iteration_20250116_141227_271.xml\n",
+      "Validation complete. Found 20 valid files.\n"
+     ]
+    }
+   ],
    "source": [
     "dataset = qwq.Dataset(args.config_path)\n",
     "dataset.validate()"
@@ -56,9 +116,94 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b72535c55d214b9da158b90ab0d3e65a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/8.14k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5b09cbc50ad94b81a25cf790b5841d75",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "d561672fb7e34306aada26df664e08cd",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "b2fa48006e3047689ce3951347ee8d87",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e4bb1c113f5b46dc95b90924de24aaaf",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "added_tokens.json:   0%|          | 0.00/707 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "200953e14bad47a38757dd643c7bb176",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "with open('/root/sia/qwq_tokenizer_config.json', 'r') as f:\n",
     "    tokenizer_config = json.load(f)\n",
@@ -71,9 +216,211 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 7,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "==((====))==  Unsloth 2025.3.19: Fast Qwen2 patching. Transformers: 4.51.3. vLLM: 0.8.2.\n",
+      "   \\\\   /|    NVIDIA RTX 6000 Ada Generation. Num GPUs = 1. Max memory: 47.5 GB. Platform: Linux.\n",
+      "O^O/ \\_/ \\    Torch: 2.6.0+cu124. CUDA: 8.9. CUDA Toolkit: 12.4. Triton: 3.2.0\n",
+      "\\        /    Bfloat16 = TRUE. FA [Xformers = 0.0.29.post2. FA2 = False]\n",
+      " \"-____-\"     Free license: http://github.com/unslothai/unsloth\n",
+      "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "78af66cb4ea84a4e8dea6cb5db0a7ca2",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model.safetensors.index.json:   0%|          | 0.00/280k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8d4904d5f68941369fef4e0b67f21397",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00001-of-00004.safetensors:   0%|          | 0.00/4.93G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "728274cc4ab84d64ab152b22a842e1bc",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00002-of-00004.safetensors:   0%|          | 0.00/4.96G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "4273150dc40846008cc36d19b87e595e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00003-of-00004.safetensors:   0%|          | 0.00/5.00G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0d4ad6c3535741a2aeb454c98d04a395",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "model-00004-of-00004.safetensors:   0%|          | 0.00/4.32G [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Sliding Window Attention is enabled but not implemented for `eager`; unexpected results may be encountered.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "73f1c658797c4b73937e26c2012e2019",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Loading checkpoint shards:   0%|          | 0/4 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "e926624cc8a1453aae8e0a814c3430da",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "generation_config.json:   0%|          | 0.00/238 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "ee12750395cc40b2a44b99e24a52ef23",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/8.14k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "36c00f147e684cca97857e4f9539b6ed",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "vocab.json:   0%|          | 0.00/2.78M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5bf2c158111a4a5c8f434d1493c6dc98",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "merges.txt:   0%|          | 0.00/1.67M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "cb036c9083a24ba092cc2c5d57f0bd32",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "added_tokens.json:   0%|          | 0.00/707 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6fcc331064ac4c2fa67461098f82fff6",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/614 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "7dea89c178f34e2185d7ebbac5b9f84e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/11.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "model, _returned_tokenizer = FastLanguageModel.from_pretrained(\n",
     "    model_name = args.base_model,\n",
@@ -83,9 +430,17 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Unsloth 2025.3.19 patched 64 layers with 64 QKV layers, 64 O layers and 64 MLP layers.\n"
+     ]
+    }
+   ],
    "source": [
     "model = FastLanguageModel.get_peft_model(\n",
     "    model,\n",
@@ -102,12 +457,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
     "training_args = TrainingArguments(\n",
-    "    output_dir=str(args.output_dir),\n",
+    "    output_dir=str(args.output_dir) + \"_train\",\n",
     "    num_train_epochs=3,\n",
     "    per_device_train_batch_size=1,\n",
     "    gradient_accumulation_steps=16,\n",
@@ -128,9 +483,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "0bf6b9b1d6de48688ff82a8077790f7a",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Generating train split: 0 examples [00:00, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "num_proc must be <= 20. Reducing num_proc to 20 for dataset of size 20.\n"
+     ]
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "74e69db167224b35b381b91827ac0c30",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "Unsloth: Tokenizing [\"messages\"] (num_proc=20):   0%|          | 0/20 [00:00<?, ? examples/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "trainer = SFTTrainer(\n",
     "    model=model,\n",
@@ -143,21 +534,103 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "==((====))==  Unsloth - 2x faster free finetuning | Num GPUs used = 1\n",
+      "   \\\\   /|    Num examples = 20 | Num Epochs = 3 | Total steps = 3\n",
+      "O^O/ \\_/ \\    Batch size per device = 1 | Gradient accumulation steps = 16\n",
+      "\\        /    Data Parallel GPUs = 1 | Total batch size (1 x 16 x 1) = 16\n",
+      " \"-____-\"     Trainable parameters = 134,217,728/32,000,000,000 (0.42% trained)\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unsloth: Will smartly offload gradients to save VRAM!\n"
+     ]
+    },
+    {
+     "data": {
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='3' max='3' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [3/3 01:48, Epoch 1/3]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Step</th>\n",
+       "      <th>Training Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
+      "text/plain": [
+       "<IPython.core.display.HTML object>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "TrainOutput(global_step=3, training_loss=1.7701025009155273, metrics={'train_runtime': 187.628, 'train_samples_per_second': 0.32, 'train_steps_per_second': 0.016, 'total_flos': 1.41437049721344e+16, 'train_loss': 1.7701025009155273})"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
    "source": [
     "trainer.train()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 13,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unsloth: Merging 4bit and LoRA weights to 4bit...\n",
+      "This might take 5 minutes...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/root/venvs/train/lib/python3.10/site-packages/peft/tuners/lora/bnb.py:351: UserWarning: Merge lora module to 4-bit linear may get different generations due to rounding errors.\n",
+      "  warnings.warn(\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Done.\n",
+      "Unsloth: Saving tokenizer... Done.\n",
+      "Unsloth: Saving model... This might take 10 minutes for Llama-7b... Done.\n"
+     ]
+    }
+   ],
    "source": [
     "model.save_pretrained_merged(\n",
-    "    str(args.output_dir), \n",
+    "    str(args.output_dir) + \"_merged_4bit\", \n",
     "    tokenizer=tokenizer,\n",
     "    save_method=\"merged_4bit_forced\"\n",
     ")"