diff --git "a/bonus-unit1/bonus-unit1.ipynb" "b/bonus-unit1/bonus-unit1.ipynb"
--- "a/bonus-unit1/bonus-unit1.ipynb"
+++ "b/bonus-unit1/bonus-unit1.ipynb"
@@ -121,17 +121,14 @@
      "base_uri": "https://localhost:8080/"
     },
     "id": "e63f4962-c644-491e-aa91-50e453e953a4",
-    "outputId": "3c1563d4-74fe-46f0-adcd-3e935261a89d",
+    "outputId": "443077a6-7cff-4c46-90ac-bf279300f6ec",
     "tags": []
    },
    "outputs": [],
    "source": [
     "!pip install -q -U bitsandbytes\n",
-    "!pip install -q -U transformers\n",
     "!pip install -q -U peft\n",
-    "!pip install -q -U accelerate\n",
-    "!pip install -q -U datasets\n",
-    "!pip install -q trl==0.12.2\n",
+    "!pip install -q -U trl\n",
     "!pip install -q -U tensorboardX\n",
     "!pip install -q wandb"
    ]
@@ -173,15 +170,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "id": "7ad2e4c2-593e-463e-9692-8d674c541d76",
    "metadata": {
-    "colab": {
-     "base_uri": "https://localhost:8080/",
-     "height": 382
-    },
     "id": "7ad2e4c2-593e-463e-9692-8d674c541d76",
-    "outputId": "5004413d-0202-4031-85e3-d1897fb8eba5",
     "tags": []
    },
    "outputs": [],
@@ -194,8 +186,8 @@
     "\n",
     "from transformers import AutoModelForCausalLM, AutoTokenizer, TrainingArguments, BitsAndBytesConfig, set_seed\n",
     "from datasets import load_dataset\n",
-    "from trl import SFTTrainer\n",
-    "from peft import get_peft_model, LoraConfig, TaskType\n",
+    "from trl import SFTConfig, SFTTrainer\n",
+    "from peft import LoraConfig, TaskType\n",
     "\n",
     "seed = 42\n",
     "set_seed(seed)\n",
@@ -203,7 +195,7 @@
     "import os\n",
     "\n",
     "# Put your HF Token here\n",
-    "os.environ['HF_TOKEN']=\"hf_xxxx\""
+    "os.environ['HF_TOKEN']=\"hf_xxxxxxx\" # the token should have write access"
    ]
   },
   {
@@ -226,112 +218,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "id": "29da85c8-33bf-4864-aed7-733cbe703512",
    "metadata": {
     "id": "29da85c8-33bf-4864-aed7-733cbe703512",
     "tags": []
    },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2196bea18c254339847558f804db8853",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer_config.json:   0%|          | 0.00/47.0k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "144ed3e99ca8400dad935c2aad2d0816",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.model:   0%|          | 0.00/4.24M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef4443985f3f44238b88775168b3eb8f",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/17.5M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f6f74184ebe44a3dbf575530c3e007f4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "special_tokens_map.json:   0%|          | 0.00/636 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "732a30a290144a65887d030eb90e562e",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "README.md:   0%|          | 0.00/354 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "2467d02301a849ce8cb0d06602652a77",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "train-00000-of-00001.parquet:   0%|          | 0.00/3.85M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "cee93ea2b48945ae82092c33b50e7ca4",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Generating train split:   0%|          | 0/3570 [00:00<?, ? examples/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
+   "outputs": [],
    "source": [
     "model_name = \"google/gemma-2-2b-it\"\n",
     "dataset_name = \"Jofthomas/hermes-function-calling-thinking-V1\"\n",
@@ -340,24 +233,24 @@
     "tokenizer.chat_template = \"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{{ '<start_of_turn>' + message['role'] + '\\n' + message['content'] | trim + '<end_of_turn><eos>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}\"\n",
     "\n",
     "\n",
-    "def preprocess(samples):\n",
-    "    batch = []\n",
-    "    for conversations in zip(samples[\"conversations\"]):\n",
-    "        conversation = conversations[0]\n",
+    "def preprocess(sample):\n",
+    "      messages = sample[\"messages\"]\n",
+    "      first_message = messages[0]\n",
     "\n",
-    "        # Instead of adding a system message, we merge the content into the first user message\n",
-    "        if conversation[0][\"role\"] == \"system\":\n",
-    "            system_message_content = conversation[0][\"content\"]\n",
-    "            # Merge system content with the first user message\n",
-    "            conversation[1][\"content\"] = system_message_content + \"Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>\\n\\n\" + conversation[1][\"content\"]\n",
-    "            # Remove the system message from the conversation\n",
-    "            conversation.pop(0)\n",
+    "      # Instead of adding a system message, we merge the content into the first user message\n",
+    "      if first_message[\"role\"] == \"system\":\n",
+    "          system_message_content = first_message[\"content\"]\n",
+    "          # Merge system content with the first user message\n",
+    "          messages[1][\"content\"] = system_message_content + \"Also, before making a call to a function take the time to plan the function to take. Make that thinking process between <think>{your thoughts}</think>\\n\\n\" + messages[1][\"content\"]\n",
+    "          # Remove the system message from the conversation\n",
+    "          messages.pop(0)\n",
     "\n",
-    "        batch.append(tokenizer.apply_chat_template(conversation, tokenize=False))\n",
+    "      return {\"text\": tokenizer.apply_chat_template(messages, tokenize=False)}\n",
     "\n",
-    "    return {\"content\": batch}\n",
     "\n",
-    "dataset = load_dataset(dataset_name)\n"
+    "\n",
+    "dataset = load_dataset(dataset_name)\n",
+    "dataset = dataset.rename_column(\"conversations\", \"messages\")\n"
    ]
   },
   {
@@ -381,18 +274,21 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "id": "b63d4832-d92e-482d-9fe6-6e9dbfee377a",
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
     "id": "b63d4832-d92e-482d-9fe6-6e9dbfee377a",
-    "outputId": "bda88f48-ca5d-4f47-b887-48d4ea5a53aa",
+    "outputId": "547d58fc-cf84-4878-f66e-ae817030a251",
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "343ae0d35c79439788601f8cb4e734d1",
+       "model_id": "b2ee958727314c8186af7ee5e5da64aa",
        "version_major": 2,
        "version_minor": 0
       },
@@ -409,11 +305,11 @@
      "text": [
       "DatasetDict({\n",
       "    train: Dataset({\n",
-      "        features: ['content'],\n",
+      "        features: ['text'],\n",
       "        num_rows: 3213\n",
       "    })\n",
       "    test: Dataset({\n",
-      "        features: ['content'],\n",
+      "        features: ['text'],\n",
       "        num_rows: 357\n",
       "    })\n",
       "})\n"
@@ -421,13 +317,12 @@
     }
    ],
    "source": [
-    "dataset = dataset.map(\n",
-    "    preprocess,\n",
-    "    batched=True,\n",
-    "    remove_columns=dataset[\"train\"].column_names\n",
-    ")\n",
+    "dataset = dataset.map(preprocess, remove_columns=\"messages\")\n",
     "dataset = dataset[\"train\"].train_test_split(0.1)\n",
-    "print(dataset)"
+    "print(dataset)\n",
+    "\n",
+    "dataset[\"train\"] = dataset[\"train\"].select(range(100))\n",
+    "dataset[\"test\"] = dataset[\"test\"].select(range(10))"
    ]
   },
   {
@@ -452,11 +347,14 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "id": "dc60da04-9411-487a-b629-2c59024a20c0",
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
     "id": "dc60da04-9411-487a-b629-2c59024a20c0",
-    "outputId": "6709e478-17b8-4769-865f-2cd025727ad4",
+    "outputId": "8af76a10-5a72-4401-cdf5-ee047fc2d850",
     "tags": []
    },
    "outputs": [
@@ -517,16 +415,19 @@
    ],
    "source": [
     "# Let's look at how we formatted the dataset\n",
-    "print(dataset[\"train\"][8][\"content\"])"
+    "print(dataset[\"train\"][8][\"text\"])"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "id": "53a48281-2346-4dfb-ad60-cad85129ec9b",
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/"
+    },
     "id": "53a48281-2346-4dfb-ad60-cad85129ec9b",
-    "outputId": "da4f7e33-227c-48bc-b3c3-24df31313a69",
+    "outputId": "e16c07f9-8bbf-4c82-d2d3-41d96a95ab2e",
     "tags": []
    },
    "outputs": [
@@ -563,93 +464,101 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "id": "833ba5d6-4c1e-4689-9fed-22cc03d2a63a",
    "metadata": {
     "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 754,
      "referenced_widgets": [
-      "fc7eee07d5824955adb7b9bbd025c297"
+      "54fc2d9962de4ffda0500a01e112b58a",
+      "8e70cccb0fb04a43b3c6ab234bcae9a8",
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      ]
     },
     "id": "833ba5d6-4c1e-4689-9fed-22cc03d2a63a",
-    "outputId": "2ef794e9-3e8e-4dec-d31c-b9242386c2d0",
+    "outputId": "3138107e-a2cd-447d-ee69-e07e401a1540",
     "tags": []
    },
    "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "c93adf223ba642ff83433bd0a49b0729",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "config.json:   0%|          | 0.00/838 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "fdcee6dad0714e3fb7c71e7964c3b277",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model.safetensors.index.json:   0%|          | 0.00/24.2k [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "f47c44a05d7e4d57b320571d39945e46",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "Downloading shards:   0%|          | 0/2 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ab17cbfdf3864dc18c8ae5fed06b8b90",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model-00001-of-00002.safetensors:   0%|          | 0.00/4.99G [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "7b4ba042da7641a1a9838d66711a0a4c",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "model-00002-of-00002.safetensors:   0%|          | 0.00/241M [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "45020ba214804c9b8e33e875508b860b",
+       "model_id": "bedabbdf4cf44865a83fc9702391c298",
        "version_major": 2,
        "version_minor": 0
       },
@@ -660,20 +569,6 @@
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "1b0a5cf11a9c4794b9de47ae54192b68",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "generation_config.json:   0%|          | 0.00/187 [00:00<?, ?B/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
     {
      "name": "stderr",
      "output_type": "stream",
@@ -714,7 +609,7 @@
        ")"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -743,7 +638,7 @@
     "tokenizer.chat_template = \"{{ bos_token }}{% if messages[0]['role'] == 'system' %}{{ raise_exception('System role not supported') }}{% endif %}{% for message in messages %}{{ '<start_of_turn>' + message['role'] + '\\n' + message['content'] | trim + '<end_of_turn><eos>\\n' }}{% endfor %}{% if add_generation_prompt %}{{'<start_of_turn>model\\n'}}{% endif %}\"\n",
     "\n",
     "model = AutoModelForCausalLM.from_pretrained(model_name,\n",
-    "                                              attn_implementation='eager',\n",
+    "                                             attn_implementation='eager',\n",
     "                                             device_map=\"auto\")\n",
     "model.resize_token_embeddings(len(tokenizer))\n",
     "model.to(torch.bfloat16)\n"
@@ -763,7 +658,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 7,
    "id": "482d36ab-e326-4fd7-bc59-425abcca55e7",
    "metadata": {
     "id": "482d36ab-e326-4fd7-bc59-425abcca55e7",
@@ -802,25 +697,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "3598b688-5a6f-437f-95ac-4794688cd38f",
    "metadata": {
     "id": "3598b688-5a6f-437f-95ac-4794688cd38f",
-    "outputId": "515f019f-87b6-40cb-9344-f4c19645e077",
     "tags": []
    },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/miniconda/lib/python3.9/site-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-      "  warnings.warn(\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "output_dir = \"gemma-2-2B-it-thinking-function_calling\" # The directory where the trained model checkpoints, logs, and other artifacts will be saved. It will also be the default name of the model when pushed to the hub if not redefined later.\n",
+    "username=\"Jofthomas\"# REPLCAE with your Hugging Face username\n",
+    "output_dir = \"gemma-2-2B-it-thinking-function_calling-V0\" # The directory where the trained model checkpoints, logs, and other artifacts will be saved. It will also be the default name of the model when pushed to the hub if not redefined later.\n",
     "per_device_train_batch_size = 1\n",
     "per_device_eval_batch_size = 1\n",
     "gradient_accumulation_steps = 4\n",
@@ -831,15 +717,15 @@
     "num_train_epochs=1\n",
     "warmup_ratio = 0.1\n",
     "lr_scheduler_type = \"cosine\"\n",
-    "max_seq_length = 2048\n",
+    "max_seq_length = 1500\n",
     "\n",
-    "training_arguments = TrainingArguments(\n",
+    "training_arguments = SFTConfig(\n",
     "    output_dir=output_dir,\n",
     "    per_device_train_batch_size=per_device_train_batch_size,\n",
     "    per_device_eval_batch_size=per_device_eval_batch_size,\n",
     "    gradient_accumulation_steps=gradient_accumulation_steps,\n",
     "    save_strategy=\"no\",\n",
-    "    evaluation_strategy=\"epoch\",\n",
+    "    eval_strategy=\"epoch\",\n",
     "    logging_steps=logging_steps,\n",
     "    learning_rate=learning_rate,\n",
     "    max_grad_norm=max_grad_norm,\n",
@@ -852,7 +738,9 @@
     "    push_to_hub=False,\n",
     "    num_train_epochs=num_train_epochs,\n",
     "    gradient_checkpointing=True,\n",
-    "    gradient_checkpointing_kwargs={\"use_reentrant\": False}\n",
+    "    gradient_checkpointing_kwargs={\"use_reentrant\": False},\n",
+    "    packing=True,\n",
+    "    max_seq_length=max_seq_length,\n",
     ")"
    ]
   },
@@ -868,11 +756,83 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 9,
    "id": "ba0366b5-c9d0-4f7e-97e0-1f964cfad147",
    "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 263,
+     "referenced_widgets": [
+      "e3fe61834c3e49a3895212a336776f9d",
+      "472cbf91b24f46829960d68e2316c417",
+      "bd746ea2e46a491e954ac6f32fb0e45b",
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+      "b2e16ad7540d4760b28f3a8c419905f8",
+      "f2157c83879046a29b72613bce9de56e",
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+      "2e6392b95f8a48568a89780adf76387f",
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+      "e0b3b3c072be44de8e0a2dae91598aa6",
+      "4e21f1bd903443a89dea32bb3f3c26a9"
+     ]
+    },
     "id": "ba0366b5-c9d0-4f7e-97e0-1f964cfad147",
-    "outputId": "8b2836b3-3a06-4c05-b046-6c7923911e40",
+    "outputId": "5602ad04-17c4-431b-e20d-7f0d0c7fd24e",
     "tags": []
    },
    "outputs": [
@@ -880,166 +840,33 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/home/user/miniconda/lib/python3.9/site-packages/huggingface_hub/utils/_deprecation.py:100: FutureWarning: Deprecated argument(s) used in '__init__': packing, dataset_text_field, max_seq_length, dataset_kwargs. Will not be supported from version '0.13.0'.\n",
-      "\n",
-      "Deprecated positional argument(s) used in SFTTrainer, please use the SFTConfig to set these arguments instead.\n",
-      "  warnings.warn(message, FutureWarning)\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/transformers/training_args.py:1568: FutureWarning: `evaluation_strategy` is deprecated and will be removed in version 4.46 of 🤗 Transformers. Use `eval_strategy` instead\n",
-      "  warnings.warn(\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/trl/trainer/sft_trainer.py:212: UserWarning: You passed a `packing` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
-      "  warnings.warn(\n",
       "/home/user/miniconda/lib/python3.9/site-packages/peft/tuners/tuners_utils.py:543: UserWarning: Model with `tie_word_embeddings=True` and the tied_target_modules=['lm_head'] are part of the adapter. This can lead to complications, for example when merging the adapter or converting your model to formats other than safetensors. See for example https://github.com/huggingface/peft/issues/2018.\n",
-      "  warnings.warn(\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/trl/trainer/sft_trainer.py:300: UserWarning: You passed a `max_seq_length` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
-      "  warnings.warn(\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/trl/trainer/sft_trainer.py:328: UserWarning: You passed a `dataset_text_field` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
-      "  warnings.warn(\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/trl/trainer/sft_trainer.py:334: UserWarning: You passed a `dataset_kwargs` argument to the SFTTrainer, the value you passed will override the one in the `SFTConfig`.\n",
-      "  warnings.warn(\n",
-      "/home/user/miniconda/lib/python3.9/site-packages/trl/trainer/sft_trainer.py:403: UserWarning: You passed a processing_class with `padding_side` not equal to `right` to the SFTTrainer. This might lead to some unexpected behaviour due to overflow issues when training a model in half-precision. You might consider adding `processing_class.padding_side = 'right'` to your code.\n",
       "  warnings.warn(\n"
      ]
-    }
-   ],
-   "source": [
-    "trainer = SFTTrainer(\n",
-    "    model=model,\n",
-    "    args=training_arguments,\n",
-    "    train_dataset=dataset[\"train\"],\n",
-    "    eval_dataset=dataset[\"test\"],\n",
-    "    tokenizer=tokenizer,\n",
-    "    packing=True,\n",
-    "    dataset_text_field=\"content\",\n",
-    "    max_seq_length=max_seq_length,\n",
-    "    peft_config=peft_config,\n",
-    "    dataset_kwargs={\n",
-    "        \"append_concat_token\": False,\n",
-    "        \"add_special_tokens\": False,\n",
-    "    },\n",
-    ")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "MtHjukK9xviB",
-   "metadata": {
-    "id": "MtHjukK9xviB"
-   },
-   "source": [
-    "Here, we launch the training 🔥. Perfect time for you to pause and grab a coffee ☕."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9e2df2e9-a82b-4540-aa89-1b40b70a7781",
-   "metadata": {
-    "id": "9e2df2e9-a82b-4540-aa89-1b40b70a7781",
-    "outputId": "8ad7e555-678b-4904-aa6b-000b619c9341",
-    "tags": []
-   },
-   "outputs": [
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
-     ]
     },
     {
      "data": {
-      "text/html": [
-       "\n",
-       "    <div>\n",
-       "      \n",
-       "      <progress value='389' max='389' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-       "      [389/389 14:14, Epoch 0/1]\n",
-       "    </div>\n",
-       "    <table border=\"1\" class=\"dataframe\">\n",
-       "  <thead>\n",
-       " <tr style=\"text-align: left;\">\n",
-       "      <th>Epoch</th>\n",
-       "      <th>Training Loss</th>\n",
-       "      <th>Validation Loss</th>\n",
-       "    </tr>\n",
-       "  </thead>\n",
-       "  <tbody>\n",
-       "    <tr>\n",
-       "      <td>0</td>\n",
-       "      <td>0.294600</td>\n",
-       "      <td>0.289091</td>\n",
-       "    </tr>\n",
-       "  </tbody>\n",
-       "</table><p>"
-      ],
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "be2be97d4aa64b23beaf316024229a3b",
+       "version_major": 2,
+       "version_minor": 0
+      },
       "text/plain": [
-       "<IPython.core.display.HTML object>"
+       "Applying chat template to train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "/home/user/miniconda/lib/python3.9/site-packages/peft/utils/save_and_load.py:230: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
-      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n"
-     ]
-    }
-   ],
-   "source": [
-    "trainer.train()\n",
-    "trainer.save_model()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393",
-   "metadata": {
-    "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393",
-    "tags": []
-   },
-   "source": [
-    "## Step 11: Let's push the Model and the Tokenizer to the Hub\n",
-    "\n",
-    "Let's push our model and out tokenizer to the Hub ! The model will be pushed under your username + the output_dir that we specified earlier."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "370af020-9319-4ff7-bea1-2842a4847caa",
-   "metadata": {
-    "colab": {
-     "referenced_widgets": [
-      "68b34e3d2eae4f24b83fa65cf5815738",
-      "15e7c632053c4ed88267061a8112d641",
-      "047ebf7fda8643c090129bc2b86a7e3e",
-      "ef5dab829e8b491581f6dae2b7718113",
-      "97ba6384a2f94db0880a17ff433a8ed9",
-      "ba0c0c53b23047ac9336fdbf8597f32f",
-      "8a033d5bd32b4a969fd5af612c550243",
-      "714d4a40a67a4763ba8f7ae029befbd7",
-      "52451f392b434fb39b882c9f094bd995",
-      "08c2ca8a719e4142bd877ae94d242f2e",
-      "02e660bd44a7414fae439751e9ffa1f2"
-     ]
-    },
-    "id": "370af020-9319-4ff7-bea1-2842a4847caa",
-    "outputId": "f5797c01-306d-48ee-a009-3c115f5b1ca5",
-    "tags": []
-   },
-   "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "68b34e3d2eae4f24b83fa65cf5815738",
+       "model_id": "f7416500f850456792cacf42656df46a",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "adapter_model.safetensors:   0%|          | 0.00/2.48G [00:00<?, ?B/s]"
+       "Tokenizing train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -1048,12 +875,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "15e7c632053c4ed88267061a8112d641",
+       "model_id": "482ded5a74484b5f8d8907b4a886f946",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739725934.r-jofthomas-fttest-0ihwmg95-70a55-shjb6:   0%|          | 0.00/21.5k [00:00<?, …"
+       "Packing train dataset:   0%|          | 0/100 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -1062,12 +889,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "047ebf7fda8643c090129bc2b86a7e3e",
+       "model_id": "3f59849c47ef42d0ac295296a4ad0a91",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739728410.r-jofthomas-fttest-0ihwmg95-70a55-shjb6:   0%|          | 0.00/22.1k [00:00<?, …"
+       "Applying chat template to eval dataset:   0%|          | 0/10 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -1076,12 +903,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ef5dab829e8b491581f6dae2b7718113",
+       "model_id": "b1acbd0589c54b5887ea7b3e8b872057",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739724308.r-jofthomas-fttest-0ihwmg95-70a55-shjb6:   0%|          | 0.00/21.5k [00:00<?, …"
+       "Tokenizing eval dataset:   0%|          | 0/10 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
@@ -1090,40 +917,138 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "97ba6384a2f94db0880a17ff433a8ed9",
+       "model_id": "24a119a8500146f28f3d866ed69f7161",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "Upload 10 LFS files:   0%|          | 0/10 [00:00<?, ?it/s]"
+       "Packing eval dataset:   0%|          | 0/10 [00:00<?, ? examples/s]"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "trainer = SFTTrainer(\n",
+    "    model=model,\n",
+    "    args=training_arguments,\n",
+    "    train_dataset=dataset[\"train\"],\n",
+    "    eval_dataset=dataset[\"test\"],\n",
+    "    processing_class=tokenizer,\n",
+    "    peft_config=peft_config,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "MtHjukK9xviB",
+   "metadata": {
+    "id": "MtHjukK9xviB"
+   },
+   "source": [
+    "Here, we launch the training 🔥. Perfect time for you to pause and grab a coffee ☕."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "9e2df2e9-a82b-4540-aa89-1b40b70a7781",
+   "metadata": {
+    "colab": {
+     "base_uri": "https://localhost:8080/",
+     "height": 92
+    },
+    "id": "9e2df2e9-a82b-4540-aa89-1b40b70a7781",
+    "outputId": "113322de-8f42-4285-d58f-8641e6daf295",
+    "tags": []
+   },
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`.\n"
+     ]
     },
     {
      "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "ba0c0c53b23047ac9336fdbf8597f32f",
-       "version_major": 2,
-       "version_minor": 0
-      },
+      "text/html": [
+       "\n",
+       "    <div>\n",
+       "      \n",
+       "      <progress value='12' max='12' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+       "      [12/12 00:24, Epoch 1/1]\n",
+       "    </div>\n",
+       "    <table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       " <tr style=\"text-align: left;\">\n",
+       "      <th>Epoch</th>\n",
+       "      <th>Training Loss</th>\n",
+       "      <th>Validation Loss</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <td>1</td>\n",
+       "      <td>1.236800</td>\n",
+       "      <td>1.240833</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table><p>"
+      ],
       "text/plain": [
-       "events.out.tfevents.1739727155.r-jofthomas-fttest-0ihwmg95-70a55-shjb6:   0%|          | 0.00/22.1k [00:00<?, …"
+       "<IPython.core.display.HTML object>"
       ]
      },
      "metadata": {},
      "output_type": "display_data"
     },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/user/miniconda/lib/python3.9/site-packages/peft/utils/save_and_load.py:230: UserWarning: Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\n",
+      "  warnings.warn(\"Setting `save_embedding_layers` to `True` as embedding layers found in `target_modules`.\")\n"
+     ]
+    }
+   ],
+   "source": [
+    "trainer.train()\n",
+    "trainer.save_model()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393",
+   "metadata": {
+    "id": "1d7ea3ab-7c8c-47ad-acd2-99fbe5b68393",
+    "tags": []
+   },
+   "source": [
+    "## Step 11: Let's push the Model and the Tokenizer to the Hub\n",
+    "\n",
+    "Let's push our model and out tokenizer to the Hub ! The model will be pushed under your username + the output_dir that we specified earlier."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "370af020-9319-4ff7-bea1-2842a4847caa",
+   "metadata": {
+    "id": "370af020-9319-4ff7-bea1-2842a4847caa",
+    "tags": []
+   },
+   "outputs": [
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "8a033d5bd32b4a969fd5af612c550243",
+       "model_id": "00c6c786a8014635952d94bb505923f1",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739809832.r-jofthomas-fttest-x95brwd3-5f8b4-w4aox:   0%|          | 0.00/22.5k [00:00<?, …"
+       "events.out.tfevents.1739887545.r-jofthomas-fttest-kff5bkw4-24c03-yhiku:   0%|          | 0.00/6.88k [00:00<?, …"
       ]
      },
      "metadata": {},
@@ -1132,12 +1057,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "714d4a40a67a4763ba8f7ae029befbd7",
+       "model_id": "6945a9a4ba05483e94061404480e2d4f",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739860009.r-jofthomas-fttest-8up4ewpe-95503-rafjw:   0%|          | 0.00/22.5k [00:00<?, …"
+       "training_args.bin:   0%|          | 0.00/5.62k [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -1146,12 +1071,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "52451f392b434fb39b882c9f094bd995",
+       "model_id": "d56b088f78144a3388749363ee944194",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "events.out.tfevents.1739862234.r-jofthomas-fttest-8up4ewpe-95503-rafjw:   0%|          | 0.00/22.5k [00:00<?, …"
+       "tokenizer.json:   0%|          | 0.00/34.4M [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -1160,12 +1085,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "08c2ca8a719e4142bd877ae94d242f2e",
+       "model_id": "eb6e24851a254a1b81a2032541453ec0",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "tokenizer.json:   0%|          | 0.00/34.4M [00:00<?, ?B/s]"
+       "adapter_model.safetensors:   0%|          | 0.00/2.48G [00:00<?, ?B/s]"
       ]
      },
      "metadata": {},
@@ -1174,12 +1099,12 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "02e660bd44a7414fae439751e9ffa1f2",
+       "model_id": "6f268fc2255c46ac99cf15212035009c",
        "version_major": 2,
        "version_minor": 0
       },
       "text/plain": [
-       "training_args.bin:   0%|          | 0.00/5.69k [00:00<?, ?B/s]"
+       "Upload 4 LFS files:   0%|          | 0/4 [00:00<?, ?it/s]"
       ]
      },
      "metadata": {},
@@ -1188,7 +1113,7 @@
     {
      "data": {
       "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling/commit/1fd13c76657670ca45620b6893e4fbfda0207a91', commit_message='End of training', commit_description='', oid='1fd13c76657670ca45620b6893e4fbfda0207a91', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling', endpoint='https://huggingface.co', repo_type='model', repo_id='Jofthomas/gemma-2-2B-it-thinking-function_calling'), pr_revision=None, pr_num=None)"
+       "CommitInfo(commit_url='https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling-V0/commit/74db7b9fd8e5c6591db5851db70069bf3016fe50', commit_message='Jofthomas/gemma-2-2B-it-thinking-function_calling-V0', commit_description='', oid='74db7b9fd8e5c6591db5851db70069bf3016fe50', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling-V0', endpoint='https://huggingface.co', repo_type='model', repo_id='Jofthomas/gemma-2-2B-it-thinking-function_calling-V0'), pr_revision=None, pr_num=None)"
       ]
      },
      "execution_count": 11,
@@ -1197,7 +1122,7 @@
     }
    ],
    "source": [
-    "trainer.push_to_hub()"
+    "trainer.push_to_hub(f\"{username}/{output_dir}\")"
    ]
   },
   {
@@ -1212,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 12,
    "id": "9d9a86b3-f23d-4060-a97f-b868a7c38c36",
    "metadata": {
     "id": "9d9a86b3-f23d-4060-a97f-b868a7c38c36",
@@ -1221,19 +1146,26 @@
    },
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "No files have been modified since last commit. Skipping to prevent empty commit.\n"
-     ]
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "1a84942726104c7a881b352df849997e",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "README.md:   0%|          | 0.00/1.53k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
     },
     {
      "data": {
       "text/plain": [
-       "CommitInfo(commit_url='https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling/commit/50ea3ee78ed458c6d773f53b326531becdda0211', commit_message='Upload tokenizer', commit_description='', oid='50ea3ee78ed458c6d773f53b326531becdda0211', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling', endpoint='https://huggingface.co', repo_type='model', repo_id='Jofthomas/gemma-2-2B-it-thinking-function_calling'), pr_revision=None, pr_num=None)"
+       "CommitInfo(commit_url='https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling-V0/commit/5eae94697866df5f9f52bc7c7be677f100a7f339', commit_message='Upload tokenizer', commit_description='', oid='5eae94697866df5f9f52bc7c7be677f100a7f339', pr_url=None, repo_url=RepoUrl('https://huggingface.co/Jofthomas/gemma-2-2B-it-thinking-function_calling-V0', endpoint='https://huggingface.co', repo_type='model', repo_id='Jofthomas/gemma-2-2B-it-thinking-function_calling-V0'), pr_revision=None, pr_num=None)"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 12,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1241,7 +1173,7 @@
    "source": [
     "tokenizer.eos_token = \"<eos>\"\n",
     "# push the tokenizer to hub ( replace with your username and your previously specified\n",
-    "tokenizer.push_to_hub(f\"username/{output_dir}\", token=True)"
+    "tokenizer.push_to_hub(f\"{username}/{output_dir}\", token=True)"
    ]
   },
   {
@@ -1257,12 +1189,12 @@
     "\n",
     "1. Load the adapter from the hub !\n",
     "2. Load the base model : **\"google/gemma-2-2b-it\"** from the hub\n",
-    "3."
+    "3. Resize the model to with the new tokens we introduced !"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "id": "56b89825-70ac-42c1-934c-26e2d54f3b7b",
    "metadata": {
     "colab": {
@@ -1280,7 +1212,7 @@
     {
      "data": {
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+       "model_id": "4c2546e08a424179af511d8abe3c1c7d",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1294,7 +1226,7 @@
     {
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+       "model_id": "e08b968b1648474e812283fa7c4358a9",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1308,7 +1240,49 @@
     {
      "data": {
       "application/vnd.jupyter.widget-view+json": {
-       "model_id": "9c313149d4324bdaa9c8ddc373964d18",
+       "model_id": "bec06c857d234a7ba0593a92272ef764",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer_config.json:   0%|          | 0.00/47.9k [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "5608d64f66fa43ef8c162a6e50fb5359",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "tokenizer.json:   0%|          | 0.00/34.4M [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "8fd1767e3a8249f5becdab5ca1ccea89",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "special_tokens_map.json:   0%|          | 0.00/605 [00:00<?, ?B/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "a6173c485914437cb0147583215e8d84",
        "version_major": 2,
        "version_minor": 0
       },
@@ -1478,7 +1452,7 @@
        ")"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1496,7 +1470,7 @@
     "            bnb_4bit_use_double_quant=True,\n",
     "        )\n",
     "\n",
-    "peft_model_id = f\"username/{output_dir}\" # replace with your newly trained adapter\n",
+    "peft_model_id = f\"{username}/{output_dir}\" # replace with your newly trained adapter\n",
     "device = \"auto\"\n",
     "config = PeftConfig.from_pretrained(peft_model_id)\n",
     "model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path,\n",
@@ -1511,7 +1485,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 20,
    "id": "69e83af9-f967-4e5a-842b-0daed13f7957",
    "metadata": {
     "id": "69e83af9-f967-4e5a-842b-0daed13f7957",
@@ -1577,7 +1551,7 @@
     }
    ],
    "source": [
-    "print(dataset[\"test\"][8][\"content\"])"
+    "print(dataset[\"test\"][8][\"text\"])"
    ]
   },
   {
@@ -1601,7 +1575,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 21,
    "id": "37bf938d-08fa-4577-9966-0238339afcdb",
    "metadata": {
     "id": "37bf938d-08fa-4577-9966-0238339afcdb",
@@ -1621,7 +1595,11 @@
       "\n",
       "Hi, I need to convert 500 USD to Euros. Can you help me with that?<end_of_turn><eos>\n",
       "<start_of_turn>model\n",
-      "<think>Okay, so the user is asking to convert 500 USD to Euros. I need to figure out how to respond. Looking at the available tools, there's a function called convert_currency which does exactly that. It takes an amount, the source currency, and the target currency. The user provided all the necessary details: 500, USD, and EUR. So, I should call convert_currency with these parameters. That should give the user the converted amount they need.\n",
+      "<think>Okay, so the user is asking to convert 500 USD to Euros. I need to figure out how to respond using the available functions. Let me look at the tools provided. There's a function called convert_currency which does exactly that—it converts one currency to another. The parameters required are amount, from_currency, and to_currency. \n",
+      "\n",
+      "The user provided the amount as 500, the source currency as USD, and the target currency as EUR. That fits perfectly with the function's parameters. I don't need to make any assumptions here because the user has given all the necessary details. \n",
+      "\n",
+      "So, I should call the convert_currency function with these arguments. That should give the user the converted amount they need.\n",
       "</think><tool_call>\n",
       "{'name': 'convert_currency', 'arguments': {'amount': 500, 'from_currency': 'USD', 'to_currency': 'EUR'}}\n",
       "</tool_call><end_of_turn><eos>\n"
@@ -1643,9 +1621,9 @@
     "inputs = tokenizer(prompt, return_tensors=\"pt\", add_special_tokens=False)\n",
     "inputs = {k: v.to(\"cuda\") for k,v in inputs.items()}\n",
     "outputs = model.generate(**inputs,\n",
-    "                         max_new_tokens=300,\n",
+    "                         max_new_tokens=300,# Adapt as necessary\n",
     "                         do_sample=True,\n",
-    "                         top_p=0.65,\n",
+    "                         top_p=0.95,\n",
     "                         temperature=0.01,\n",
     "                         repetition_penalty=1.0,\n",
     "                         eos_token_id=tokenizer.eos_token_id)\n",
@@ -1673,7 +1651,9 @@
   }
  ],
  "metadata": {
+  "accelerator": "GPU",
   "colab": {
+   "gpuType": "T4",
    "provenance": []
   },
   "kernelspec": {
@@ -1692,6 +1672,4456 @@
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    "pygments_lexer": "ipython3",
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