diff --git a/notebooks/en/fine_tuning_smol_vlm_sft_trl.ipynb b/notebooks/en/fine_tuning_smol_vlm_sft_trl.ipynb
index 2931b7d1..5645da68 100644
--- a/notebooks/en/fine_tuning_smol_vlm_sft_trl.ipynb
+++ b/notebooks/en/fine_tuning_smol_vlm_sft_trl.ipynb
@@ -60,23 +60,58 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 1,
       "metadata": {
-        "id": "GCMhPmFdIGSb"
+        "id": "GCMhPmFdIGSb",
+        "outputId": "1d97a52a-72e7-4d88-bad8-ffde22ed79af",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m61.3/61.3 MB\u001b[0m \u001b[31m40.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m504.9/504.9 kB\u001b[0m \u001b[31m38.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m844.5/844.5 kB\u001b[0m \u001b[31m56.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.6/59.6 MB\u001b[0m \u001b[31m40.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m324.6/324.6 kB\u001b[0m \u001b[31m28.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m40.0/40.0 MB\u001b[0m \u001b[31m60.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Building wheel for trl (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
       "source": [
-        "!pip install  -U -q transformers trl datasets bitsandbytes peft accelerate\n",
-        "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, accelerate==1.8.1"
+        "!pip install -U -q git+https://github.com/huggingface/trl.git bitsandbytes peft qwen-vl-utils trackio\n",
+        "# Tested with trl==0.22.0.dev0, bitsandbytes==0.47.0, peft==0.17.1, qwen-vl-utils==0.0.11, trackio==0.2.8"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 2,
       "metadata": {
-        "id": "x6fAqSnKDtKg"
+        "id": "x6fAqSnKDtKg",
+        "outputId": "b3c6909f-ce1e-4c27-d2d5-5dbc0726e4c0",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/8.4 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━\u001b[0m\u001b[91m╸\u001b[0m\u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/8.4 MB\u001b[0m \u001b[31m43.6 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[91m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[91m╸\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m133.9 MB/s\u001b[0m eta \u001b[36m0:00:01\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m8.4/8.4 MB\u001b[0m \u001b[31m92.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Building wheel for flash-attn (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
+          ]
+        }
+      ],
       "source": [
         "!pip install -q flash-attn --no-build-isolation"
       ]
@@ -92,11 +127,53 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 3,
       "metadata": {
-        "id": "xcL4-bwGIoaR"
+        "id": "xcL4-bwGIoaR",
+        "outputId": "5ae2f971-65ee-4fac-b34a-51ea281b1b9a",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 17,
+          "referenced_widgets": [
+            "57c3c655352b45e0813b8c251a157cbc",
+            "a54f4ca184734d6897c373e00a575bc3",
+            "32109ed3388f4cbc9f02e30c06db9559",
+            "9acdca4ab08a4984a65408928798373f",
+            "821d65ae9cc2496da0d0b65adcb9baaa",
+            "b38d957d13a444fdb54bb8f0a76d97f0",
+            "d5a33cb60b20480ba4b8c601d803b7c0",
+            "a63ae0630069403284388d5379d5ed8c",
+            "49b248a7ab97444287c3cefa92cb9fac",
+            "fd009d2a14474f8ca0d7cd063ac18f81",
+            "0851414d08174cd9b1c4772ed1386540",
+            "a0d4a8a10b204ec7a14db3e4680753fb",
+            "025a590f09944d4c928bbfdc0eb6db0e",
+            "438820f803ba44a7b8aeef39b1dfa57a",
+            "fdc33e42f9a1480caf012c1a9a058c8c",
+            "0b86b7406dae472295030fb65e80f282",
+            "5c0f4da623f44a8b99fdc398c80f9772",
+            "85b7c6bed3b04c4795badc845d284f68",
+            "8ca311df3db2490b8c349d877baea6b4",
+            "72569ad9a7784a6bbe45421d62c29f51"
+          ]
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "VBox(children=(HTML(value='<center> <img\\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "57c3c655352b45e0813b8c251a157cbc"
+            }
+          },
+          "metadata": {}
+        }
+      ],
       "source": [
         "from huggingface_hub import notebook_login\n",
         "\n",
@@ -125,7 +202,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 4,
       "metadata": {
         "id": "IBvKAlXhI46X"
       },
@@ -150,14 +227,16 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 5,
       "metadata": {
         "id": "XG8NuzqjjbgI"
       },
       "outputs": [],
       "source": [
         "def format_data(sample):\n",
-        "    return [\n",
+        "    return {\n",
+        "      \"images\": [sample[\"image\"]],\n",
+        "      \"messages\": [\n",
         "        {\n",
         "            \"role\": \"system\",\n",
         "            \"content\": [\n",
@@ -189,7 +268,8 @@
         "                }\n",
         "            ],\n",
         "        },\n",
-        "    ]"
+        "        ]\n",
+        "    }"
       ]
     },
     {
@@ -203,11 +283,255 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 6,
       "metadata": {
-        "id": "QFe_A78aIwK8"
+        "id": "QFe_A78aIwK8",
+        "outputId": "e0a45aea-c263-4109-c44b-1fe04fbb4bd5",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 392,
+          "referenced_widgets": [
+            "dc52ab966aca440eade1a2ab40efc6ae",
+            "74f0371e80a04f78a082b50a3af3fe63",
+            "917ff3aad62e48688c485416903becf0",
+            "0f858f537feb4e3a8d8c9266a13aad86",
+            "dd268bddfe624436a6e8896663fbb77d",
+            "043988feffc64e28b2c30467534da61b",
+            "cfa3a1f8ca054764b5e8d67cafb5f7ff",
+            "37cf80aa41cd42dcb431686fa6a9172d",
+            "f9c0f4d2c5be4edcbde510931a216c21",
+            "c0ccdac70bad40eaba1f34a92751b49b",
+            "7cce7c8403c646799f2ad8f80f8243da",
+            "79843551da774cd3a9355e2d767a13eb",
+            "c8ec9d88bde545838334bd0ecc679a5d",
+            "cf1031a9bd6b41ae8dcf5176fe6d605b",
+            "73f2bb5c4ba648a18bfc85e969d421eb",
+            "4072bff11bc64f97971d3fdf2fe8bbaa",
+            "5a57eaf287ed4974bbf9caddb87f01b7",
+            "ef947e31f3b64660b62e7f038675ed9f",
+            "21462112f28746cd8072bea1a9ce19df",
+            "bfae6eca5c5042e49f00f4f6bf5187a6",
+            "4fbd14db00864bb883935787625eda6c",
+            "ba6fc9a0f81440cca564b90e990b5802",
+            "45df35358d80493489dacf90d885e814",
+            "b11345199a304fb090b8d65b486c76a5",
+            "8b02d9d8ba8d42989a7018777111673a",
+            "8080601d5878458c9a687588d87039ce",
+            "4221ad0707f84074b07df29ef9213702",
+            "1e00eaf37a1648f3a46ba0280e66f7c6",
+            "35fac4f28628439a94bd878944bc3f13",
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+            "59810f45918c459e8263115e91b1ea7a",
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+            "7ebf4489d17c4903bb69db881efff663",
+            "6d6ea00d78df4c11be63e8337ec700f9",
+            "5d58ae3c2d4844f294cb4c894d427fad",
+            "d424c5bb9cd44204806101d7ac5e3c23",
+            "09ee0d65773f450a89656e540b81050e",
+            "c91dc2004eea4e249ac756858d644ae5",
+            "f0b4406c75ef42ccaaadab6c94306d6f",
+            "224645e71e884093846d3426b094c5d3",
+            "9b553dd13b434bffa5ae64aa59a297f2",
+            "34aa576c0d5c41599ccb4eb1188d7005",
+            "ba9efc2797ef4952b15291d37b7d894d",
+            "b316253faab2447bbf29deb9be588f4e",
+            "470fe2b41ca44b3096d42d9a54fc17f0",
+            "583e8ed72e794198a6bb33f90804326a"
+          ]
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:86: UserWarning: \n",
+            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
+            "You will not be requested again.\n",
+            "Please restart the session if you want to be prompted again.\n",
+            "  warnings.warn(\n"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "README.md:   0%|          | 0.00/852 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "dc52ab966aca440eade1a2ab40efc6ae"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/train-00000-of-00003-49492f364babfa(…):   0%|          | 0.00/219M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "79843551da774cd3a9355e2d767a13eb"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/train-00001-of-00003-7302bae5e425bb(…):   0%|          | 0.00/311M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "45df35358d80493489dacf90d885e814"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/train-00002-of-00003-194c9400785577(…):   0%|          | 0.00/315M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c7bd388b60874c77bd39c20527f5671c"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/val-00000-of-00001-0f11003c77497969(…):   0%|          | 0.00/50.2M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "48a2c2d1869b48bda94ee6af0a53e7c9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "data/test-00000-of-00001-e2cd0b7a0f9eb20(…):   0%|          | 0.00/68.9M [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "976d2c339d2041ad917f87a23f8b5cf9"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating train split:   0%|          | 0/28299 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "eeabecc50dcf446ebd72f58593187fd4"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating val split:   0%|          | 0/1920 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "de91d2aa88e14c378f7a9b4db5857c3a"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "Generating test split:   0%|          | 0/2500 [00:00<?, ? examples/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "d424c5bb9cd44204806101d7ac5e3c23"
+            }
+          },
+          "metadata": {}
+        }
+      ],
       "source": [
         "from datasets import load_dataset\n",
         "\n",
@@ -226,16 +550,17 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 7,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "o2UKZj15jGwv",
-        "outputId": "773e9dc2-4477-47e1-8992-7e777c786a0c"
+        "outputId": "09d64b24-60fc-47b1-d9a9-2bc9e017fea5"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "Dataset({\n",
@@ -244,9 +569,8 @@
               "})"
             ]
           },
-          "execution_count": 7,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 7
         }
       ],
       "source": [
@@ -264,7 +588,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 8,
       "metadata": {
         "id": "oSHNqk0dkxii"
       },
@@ -277,32 +601,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "dlp3WnfEuftT",
-        "outputId": "7bf3b2df-005a-4960-b7f8-07b7c24b560e"
+        "outputId": "71e212ad-2ca4-4b70-a4c0-c7aed879195f"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[{'role': 'system',\n",
-              "  'content': [{'type': 'text',\n",
-              "    'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
-              " {'role': 'user',\n",
-              "  'content': [{'type': 'image',\n",
-              "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>},\n",
-              "   {'type': 'text',\n",
-              "    'text': 'Is the rightmost value of light brown graph 58?'}]},\n",
-              " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'No'}]}]"
+              "{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>],\n",
+              " 'messages': [{'role': 'system',\n",
+              "   'content': [{'type': 'text',\n",
+              "     'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+              "  {'role': 'user',\n",
+              "   'content': [{'type': 'image',\n",
+              "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>},\n",
+              "    {'type': 'text',\n",
+              "     'text': 'Is the rightmost value of light brown graph 58?'}]},\n",
+              "  {'role': 'assistant', 'content': [{'type': 'text', 'text': 'No'}]}]}"
             ]
           },
-          "execution_count": 9,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 9
         }
       ],
       "source": [
@@ -333,7 +658,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 10,
       "metadata": {
         "id": "PCJhM6tCw4lq"
       },
@@ -356,11 +681,319 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 11,
       "metadata": {
-        "id": "awtjIq86JfFF"
+        "id": "awtjIq86JfFF",
+        "outputId": "8bfa041f-2940-4193-f128-958ecdee6599",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 401,
+          "referenced_widgets": [
+            "43b9733d0bd54b258b1062ce092417d0",
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+            "a2128425091840cf981d59dec19e2f6c",
+            "6ba2009d98ce4daab254cba4ad4d9525",
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+            "fd90867dbcc54941a1846e0b491e2a07",
+            "6c2cf6adec9744db9072f34f3c2fec2b",
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+            "31777b234d53456bafbdc96a88ff22ba",
+            "98bcf6b39e044a1496a7fc2155fee6f4",
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+            "10ed0e7564e349da94c7d9fc7f4d826c",
+            "ca6d81268cb24c2fbb8026837bd0ceee",
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+            "3ef60c6016684b95981d3ae4d6917192",
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+            "8d038e3fdafe4ee5bcc2ded547ad99c2",
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+            "76b10f7f54c949ee862a6ef3585d4921",
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+            "2519a7e0b06545daadddc8d7328964c5",
+            "0b3d3e69e4744d3e82017c2c9e76cb69",
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+            "71f70c207b11430d80201d5bce3b37db",
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+            "0fcf23ef0a38499da74c8134a42e5727",
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+            "0dfeca97157b49f2ae3e21b42a5ef280",
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+            "43e5380ab65d4aa5ae25a89f27a63c6a",
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+            "541602421c2d44bf81b1c8f72e03fe6a",
+            "9bd2b7641e7c4a65b8fb780e2003112c",
+            "93c1a0270eb5414f93a6a6a125b1cfeb",
+            "60b77fde254c4c16b3a5c81b9940c33c",
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+            "d8b4508a30424b3fab36aa0fd1100ee6",
+            "5be84b96f0dc4177bd52f14ae20886c8",
+            "b7dba8b233554119a4c4eed455be1206",
+            "e3044b3bb89549578930633c0104c456",
+            "c2c4d50d14ec4d7784ea1f304539d1b1"
+          ]
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "config.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "43b9733d0bd54b258b1062ce092417d0"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "model.safetensors:   0%|          | 0.00/4.49G [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "c6523d59711148cc96748b5fe5911b5b"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "generation_config.json:   0%|          | 0.00/136 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3427970d8bc0470ba7c67dad72fdb926"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "processor_config.json:   0%|          | 0.00/68.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "3736975dd8194ece8f6e484b4c9dafdd"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "chat_template.json:   0%|          | 0.00/429 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "b188ea0e5be8488396c9f61fc4212d16"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "preprocessor_config.json:   0%|          | 0.00/486 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "6b9e5b9ce7314994a37f708e96806c32"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer_config.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2a1294734f5f46dd823afc0c114849f8"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "vocab.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "49842653b5f34b8daefdef25e881b3a2"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "merges.txt: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "2519a7e0b06545daadddc8d7328964c5"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "tokenizer.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "833bcaf788c942fd88f8837706619199"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "added_tokens.json:   0%|          | 0.00/92.0 [00:00<?, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "0409cda8b4c645a39d7a4265aa00bbfa"
+            }
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "special_tokens_map.json: 0.00B [00:00, ?B/s]"
+            ],
+            "application/vnd.jupyter.widget-view+json": {
+              "version_major": 2,
+              "version_minor": 0,
+              "model_id": "652997c65d90476ea50003fcabcfbe73"
+            }
+          },
+          "metadata": {}
+        }
+      ],
       "source": [
         "model = Idefics3ForConditionalGeneration.from_pretrained(\n",
         "    model_id,\n",
@@ -383,32 +1016,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "i-eIIdL9lqJJ",
-        "outputId": "30cbff2c-49f1-4316-9636-475b5c6697ff"
+        "outputId": "539c7e58-9975-437f-83f2-8e6743aace83"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
-              "[{'role': 'system',\n",
-              "  'content': [{'type': 'text',\n",
-              "    'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
-              " {'role': 'user',\n",
-              "  'content': [{'type': 'image',\n",
-              "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>},\n",
-              "   {'type': 'text',\n",
-              "    'text': 'How many values are below 40 in Unfavorable graph?'}]},\n",
-              " {'role': 'assistant', 'content': [{'type': 'text', 'text': '6'}]}]"
+              "{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>],\n",
+              " 'messages': [{'role': 'system',\n",
+              "   'content': [{'type': 'text',\n",
+              "     'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+              "  {'role': 'user',\n",
+              "   'content': [{'type': 'image',\n",
+              "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>},\n",
+              "    {'type': 'text',\n",
+              "     'text': 'How many values are below 40 in Unfavorable graph?'}]},\n",
+              "  {'role': 'assistant', 'content': [{'type': 'text', 'text': '6'}]}]}"
             ]
           },
-          "execution_count": 12,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 12
         }
       ],
       "source": [
@@ -426,32 +1060,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 13,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "Ytnr1rePOamM",
-        "outputId": "37ccf628-da62-448f-a45b-3c135928bca2"
+        "outputId": "3900da91-7859-4833-b104-1c7e45ce9293"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
             "text/plain": [
               "[{'role': 'user',\n",
               "  'content': [{'type': 'image',\n",
               "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>},\n",
-              "   {'type': 'text',\n",
-              "    'text': 'How many values are below 40 in Unfavorable graph?'}]}]"
+              "   {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
             ]
           },
-          "execution_count": 13,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 13
         }
       ],
       "source": [
-        "train_dataset[1][1:2]"
+        "train_dataset[0]['messages'][1:2]"
       ]
     },
     {
@@ -465,31 +1098,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 376
         },
         "id": "QavnLzjJUbxf",
-        "outputId": "e2bc3d15-e2e9-45bf-a848-8d8bb41c0530"
+        "outputId": "37fd67d5-bf2d-4e39-fb21-cc753d50c81e"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
-            "image/jpeg": 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",
             "text/plain": [
               "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>"
-            ]
+            ],
+            "image/png": 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\n",
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\n"
           },
-          "execution_count": 14,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 14
         }
       ],
       "source": [
-        "train_dataset[1][1]['content'][0]['image']"
+        "train_dataset[0]['images'][0]"
       ]
     },
     {
@@ -503,7 +1136,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 15,
       "metadata": {
         "id": "_MoRTjFcE8qD"
       },
@@ -512,12 +1145,13 @@
         "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
         "    # Prepare the text input by applying the chat template\n",
         "    text_input = processor.apply_chat_template(\n",
-        "        sample[1:2],  # Use the sample without the system message\n",
+        "        sample['messages'][1:2],  # Use the sample without the system message\n",
         "        add_generation_prompt=True\n",
         "    )\n",
         "\n",
         "    image_inputs = []\n",
-        "    image = sample[1]['content'][0]['image']\n",
+        "    image = sample['images'][0]\n",
+        "    #image = sample[1]['content'][0]['image']\n",
         "    if image.mode != 'RGB':\n",
         "        image = image.convert('RGB')\n",
         "    image_inputs.append([image])\n",
@@ -550,28 +1184,28 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 36
+          "height": 35
         },
         "id": "5UeNiMJC_uCk",
-        "outputId": "d0d39070-4221-4c38-928d-75ebac8a3479"
+        "outputId": "c01449fb-c4c1-4443-eea7-44346541e161"
       },
       "outputs": [
         {
+          "output_type": "execute_result",
           "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
             "text/plain": [
               "' 3.'"
-            ]
+            ],
+            "application/vnd.google.colaboratory.intrinsic+json": {
+              "type": "string"
+            }
           },
-          "execution_count": 16,
           "metadata": {},
-          "output_type": "execute_result"
+          "execution_count": 16
         }
       ],
       "source": [
@@ -602,21 +1236,21 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 17,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "dxkXZuUkvy8j",
-        "outputId": "d75c1c7b-04d0-468c-8d45-9a7b3e7f5426"
+        "outputId": "ec2fbf9b-44e2-4d2f-96f2-a1cc572a64a0"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "GPU allocated memory: 0.01 GB\n",
-            "GPU reserved memory: 0.06 GB\n"
+            "GPU reserved memory: 0.02 GB\n"
           ]
         }
       ],
@@ -671,7 +1305,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 18,
       "metadata": {
         "id": "zm_bJRrXsESg"
       },
@@ -713,18 +1347,18 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 19,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "ITmkRHWCKYjf",
-        "outputId": "cd621500-ce1f-470d-d1a8-21ab026c36cb"
+        "outputId": "9c76485a-f6fa-409c-bd6e-3f0058ee0842"
       },
       "outputs": [
         {
-          "name": "stdout",
           "output_type": "stream",
+          "name": "stdout",
           "text": [
             "trainable params: 11,269,248 || all params: 2,257,542,128 || trainable%: 0.4992\n"
           ]
@@ -761,7 +1395,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 27,
       "metadata": {
         "id": "SbqX1pQUKaSM"
       },
@@ -785,63 +1419,61 @@
         "    optim=\"adamw_torch_fused\",\n",
         "    bf16=True,\n",
         "    push_to_hub=True,\n",
-        "    report_to=\"tensorboard\",\n",
-        "    remove_unused_columns=False,\n",
-        "    gradient_checkpointing=True,\n",
-        "    dataset_text_field=\"\",\n",
-        "    dataset_kwargs={\"skip_prepare_dataset\": True},\n",
+        "    report_to=\"none\",\n",
+        "    max_length=None\n",
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "pOUrD9P-y-Kf"
-      },
-      "source": [
-        "### 4.3 Training the Model 🏃"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ucTUbGURV2_-"
-      },
-      "source": [
-        "To ensure that the data is correctly structured for the model during training, we need to define a collator function. This function will handle the formatting and batching of our dataset inputs, ensuring the data is properly aligned for training.\n",
-        "\n",
-        "👉 For more details, check out the official [TRL example scripts](https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm_smol_vlm.py)."
-      ]
-    },
     {
       "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "id": "pAzDovzylQeZ"
-      },
-      "outputs": [],
       "source": [
-        "image_token_id = processor.tokenizer.additional_special_tokens_ids[\n",
-        "            processor.tokenizer.additional_special_tokens.index(\"<image>\")]\n",
-        "\n",
-        "def collate_fn(examples):\n",
-        "    texts = [processor.apply_chat_template(example, tokenize=False) for example in examples]\n",
+        "import trackio\n",
         "\n",
-        "    image_inputs = []\n",
-        "    for example in examples:\n",
-        "      image = example[1]['content'][0]['image']\n",
-        "      if image.mode != 'RGB':\n",
-        "          image = image.convert('RGB')\n",
-        "      image_inputs.append([image])\n",
-        "\n",
-        "    batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True)\n",
-        "    labels = batch[\"input_ids\"].clone()\n",
-        "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
-        "    labels[labels == image_token_id] = -100  # Mask image token IDs in labels\n",
-        "\n",
-        "    batch[\"labels\"] = labels\n",
-        "\n",
-        "    return batch"
+        "trackio.init(\n",
+        "    project=\"smolvlm-instruct-trl-sft-ChartQA\",\n",
+        "    name=\"smolvlm-instruct-trl-sft-ChartQA\",\n",
+        "    config=training_args,\n",
+        "    space_id=training_args.output_dir + \"-trackio\"\n",
+        ")"
+      ],
+      "metadata": {
+        "id": "UCF7MW074p4r",
+        "outputId": "63a4bf4d-4811-449d-87c8-622f1755bd36",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
+      },
+      "execution_count": 21,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "* Trackio project initialized: smolvlm-instruct-trl-sft-ChartQA\n",
+            "* Trackio metrics will be synced to Hugging Face Dataset: sergiopaniego/smolvlm-instruct-trl-sft-ChartQA-trackio-dataset\n",
+            "* Creating new space: https://huggingface.co/spaces/sergiopaniego/smolvlm-instruct-trl-sft-ChartQA-trackio\n",
+            "* View dashboard by going to: https://huggingface.co/spaces/sergiopaniego/smolvlm-instruct-trl-sft-ChartQA-trackio\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<trackio.run.Run at 0x7fcb74bcde50>"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 21
+        }
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "metadata": {
+        "id": "pOUrD9P-y-Kf"
+      },
+      "source": [
+        "### 4.3 Training the Model 🏃"
       ]
     },
     {
@@ -850,17 +1482,32 @@
         "id": "skbpTuJlV8qN"
       },
       "source": [
-        "Now, we will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
+        "We will define the [SFTTrainer](https://huggingface.co/docs/trl/sft_trainer), which is a wrapper around the [transformers.Trainer](https://huggingface.co/docs/transformers/main_classes/trainer) class and inherits its attributes and methods. This class simplifies the fine-tuning process by properly initializing the [PeftModel](https://huggingface.co/docs/peft/v0.6.0/package_reference/peft_model) when a [PeftConfig](https://huggingface.co/docs/peft/v0.6.0/en/package_reference/config#peft.PeftConfig) object is provided. By using `SFTTrainer`, we can efficiently manage the training workflow and ensure a smooth fine-tuning experience for our Vision Language Model.\n",
         "\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 28,
       "metadata": {
-        "id": "k_jk-U7ULYtA"
+        "id": "k_jk-U7ULYtA",
+        "outputId": "f148ffcb-6c2f-4288-f567-21f8a55ccc67",
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        }
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stderr",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/peft/mapping_func.py:73: UserWarning: You are trying to modify a model with PEFT for a second time. If you want to reload the model with a different config, make sure to call `.unload()` before.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.12/dist-packages/peft/tuners/tuners_utils.py:196: UserWarning: Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
         "from trl import SFTTrainer\n",
         "\n",
@@ -869,9 +1516,8 @@
         "    args=training_args,\n",
         "    train_dataset=train_dataset,\n",
         "    eval_dataset=eval_dataset,\n",
-        "    data_collator=collate_fn,\n",
         "    peft_config=peft_config,\n",
-        "    processing_class=processor.tokenizer,\n",
+        "    processing_class=processor,\n",
         ")"
       ]
     },
@@ -890,28 +1536,32 @@
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 610
+          "height": 110
         },
         "id": "p1rgMTBDLboO",
-        "outputId": "9da86b78-a432-459f-94c3-89025d6059ce"
+        "outputId": "191b98fc-e56e-4927-db50-0493cfe31930"
       },
       "outputs": [
         {
-          "name": "stderr",
           "output_type": "stream",
+          "name": "stderr",
           "text": [
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n"
+            "Casting fp32 inputs back to torch.bfloat16 for flash-attn compatibility.\n",
+            "`loss_type=None` was set in the config but it is unrecognised.Using the default loss: `ForCausalLMLoss`.\n"
           ]
         },
         {
+          "output_type": "display_data",
           "data": {
+            "text/plain": [
+              "<IPython.core.display.HTML object>"
+            ],
             "text/html": [
               "\n",
               "    <div>\n",
               "      \n",
-              "      <progress value='177' max='177' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
-              "      [177/177 1:07:11, Epoch 1/1]\n",
+              "      <progress value='3' max='177' style='width:300px; height:20px; vertical-align: middle;'></progress>\n",
+              "      [  3/177 01:23 < 4:01:02, 0.01 it/s, Epoch 0.01/1]\n",
               "    </div>\n",
               "    <table border=\"1\" class=\"dataframe\">\n",
               "  <thead>\n",
@@ -921,73 +1571,11 @@
               "    </tr>\n",
               "  </thead>\n",
               "  <tbody>\n",
-              "    <tr>\n",
-              "      <td>25</td>\n",
-              "      <td>1.645000</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>50</td>\n",
-              "      <td>0.607100</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>75</td>\n",
-              "      <td>0.182700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>100</td>\n",
-              "      <td>0.129700</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>125</td>\n",
-              "      <td>0.111500</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>150</td>\n",
-              "      <td>0.105600</td>\n",
-              "    </tr>\n",
-              "    <tr>\n",
-              "      <td>175</td>\n",
-              "      <td>0.105200</td>\n",
-              "    </tr>\n",
               "  </tbody>\n",
               "</table><p>"
-            ],
-            "text/plain": [
-              "<IPython.core.display.HTML object>"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n",
-            "/usr/local/lib/python3.10/dist-packages/torch/_dynamo/eval_frame.py:632: UserWarning: torch.utils.checkpoint: the use_reentrant parameter should be passed explicitly. In version 2.5 we will raise an exception if use_reentrant is not passed. use_reentrant=False is recommended, but if you need to preserve the current default behavior, you can pass use_reentrant=True. Refer to docs for more details on the differences between the two variants.\n",
-            "  return fn(*args, **kwargs)\n"
-          ]
-        },
-        {
-          "data": {
-            "text/plain": [
-              "TrainOutput(global_step=177, training_loss=0.40889345513562025, metrics={'train_runtime': 4055.974, 'train_samples_per_second': 0.698, 'train_steps_per_second': 0.044, 'total_flos': 5.950609352968704e+16, 'train_loss': 0.40889345513562025, 'epoch': 1.0})"
             ]
           },
-          "execution_count": 40,
-          "metadata": {},
-          "output_type": "execute_result"
+          "metadata": {}
         }
       ],
       "source": [
@@ -1144,7 +1732,7 @@
         }
       ],
       "source": [
-        "test_dataset[20][:2]"
+        "test_dataset[20]['messages'][:2]"
       ]
     },
     {
@@ -1172,7 +1760,7 @@
         }
       ],
       "source": [
-        "test_dataset[20][1]['content'][0]['image']"
+        "test_dataset[20]['images'][0]"
       ]
     },
     {
@@ -1292,8 +1880,7797 @@
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+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": "20px"
+          }
+        },
+        "b7dba8b233554119a4c4eed455be1206": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "ProgressStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "ProgressStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "bar_color": null,
+            "description_width": ""
+          }
+        },
+        "e3044b3bb89549578930633c0104c456": {
+          "model_module": "@jupyter-widgets/base",
+          "model_name": "LayoutModel",
+          "model_module_version": "1.2.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/base",
+            "_model_module_version": "1.2.0",
+            "_model_name": "LayoutModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "LayoutView",
+            "align_content": null,
+            "align_items": null,
+            "align_self": null,
+            "border": null,
+            "bottom": null,
+            "display": null,
+            "flex": null,
+            "flex_flow": null,
+            "grid_area": null,
+            "grid_auto_columns": null,
+            "grid_auto_flow": null,
+            "grid_auto_rows": null,
+            "grid_column": null,
+            "grid_gap": null,
+            "grid_row": null,
+            "grid_template_areas": null,
+            "grid_template_columns": null,
+            "grid_template_rows": null,
+            "height": null,
+            "justify_content": null,
+            "justify_items": null,
+            "left": null,
+            "margin": null,
+            "max_height": null,
+            "max_width": null,
+            "min_height": null,
+            "min_width": null,
+            "object_fit": null,
+            "object_position": null,
+            "order": null,
+            "overflow": null,
+            "overflow_x": null,
+            "overflow_y": null,
+            "padding": null,
+            "right": null,
+            "top": null,
+            "visibility": null,
+            "width": null
+          }
+        },
+        "c2c4d50d14ec4d7784ea1f304539d1b1": {
+          "model_module": "@jupyter-widgets/controls",
+          "model_name": "DescriptionStyleModel",
+          "model_module_version": "1.5.0",
+          "state": {
+            "_model_module": "@jupyter-widgets/controls",
+            "_model_module_version": "1.5.0",
+            "_model_name": "DescriptionStyleModel",
+            "_view_count": null,
+            "_view_module": "@jupyter-widgets/base",
+            "_view_module_version": "1.2.0",
+            "_view_name": "StyleView",
+            "description_width": ""
+          }
+        }
+      }
     }
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
+}
\ No newline at end of file
diff --git a/notebooks/en/fine_tuning_vlm_trl.ipynb b/notebooks/en/fine_tuning_vlm_trl.ipynb
index a6249bcf..4981a362 100644
--- a/notebooks/en/fine_tuning_vlm_trl.ipynb
+++ b/notebooks/en/fine_tuning_vlm_trl.ipynb
@@ -58,42 +58,32 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "GCMhPmFdIGSb",
-        "outputId": "016a9f29-9c8d-42c5-9187-813f5bdeb536"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install  -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
-        "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
-      ]
-    },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "J4pAvoQaOJ1M"
-      },
-      "source": [
-        "We’ll also need to install an earlier version of *PyTorch*, as the latest version has an issue that currently prevents this notebook from running correctly. You can learn more about the issue [here](https://github.com/pytorch/pytorch/issues/138340) and consider updating to the latest version once it’s resolved."
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "D8iRteA4oXVj",
-        "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
+        "outputId": "b08f3edd-03e2-42af-a075-04dafa232c66"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
+            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
+            "  Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m844.5/844.5 kB\u001b[0m \u001b[31m15.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m59.6/59.6 MB\u001b[0m \u001b[31m43.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m324.6/324.6 kB\u001b[0m \u001b[31m30.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
+            "\u001b[?25h"
+          ]
+        }
+      ],
       "source": [
-        "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
+        "!pip install -U -q git+https://github.com/huggingface/trl.git bitsandbytes peft qwen-vl-utils trackio\n",
+        "# Tested with trl==0.22.0.dev0, bitsandbytes==0.47.0, peft==0.17.1, qwen-vl-utils==0.0.11, trackio==0.2.8"
       ]
     },
     {
@@ -109,54 +99,42 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 3,
+      "execution_count": 2,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 145,
+          "height": 17,
           "referenced_widgets": [
-            "2b562e85757a4a0db226004bf31b84f3",
-            "4bd8e36864194b7fa72577e278b3bb0e",
-            "0f66116815944c6fa063d680eaf8cf04",
-            "ae55dc6dbf6342ea8fb15640159b2b6f",
-            "4f1dabaf2f704bc3bef0367bd8bce4d3",
-            "9f47816dd7dd4e67bc70e422053b23ed",
-            "8e228fa888624e719fa707fa1de833b6",
-            "67affbc55ed1418ca3b93db41d6293ea",
-            "feb02377d8e04fec9895fe4981801dff",
-            "0358ea78f0d8400f876ea9c6da32fced",
-            "b85503523ec64927b0f6156f327ad4cb",
-            "2146d2c7e78f4362a7ec6633be638014",
-            "1fb884636b054f62bb6ed57800ed0472",
-            "e6f413f66e6444b99c8577050455b931",
-            "82f4a474ec4044fda5c3b53a0e84f618",
-            "7dca4a9c89bb4a26ab9e8d57f29f0adb",
-            "ca525f636cde4c6db9bdd3de3a5d183b",
-            "74655b6b2f0a49d78b900e3b6d90b286",
-            "c17a2f3b28ae4a098df567f16898339b",
-            "e3016dfef4a844a083847d475d227640",
-            "2a8fdef853094279abb354407621aff0",
-            "801208c2269d4c03946378bc149da255",
-            "cbf7332ded8e49af9ba44b330e415b1a",
-            "7843a3cdc9d942b39da7ff3862dedf15",
-            "6dd5e55d26e34078bdaafe73e704e3ff",
-            "9189c4f61b2e4f78ae6d14044227f4e1",
-            "a05c7afc53b5426aae66469cdcf412d7",
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-            "8b6b3b39fd2c449a8bfffa51352bf5cb",
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-            "2682fb8a90bd452c975246341a492fc9",
-            "0ad6d738673e445a83cefbd5ec07a3f2"
+            "3d7dbcc18c1b4dceae74bdfb75a8da8e",
+            "b2ec4b8bf53245c88cb74e59612713fb",
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+            "23511e8b562e4f7e99aa2854a9d98c17",
+            "76467e7df06e43cdb2a76c9ecc0f8c15",
+            "582995cfeaf44e118d498cb7ccfd857a",
+            "8a732dcf3d1e4664b99b6f68df75ae17",
+            "23d8b425314a447b8239de0b294e5db9",
+            "30c9d00326c04024a9a171a83352281b",
+            "4cc0e10451564ae190dbe249b15ca94d",
+            "ddaddfb293174a79840350d5c5d98a3e",
+            "a2e17e3dcd684ac89e09de8fa4082e90",
+            "efbd24054785495f9c521b097d487b83",
+            "0bf82ddbeeab48eab8f77dd8d99071c1"
           ]
         },
         "id": "xcL4-bwGIoaR",
-        "outputId": "0cd5d7d0-c570-413b-a4c0-7d14c15f0fc5"
+        "outputId": "289d8507-dde1-46c4-e6ad-eb1d072cc868"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "2b562e85757a4a0db226004bf31b84f3",
+              "model_id": "3d7dbcc18c1b4dceae74bdfb75a8da8e",
               "version_major": 2,
               "version_minor": 0
             },
@@ -198,7 +176,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 4,
+      "execution_count": 1,
       "metadata": {
         "id": "IBvKAlXhI46X"
       },
@@ -223,46 +201,50 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": 2,
       "metadata": {
         "id": "XG8NuzqjjbgI"
       },
       "outputs": [],
       "source": [
         "def format_data(sample):\n",
-        "    return [\n",
-        "        {\n",
-        "            \"role\": \"system\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": system_message\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "        {\n",
-        "            \"role\": \"user\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"image\",\n",
-        "                    \"image\": sample[\"image\"],\n",
-        "                },\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": sample['query'],\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "        {\n",
-        "            \"role\": \"assistant\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": sample[\"label\"][0]\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "    ]"
+        "    return {\n",
+        "      \"images\": [sample[\"image\"]],\n",
+        "      \"messages\": [\n",
+        "\n",
+        "          {\n",
+        "              \"role\": \"system\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": system_message\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "          {\n",
+        "              \"role\": \"user\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"image\",\n",
+        "                      \"image\": sample[\"image\"],\n",
+        "                  },\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": sample['query'],\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "          {\n",
+        "              \"role\": \"assistant\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": sample[\"label\"][0]\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "      ]\n",
+        "      }"
       ]
     },
     {
@@ -276,11 +258,27 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 91,
+      "execution_count": 3,
       "metadata": {
-        "id": "QFe_A78aIwK8"
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "QFe_A78aIwK8",
+        "outputId": "70d76cfc-ca6f-4dd0-b739-665df6270a9f"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/huggingface_hub/utils/_auth.py:86: UserWarning: \n",
+            "Access to the secret `HF_TOKEN` has not been granted on this notebook.\n",
+            "You will not be requested again.\n",
+            "Please restart the session if you want to be prompted again.\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
         "from datasets import load_dataset\n",
         "\n",
@@ -299,13 +297,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 76,
+      "execution_count": 4,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "o2UKZj15jGwv",
-        "outputId": "abe2d01c-f4d2-4b57-f610-2b3aaa000957"
+        "outputId": "4ff1f9b3-7c77-4812-d9e8-e6719018b7af"
       },
       "outputs": [
         {
@@ -317,7 +315,7 @@
               "})"
             ]
           },
-          "execution_count": 76,
+          "execution_count": 4,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -337,7 +335,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 92,
+      "execution_count": 5,
       "metadata": {
         "id": "oSHNqk0dkxii"
       },
@@ -350,30 +348,31 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 78,
+      "execution_count": 6,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "dlp3WnfEuftT",
-        "outputId": "00fb0983-d4a3-49d0-e52c-02ba7d2af316"
+        "outputId": "f9723cfa-c6bd-4908-b89e-e41e60c2b1e9"
       },
       "outputs": [
         {
           "data": {
             "text/plain": [
-              "[{'role': 'system',\n",
-              "  'content': [{'type': 'text',\n",
-              "    'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
-              " {'role': 'user',\n",
-              "  'content': [{'type': 'image',\n",
-              "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>},\n",
-              "   {'type': 'text',\n",
-              "    'text': 'Is the rightmost value of light brown graph 58?'}]},\n",
-              " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'No'}]}]"
+              "{'images': [<PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>],\n",
+              " 'messages': [{'role': 'system',\n",
+              "   'content': [{'type': 'text',\n",
+              "     'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
+              "  {'role': 'user',\n",
+              "   'content': [{'type': 'image',\n",
+              "     'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=308x369>},\n",
+              "    {'type': 'text',\n",
+              "     'text': 'Is the rightmost value of light brown graph 58?'}]},\n",
+              "  {'role': 'assistant', 'content': [{'type': 'text', 'text': 'No'}]}]}"
             ]
           },
-          "execution_count": 78,
+          "execution_count": 6,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -404,51 +403,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 15,
+      "execution_count": 7,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 66,
-          "referenced_widgets": [
-            "e4c580dc65364a6b9f5d12e8ca451d0c",
-            "8768e60ff7644ad7a66347308c2b36ce",
-            "d687bac2ab5e45029fa8054dd5588a02",
-            "6c70d253fccc4e329b525011570d2908",
-            "b54e047ff71a4ab4a347f005ee6f7d4b",
-            "6a757d5b9aab427b8ccc53c54d11ec13",
-            "ab75ccbe867a4110b9bc17576cbd265c",
-            "aacdd97a2aab424eb83018d53ec2cb4e",
-            "e25c09bb8b014cde8f4ee8ff13418731",
-            "d770d6f926da42c19752a74a0f27ce00",
-            "918e0002b82741809044ee59ab5ce0b8"
-          ]
-        },
-        "id": "PCJhM6tCw4lq",
-        "outputId": "c77dc342-6d66-4670-8ed8-5fd551bb804d"
+        "id": "PCJhM6tCw4lq"
       },
-      "outputs": [
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "The cache for model files in Transformers v4.22.0 has been updated. Migrating your old cache. This is a one-time only operation. You can interrupt this and resume the migration later on by calling `transformers.utils.move_cache()`.\n"
-          ]
-        },
-        {
-          "data": {
-            "application/vnd.jupyter.widget-view+json": {
-              "model_id": "e4c580dc65364a6b9f5d12e8ca451d0c",
-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "0it [00:00, ?it/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "import torch\n",
         "from transformers import Qwen2VLForConditionalGeneration, Qwen2VLProcessor\n",
@@ -467,426 +426,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 16,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 546,
-          "referenced_widgets": [
-            "6c1f3fe4d65d4e4e966ad3f144eaedcc",
-            "d1575e6242544e9ea1b8a92ea0f15b0c",
-            "2f2a114a42ed4ab0bcc5b56ba14604d6",
-            "316acc66bf404925a71f24ecc9828363",
-            "02b05d314a594abca73348b2c8f8c4ab",
-            "e8bb197da3a443df95f660d73d4c5394",
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-        },
-        {
-          "name": "stderr",
-          "output_type": "stream",
-          "text": [
-            "`Qwen2VLRotaryEmbedding` can now be fully parameterized by passing the model config through the `config` argument. All other arguments will be removed in v4.46\n"
-          ]
-        },
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-            "text/plain": [
-              "Loading checkpoint shards:   0%|          | 0/5 [00:00<?, ?it/s]"
-            ]
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-            "text/plain": [
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-            ]
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-            ]
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-              "version_major": 2,
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-            },
-            "text/plain": [
-              "tokenizer.json:   0%|          | 0.00/7.03M [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        },
-        {
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-              "version_major": 2,
-              "version_minor": 0
-            },
-            "text/plain": [
-              "chat_template.json:   0%|          | 0.00/1.05k [00:00<?, ?B/s]"
-            ]
-          },
-          "metadata": {},
-          "output_type": "display_data"
-        }
-      ],
+      "outputs": [],
       "source": [
         "model = Qwen2VLForConditionalGeneration.from_pretrained(\n",
         "    model_id,\n",
@@ -908,33 +452,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 17,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "i-eIIdL9lqJJ",
-        "outputId": "02eda1d8-f6e8-43e7-85f4-b58e50370da3"
+        "id": "i-eIIdL9lqJJ"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[{'role': 'system',\n",
-              "  'content': [{'type': 'text',\n",
-              "    'text': 'You are a Vision Language Model specialized in interpreting visual data from chart images.\\nYour task is to analyze the provided chart image and respond to queries with concise answers, usually a single word, number, or short phrase.\\nThe charts include a variety of types (e.g., line charts, bar charts) and contain colors, labels, and text.\\nFocus on delivering accurate, succinct answers based on the visual information. Avoid additional explanation unless absolutely necessary.'}]},\n",
-              " {'role': 'user',\n",
-              "  'content': [{'type': 'image',\n",
-              "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>},\n",
-              "   {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]},\n",
-              " {'role': 'assistant', 'content': [{'type': 'text', 'text': 'Yes'}]}]"
-            ]
-          },
-          "execution_count": 17,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "train_dataset[0]"
       ]
@@ -950,31 +472,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "Ytnr1rePOamM",
-        "outputId": "b38d536e-bfa2-49e8-eb0f-22c1c1cbcff2"
+        "id": "Ytnr1rePOamM"
       },
-      "outputs": [
-        {
-          "data": {
-            "text/plain": [
-              "[{'role': 'user',\n",
-              "  'content': [{'type': 'image',\n",
-              "    'image': <PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>},\n",
-              "   {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
-            ]
-          },
-          "execution_count": 18,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
-        "train_dataset[0][1:2]"
+        "train_dataset[0]['messages'][1:2]"
       ]
     },
     {
@@ -988,31 +492,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 376
-        },
-        "id": "QavnLzjJUbxf",
-        "outputId": "0b935e4d-3b13-4676-f3cc-1da64bc828ab"
+        "id": "QavnLzjJUbxf"
       },
-      "outputs": [
-        {
-          "data": {
-            "image/jpeg": 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",
-            "text/plain": [
-              "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>"
-            ]
-          },
-          "execution_count": 19,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
-        "train_dataset[0][1]['content'][0]['image']"
+        "train_dataset[0]['images'][0]"
       ]
     },
     {
@@ -1026,7 +512,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": 11,
       "metadata": {
         "id": "_MoRTjFcE8qD"
       },
@@ -1037,13 +523,13 @@
         "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
         "    # Prepare the text input by applying the chat template\n",
         "    text_input = processor.apply_chat_template(\n",
-        "        sample[1:2],  # Use the sample without the system message\n",
+        "        sample['messages'][1:2],  # Use the sample without the system message\n",
         "        tokenize=False,\n",
         "        add_generation_prompt=True\n",
         "    )\n",
         "\n",
         "    # Process the visual input from the sample\n",
-        "    image_inputs, _ = process_vision_info(sample)\n",
+        "    image_inputs, _ = process_vision_info(sample['messages'])\n",
         "\n",
         "    # Prepare the inputs for the model\n",
         "    model_inputs = processor(\n",
@@ -1072,30 +558,11 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 26,
+      "execution_count": null,
       "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 36
-        },
-        "id": "5UeNiMJC_uCk",
-        "outputId": "6b7c1260-9980-442f-9d55-a8d7ebedaf94"
+        "id": "5UeNiMJC_uCk"
       },
-      "outputs": [
-        {
-          "data": {
-            "application/vnd.google.colaboratory.intrinsic+json": {
-              "type": "string"
-            },
-            "text/plain": [
-              "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
-            ]
-          },
-          "execution_count": 26,
-          "metadata": {},
-          "output_type": "execute_result"
-        }
-      ],
+      "outputs": [],
       "source": [
         "# Example of how to call the method with sample:\n",
         "output = generate_text_from_sample(model, processor, train_dataset[0])\n",
@@ -1181,33 +648,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 28,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 49,
+          "height": 104,
           "referenced_widgets": [
-            "b4e7dc3a0b6643a1b2c4124a11f1a932",
-            "1065fc621b1f4ae38e021e464ba28340",
-            "d66b6961a23f4c99872521d8b12b3c93",
-            "67c2b6c826b4437ca9f6f354af959e9d",
-            "dd37b7d9cd2a456986ac59ced8a35205",
-            "1bf49b9b487442df874672b30e0af8da",
-            "18e8ee9a09ab46c2833646651409b8b0",
-            "dd477ac59fa6470ca0ef1f59c39f43cb",
-            "df149f2746544b4a8c3cac6a2365e6db",
-            "1b60256c2fa440ccbb085ada32702b1e",
-            "7531040fc6c743e8bb85dcf07eb73560"
+            "b45a7622ec4d4d8986305dcf6d85c0bf",
+            "3ab0220ff74845fbaf546603153f5cb8",
+            "76c3f48584c2454f9ac3e5756d24604f",
+            "f8a009ee72324c3cbe13411430c3f8fb",
+            "2b4f36d611ab419396c9e84c3cb8f399",
+            "e7cbc93b8a284c98bc1e1b1462d9cfb2",
+            "942102ce4e974e41b2361475ca414a93",
+            "2e4557d45259450b9c3a84c186c9bbce",
+            "b8ac9ae1610943d288ed7d8adf144304",
+            "7c9be642c9f9452b94f8cf93e05cdc72",
+            "879673e1f55640849f0ef5310c6b4b11"
           ]
         },
         "id": "zm_bJRrXsESg",
-        "outputId": "5a3ccdc6-9d40-43c8-df9e-9222a0656c2b"
+        "outputId": "7cc9cfd7-c3da-48af-b83c-b93b50e2ffc0"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "b4e7dc3a0b6643a1b2c4124a11f1a932",
+              "model_id": "b45a7622ec4d4d8986305dcf6d85c0bf",
               "version_major": 2,
               "version_minor": 0
             },
@@ -1217,6 +684,14 @@
           },
           "metadata": {},
           "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that this behavior will be extended to all models in a future release.\n",
+            "You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.\n"
+          ]
         }
       ],
       "source": [
@@ -1255,13 +730,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 42,
+      "execution_count": 10,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "ITmkRHWCKYjf",
-        "outputId": "3ca824c9-4aca-4d5b-e942-7a1705939e08"
+        "outputId": "49440aaf-89a4-4810-ad40-eafe4582bab3"
       },
       "outputs": [
         {
@@ -1307,7 +782,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": null,
       "metadata": {
         "id": "SbqX1pQUKaSM"
       },
@@ -1322,37 +797,25 @@
         "    per_device_train_batch_size=4,  # Batch size for training\n",
         "    per_device_eval_batch_size=4,  # Batch size for evaluation\n",
         "    gradient_accumulation_steps=8,  # Steps to accumulate gradients\n",
-        "    gradient_checkpointing=True,  # Enable gradient checkpointing for memory efficiency\n",
+        "    gradient_checkpointing_kwargs={\"use_reentrant\": False},  # Options for gradient checkpointing\n",
+        "    max_length=None,\n",
         "    # Optimizer and scheduler settings\n",
         "    optim=\"adamw_torch_fused\",  # Optimizer type\n",
         "    learning_rate=2e-4,  # Learning rate for training\n",
-        "    lr_scheduler_type=\"constant\",  # Type of learning rate scheduler\n",
         "    # Logging and evaluation\n",
         "    logging_steps=10,  # Steps interval for logging\n",
         "    eval_steps=10,  # Steps interval for evaluation\n",
         "    eval_strategy=\"steps\",  # Strategy for evaluation\n",
         "    save_strategy=\"steps\",  # Strategy for saving the model\n",
         "    save_steps=20,  # Steps interval for saving\n",
-        "    metric_for_best_model=\"eval_loss\",  # Metric to evaluate the best model\n",
-        "    greater_is_better=False,  # Whether higher metric values are better\n",
-        "    load_best_model_at_end=True,  # Load the best model after training\n",
         "    # Mixed precision and gradient settings\n",
         "    bf16=True,  # Use bfloat16 precision\n",
-        "    tf32=True,  # Use TensorFloat-32 precision\n",
         "    max_grad_norm=0.3,  # Maximum norm for gradient clipping\n",
         "    warmup_ratio=0.03,  # Ratio of total steps for warmup\n",
         "    # Hub and reporting\n",
         "    push_to_hub=True,  # Whether to push model to Hugging Face Hub\n",
-        "    report_to=\"wandb\",  # Reporting tool for tracking metrics\n",
-        "    # Gradient checkpointing settings\n",
-        "    gradient_checkpointing_kwargs={\"use_reentrant\": False},  # Options for gradient checkpointing\n",
-        "    # Dataset configuration\n",
-        "    dataset_text_field=\"\",  # Text field in dataset\n",
-        "    dataset_kwargs={\"skip_prepare_dataset\": True},  # Additional dataset options\n",
-        "    #max_seq_length=1024  # Maximum sequence length for input\n",
-        ")\n",
-        "\n",
-        "training_args.remove_unused_columns = False  # Keep unused columns in dataset"
+        "    report_to=\"trackio\",  # Reporting tool for tracking metrics\n",
+        ")"
       ]
     },
     {
@@ -1370,78 +833,52 @@
         "id": "wjQGt-iZVyef"
       },
       "source": [
-        "We will log our training progress using [Weights & Biases (W&B)](https://wandb.ai/). Let’s connect our notebook to W&B to capture essential information during training.\n"
+        "We will log our training progress using [trackio](https://huggingface.co/blog/trackio). Let’s connect our notebook to W&B to capture essential information during training.\n"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 19,
       "metadata": {
         "colab": {
-          "base_uri": "https://localhost:8080/",
-          "height": 228
+          "base_uri": "https://localhost:8080/"
         },
         "id": "ckVfXDWsoF4Y",
-        "outputId": "bb7ce99c-ed2b-481c-a11f-27272ce8da91"
+        "outputId": "25584786-8110-48c5-a634-bc731bdce556"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stdout",
+          "output_type": "stream",
+          "text": [
+            "* Trackio project initialized: qwen2-7b-instruct-trl-sft-ChartQA\n",
+            "* Trackio metrics will be synced to Hugging Face Dataset: sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA-trackio-dataset\n",
+            "* Creating new space: https://huggingface.co/spaces/sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA-trackio\n",
+            "* View dashboard by going to: https://huggingface.co/spaces/sergiopaniego/qwen2-7b-instruct-trl-sft-ChartQA-trackio\n"
+          ]
+        },
+        {
+          "data": {
+            "text/plain": [
+              "<trackio.run.Run at 0x7b48df258410>"
+            ]
+          },
+          "execution_count": 19,
+          "metadata": {},
+          "output_type": "execute_result"
+        }
+      ],
       "source": [
-        "import wandb\n",
+        "import trackio\n",
         "\n",
-        "wandb.init(\n",
-        "    project=\"qwen2-7b-instruct-trl-sft-ChartQA\",  # change this\n",
-        "    name=\"qwen2-7b-instruct-trl-sft-ChartQA\",  # change this\n",
+        "trackio.init(\n",
+        "    project=\"qwen2-7b-instruct-trl-sft-ChartQA\",\n",
+        "    name=\"qwen2-7b-instruct-trl-sft-ChartQA\",\n",
         "    config=training_args,\n",
+        "    space_id=training_args.output_dir + \"-trackio\"\n",
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ucTUbGURV2_-"
-      },
-      "source": [
-        "We need a collator function to properly retrieve and batch the data during the training procedure. This function will handle the formatting of our dataset inputs, ensuring they are correctly structured for the model. Let's define the collator function below.\n",
-        "\n",
-        "👉 Check out the TRL official example [scripts]( https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87) for more details.\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {
-        "id": "pAzDovzylQeZ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a data collator to encode text and image pairs\n",
-        "def collate_fn(examples):\n",
-        "    # Get the texts and images, and apply the chat template\n",
-        "    texts = [processor.apply_chat_template(example, tokenize=False) for example in examples]  # Prepare texts for processing\n",
-        "    image_inputs = [process_vision_info(example)[0] for example in examples]  # Process the images to extract inputs\n",
-        "\n",
-        "    # Tokenize the texts and process the images\n",
-        "    batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True)  # Encode texts and images into tensors\n",
-        "\n",
-        "    # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
-        "    labels = batch[\"input_ids\"].clone()  # Clone input IDs for labels\n",
-        "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
-        "\n",
-        "    # Ignore the image token index in the loss computation (model specific)\n",
-        "    if isinstance(processor, Qwen2VLProcessor):  # Check if the processor is Qwen2VLProcessor\n",
-        "        image_tokens = [151652, 151653, 151655]  # Specific image token IDs for Qwen2VLProcessor\n",
-        "    else:\n",
-        "        image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)]  # Convert image token to ID\n",
-        "\n",
-        "    # Mask image token IDs in the labels\n",
-        "    for image_token_id in image_tokens:\n",
-        "        labels[labels == image_token_id] = -100  # Mask image token IDs in labels\n",
-        "\n",
-        "    batch[\"labels\"] = labels  # Add labels to the batch\n",
-        "\n",
-        "    return batch  # Return the prepared batch"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1454,15 +891,26 @@
     },
     {
       "cell_type": "code",
-      "execution_count": null,
+      "execution_count": 20,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "k_jk-U7ULYtA",
-        "outputId": "0dc465a9-1744-4b9a-d090-22a63f2e48de"
+        "outputId": "3bbe53a4-73a3-40e4-c318-4397670df20d"
       },
-      "outputs": [],
+      "outputs": [
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "/usr/local/lib/python3.12/dist-packages/peft/mapping_func.py:73: UserWarning: You are trying to modify a model with PEFT for a second time. If you want to reload the model with a different config, make sure to call `.unload()` before.\n",
+            "  warnings.warn(\n",
+            "/usr/local/lib/python3.12/dist-packages/peft/tuners/tuners_utils.py:196: UserWarning: Already found a `peft_config` attribute in the model. This will lead to having multiple adapters in the model. Make sure to know what you are doing!\n",
+            "  warnings.warn(\n"
+          ]
+        }
+      ],
       "source": [
         "from trl import SFTTrainer\n",
         "\n",
@@ -1471,9 +919,8 @@
         "    args=training_args,\n",
         "    train_dataset=train_dataset,\n",
         "    eval_dataset=eval_dataset,\n",
-        "    data_collator=collate_fn,\n",
         "    peft_config=peft_config,\n",
-        "    processing_class=processor.tokenizer,\n",
+        "    processing_class=processor,\n",
         ")"
       ]
     },
@@ -1593,33 +1040,33 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 72,
+      "execution_count": 8,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 49,
+          "height": 104,
           "referenced_widgets": [
-            "dc179daa0be34359a3c9ec0224537c53",
-            "2b21097352c34b2e8e91f86e20bf834c",
-            "f3c4e0dd2e274dce8e3c74ca58796581",
-            "57e326d7a47242d09c6819ec367d87ed",
-            "8b1ba107530f43818c2c9a159150a6a4",
-            "9aa9e4f562274e33aeb213bfd7bd7c0e",
-            "a5a53558120545f69d4a751190261945",
-            "24d743bc855c4f3183ac800fb655dbd6",
-            "6c53c7d2c14d4df9b3cd9191f8b008c7",
-            "7787baba17db494590ccc37d3017002c",
-            "964f75e681d6498592ff634a22665a70"
+            "6d358c3999024558bbdf63cc8ff3b0c8",
+            "63e7a6485906469aa9b709f3b119f9be",
+            "1aac4bfb05c14e42a3f4372fc535ece6",
+            "fc3de4763c3f462785f4cde31c35c68e",
+            "7411cc7b1f224a10984238aec1967a44",
+            "ee59471ae1ef4182a04260ef870a5fae",
+            "2d4e5b7585a0441ca4eacec3a2ac7785",
+            "8f940de2b7ff49ecb05257990b335eed",
+            "cdfde68c4fbf4d7c9bb44ce31bfdf243",
+            "c264ef9a3ada4fbdbb2b7c55ecb84ed1",
+            "a3fc798acf0848238b43b7580998e300"
           ]
         },
         "id": "EFqTNUud2lA7",
-        "outputId": "32f3a882-0fed-4527-ca83-74857afe658a"
+        "outputId": "f862017b-ae59-44ca-d39d-7deceaef0ead"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "dc179daa0be34359a3c9ec0224537c53",
+              "model_id": "6d358c3999024558bbdf63cc8ff3b0c8",
               "version_major": 2,
               "version_minor": 0
             },
@@ -1629,6 +1076,14 @@
           },
           "metadata": {},
           "output_type": "display_data"
+        },
+        {
+          "name": "stderr",
+          "output_type": "stream",
+          "text": [
+            "The image processor of type `Qwen2VLImageProcessor` is now loaded as a fast processor by default, even if the model checkpoint was saved with a slow processor. This is a breaking change and may produce slightly different outputs. To continue using the slow processor, instantiate this class with `use_fast=False`. Note that this behavior will be extended to all models in a future release.\n",
+            "You have video processor config saved in `preprocessor.json` file which is deprecated. Video processor configs should be saved in their own `video_preprocessor.json` file. You can rename the file or load and save the processor back which renames it automatically. Loading from `preprocessor.json` will be removed in v5.0.\n"
+          ]
         }
       ],
       "source": [
@@ -1652,49 +1107,49 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 73,
+      "execution_count": 9,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 81,
           "referenced_widgets": [
-            "9f91c4c646c14451a8d9013ff7c8b754",
-            "1e3329ab17004575adf04fdfbadcf477",
-            "748b6e32f3df4e70ba65a1e8a0b439f5",
-            "fa77f069797d4909b1cd8d8ed8ad98a4",
-            "9d301e23ce0742678393d6b28f9dc3c6",
-            "91c9787bdb2c4334bc9dd2ca472f7a73",
-            "0fa4ad9efb6b40b3b3101f70b0c2783d",
-            "fb8186d891e2407893195e581cf1050c",
-            "10abc52f216b43b69539ae79a2b1778c",
-            "f99bfabeae344ddca98017f43a9c95d1",
-            "ca03b3317b9c4700b9fb36a81a7928b7",
-            "1a953c336a9049b3a46f1895bcc03ed4",
-            "a53d054b82bc449d9abbddfc1fd72b2f",
-            "106a2f1a5ec24ceebdf047b6ff3d7564",
-            "c77a78daf5124afa8aa1aae19664b138",
-            "bc7725cc53fd436e9c73cf290734565d",
-            "5610f43f3ba04091a6f472c6ea706f92",
-            "8c42f14dda9e47c9934193b7e41241bb",
-            "54c4ef58e08c468fa84209fefc1c8cfa",
-            "4e4a58afbec7429fa3cf86c50d2bc2e0",
-            "fdda9bb09ce04f32b66c685a4551a62e",
-            "0fb1f35201994c61a18798a521f0808b"
+            "81f0b09f30974d5da6d54c7495df3afb",
+            "319ce7779dd54087a79748cff15dd380",
+            "bc10f7a03e1c4260b71190829160d57a",
+            "9bf65c0fc9714cd4bbfc8b5b9ada1986",
+            "d0106dbd65d2407da49802adca5a3f6b",
+            "5d1335feb15d470eb70727f1fc85fc64",
+            "5b0be6b9dfc046769a90b0167faa9023",
+            "775a2d404cb94764ad3c33767237cb3d",
+            "905424605e68484796735ce42262b511",
+            "341897caa36f49c082e98ea94d559ddb",
+            "0771b04a68e7476787cd2d1e0f2ce30b",
+            "815589aa997046419e28fa02ad87d2dc",
+            "0f2fb0be37b14323be7b7ee9e9ef6e3d",
+            "812420893e2d4b0f8676263f044410dc",
+            "375ede44354d4b4da527d5291c8e863a",
+            "b4611840b5fb433aad47e0acbdc2cd2f",
+            "03cfe58d482440f3bf80afc43e53730a",
+            "801d03f0db264fd58221d76b3cbc9a35",
+            "9e3f26cdd0e347f2b06762128aaae329",
+            "c99c38ec815049eaa15972ae46f94a8d",
+            "a62f4664a5ac40d785893e4eff1e34c0",
+            "1aa67fff82344f299f5c73a419871b03"
           ]
         },
         "id": "mQi2xBXk4sHe",
-        "outputId": "1096bbce-04e1-475d-c7ea-090ef2e5bf5b"
+        "outputId": "c2c64fa7-6803-4237-91e8-463aa96baed3"
       },
       "outputs": [
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "9f91c4c646c14451a8d9013ff7c8b754",
+              "model_id": "81f0b09f30974d5da6d54c7495df3afb",
               "version_major": 2,
               "version_minor": 0
             },
             "text/plain": [
-              "adapter_config.json:   0%|          | 0.00/650 [00:00<?, ?B/s]"
+              "adapter_config.json:   0%|          | 0.00/859 [00:00<?, ?B/s]"
             ]
           },
           "metadata": {},
@@ -1703,7 +1158,7 @@
         {
           "data": {
             "application/vnd.jupyter.widget-view+json": {
-              "model_id": "1a953c336a9049b3a46f1895bcc03ed4",
+              "model_id": "815589aa997046419e28fa02ad87d2dc",
               "version_major": 2,
               "version_minor": 0
             },
@@ -1731,13 +1186,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 79,
+      "execution_count": 12,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "3X9YGJUezZr6",
-        "outputId": "0598cff6-dae8-4496-bef7-82e2015d12bf"
+        "outputId": "a9dad5c4-ef2f-49e6-bb7d-a38c905b4faf"
       },
       "outputs": [
         {
@@ -1752,25 +1207,25 @@
               "   {'type': 'text', 'text': 'Is the value of Favorable 38 in 2015?'}]}]"
             ]
           },
-          "execution_count": 79,
+          "execution_count": 12,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "train_dataset[0][:2]"
+        "train_dataset[0]['messages'][:2]"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 80,
+      "execution_count": 14,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
           "height": 376
         },
         "id": "2hLJrxxTVn6x",
-        "outputId": "ee8fd396-f73b-4b8d-e10f-e4430a6a9b13"
+        "outputId": "0e508f63-e60e-40b8-9e2b-61ebf12750c9"
       },
       "outputs": [
         {
@@ -1781,25 +1236,25 @@
               "<PIL.PngImagePlugin.PngImageFile image mode=RGB size=422x359>"
             ]
           },
-          "execution_count": 80,
+          "execution_count": 14,
           "metadata": {},
           "output_type": "execute_result"
         }
       ],
       "source": [
-        "train_dataset[0][1]['content'][0]['image']"
+        "train_dataset[0]['images'][0]"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 81,
+      "execution_count": 15,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
-          "height": 36
+          "height": 35
         },
         "id": "pdb9vErmzdAf",
-        "outputId": "e5059c15-c1ec-4f9b-d642-820cac72bb59"
+        "outputId": "157008ef-e1f7-4968-d55a-eeb41906d807"
       },
       "outputs": [
         {
@@ -1808,10 +1263,10 @@
               "type": "string"
             },
             "text/plain": [
-              "'Yes'"
+              "'No, the value of Favorable is not 38 in 2015. According to the chart, the value of Favorable in 2015 is 38.'"
             ]
           },
-          "execution_count": 81,
+          "execution_count": 15,
           "metadata": {},
           "output_type": "execute_result"
         }
@@ -1839,13 +1294,13 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 82,
+      "execution_count": 16,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
         },
         "id": "czZSBgnoef1E",
-        "outputId": "350d3520-4973-444e-f2bc-12e44d548fe6"
+        "outputId": "e9532aa4-c4cd-47d6-8c40-c4a36677ceba"
       },
       "outputs": [
         {
@@ -1860,25 +1315,25 @@
               "   {'type': 'text', 'text': 'What is the value of Slovenia in the graph?'}]}]"
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+              "'The value of Slovenia in the graph is 1.0.'"
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diff --git a/notebooks/zh-CN/fine_tuning_vlm_trl.ipynb b/notebooks/zh-CN/fine_tuning_vlm_trl.ipynb
index 748d0062..a9527746 100644
--- a/notebooks/zh-CN/fine_tuning_vlm_trl.ipynb
+++ b/notebooks/zh-CN/fine_tuning_vlm_trl.ipynb
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       },
       "outputs": [],
       "source": [
-        "!pip install  -U -q git+https://github.com/huggingface/transformers.git git+https://github.com/huggingface/trl.git datasets bitsandbytes peft qwen-vl-utils wandb accelerate\n",
-        "# Tested with transformers==4.53.0.dev0, trl==0.20.0.dev0, datasets==3.6.0, bitsandbytes==0.46.0, peft==0.15.2, qwen-vl-utils==0.0.11, wandb==0.20.1, accelerate==1.8.1"
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-      "cell_type": "markdown",
-      "metadata": {
-        "id": "J4pAvoQaOJ1M"
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-      "source": [
-        "我们还需要安装早期版本的 *PyTorch*，因为最新版本存在一个问题，当前会导致本 Notebook 无法正常运行。你可以在 [此处](https://github.com/pytorch/pytorch/issues/138340) 了解更多关于该问题的信息，并在问题解决后考虑更新到最新版本。\n"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": null,
-      "metadata": {
-        "colab": {
-          "base_uri": "https://localhost:8080/"
-        },
-        "id": "D8iRteA4oXVj",
-        "outputId": "2a872542-c0ce-4ebd-92af-a9e593e3b18c"
-      },
-      "outputs": [],
-      "source": [
-        "!pip install -q torch==2.4.1+cu121 torchvision==0.19.1+cu121 torchaudio==2.4.1+cu121 --extra-index-url https://download.pytorch.org/whl/cu121"
+        "!pip install -U -q git+https://github.com/huggingface/trl.git bitsandbytes peft qwen-vl-utils trackio\n",
+        "# Tested with trl==0.22.0.dev0, bitsandbytes==0.47.0, peft==0.17.1, qwen-vl-utils==0.0.11, trackio==0.2.8"
       ]
     },
     {
@@ -217,46 +193,50 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 5,
+      "execution_count": null,
       "metadata": {
         "id": "XG8NuzqjjbgI"
       },
       "outputs": [],
       "source": [
         "def format_data(sample):\n",
-        "    return [\n",
-        "        {\n",
-        "            \"role\": \"system\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": system_message\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "        {\n",
-        "            \"role\": \"user\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"image\",\n",
-        "                    \"image\": sample[\"image\"],\n",
-        "                },\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": sample['query'],\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "        {\n",
-        "            \"role\": \"assistant\",\n",
-        "            \"content\": [\n",
-        "                {\n",
-        "                    \"type\": \"text\",\n",
-        "                    \"text\": sample[\"label\"][0]\n",
-        "                }\n",
-        "            ],\n",
-        "        },\n",
-        "    ]"
+        "    return {\n",
+        "      \"images\": [sample[\"image\"]],\n",
+        "      \"messages\": [\n",
+        "\n",
+        "          {\n",
+        "              \"role\": \"system\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": system_message\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "          {\n",
+        "              \"role\": \"user\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"image\",\n",
+        "                      \"image\": sample[\"image\"],\n",
+        "                  },\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": sample['query'],\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "          {\n",
+        "              \"role\": \"assistant\",\n",
+        "              \"content\": [\n",
+        "                  {\n",
+        "                      \"type\": \"text\",\n",
+        "                      \"text\": sample[\"label\"][0]\n",
+        "                  }\n",
+        "              ],\n",
+        "          },\n",
+        "      ]\n",
+        "      }"
       ]
     },
     {
@@ -951,7 +931,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 18,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -975,7 +955,7 @@
         }
       ],
       "source": [
-        "train_dataset[0][1:2]"
+        "train_dataset[0]['messages'][1:2]"
       ]
     },
     {
@@ -989,7 +969,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 19,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1013,7 +993,7 @@
         }
       ],
       "source": [
-        "train_dataset[0][1]['content'][0]['image']"
+        "train_dataset[0]['images'][0]"
       ]
     },
     {
@@ -1027,7 +1007,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 25,
+      "execution_count": null,
       "metadata": {
         "id": "_MoRTjFcE8qD"
       },
@@ -1038,13 +1018,13 @@
         "def generate_text_from_sample(model, processor, sample, max_new_tokens=1024, device=\"cuda\"):\n",
         "    # Prepare the text input by applying the chat template\n",
         "    text_input = processor.apply_chat_template(\n",
-        "        sample[1:2],  # Use the sample without the system message\n",
+        "        sample['messages'][1:2],  # Use the sample without the system message\n",
         "        tokenize=False,\n",
         "        add_generation_prompt=True\n",
         "    )\n",
         "\n",
         "    # Process the visual input from the sample\n",
-        "    image_inputs, _ = process_vision_info(sample)\n",
+        "    image_inputs, _ = process_vision_info(sample['messages'])\n",
         "\n",
         "    # Prepare the inputs for the model\n",
         "    model_inputs = processor(\n",
@@ -1303,7 +1283,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 47,
+      "execution_count": null,
       "metadata": {
         "id": "SbqX1pQUKaSM"
       },
@@ -1318,37 +1298,25 @@
         "    per_device_train_batch_size=4,  # Batch size for training\n",
         "    per_device_eval_batch_size=4,  # Batch size for evaluation\n",
         "    gradient_accumulation_steps=8,  # Steps to accumulate gradients\n",
-        "    gradient_checkpointing=True,  # Enable gradient checkpointing for memory efficiency\n",
+        "    gradient_checkpointing_kwargs={\"use_reentrant\": False},  # Options for gradient checkpointing\n",
+        "    max_length=None,\n",
         "    # Optimizer and scheduler settings\n",
         "    optim=\"adamw_torch_fused\",  # Optimizer type\n",
         "    learning_rate=2e-4,  # Learning rate for training\n",
-        "    lr_scheduler_type=\"constant\",  # Type of learning rate scheduler\n",
         "    # Logging and evaluation\n",
         "    logging_steps=10,  # Steps interval for logging\n",
         "    eval_steps=10,  # Steps interval for evaluation\n",
         "    eval_strategy=\"steps\",  # Strategy for evaluation\n",
         "    save_strategy=\"steps\",  # Strategy for saving the model\n",
         "    save_steps=20,  # Steps interval for saving\n",
-        "    metric_for_best_model=\"eval_loss\",  # Metric to evaluate the best model\n",
-        "    greater_is_better=False,  # Whether higher metric values are better\n",
-        "    load_best_model_at_end=True,  # Load the best model after training\n",
         "    # Mixed precision and gradient settings\n",
         "    bf16=True,  # Use bfloat16 precision\n",
-        "    tf32=True,  # Use TensorFloat-32 precision\n",
         "    max_grad_norm=0.3,  # Maximum norm for gradient clipping\n",
         "    warmup_ratio=0.03,  # Ratio of total steps for warmup\n",
         "    # Hub and reporting\n",
         "    push_to_hub=True,  # Whether to push model to Hugging Face Hub\n",
-        "    report_to=\"wandb\",  # Reporting tool for tracking metrics\n",
-        "    # Gradient checkpointing settings\n",
-        "    gradient_checkpointing_kwargs={\"use_reentrant\": False},  # Options for gradient checkpointing\n",
-        "    # Dataset configuration\n",
-        "    dataset_text_field=\"\",  # Text field in dataset\n",
-        "    dataset_kwargs={\"skip_prepare_dataset\": True},  # Additional dataset options\n",
-        "    #max_seq_length=1024  # Maximum sequence length for input\n",
-        ")\n",
-        "\n",
-        "training_args.remove_unused_columns = False  # Keep unused columns in dataset"
+        "    report_to=\"trackio\",  # Reporting tool for tracking metrics\n",
+        ")"
       ]
     },
     {
@@ -1366,7 +1334,7 @@
         "id": "wjQGt-iZVyef"
       },
       "source": [
-        "我们将使用[Weights & Biases (W&B)](https://wandb.ai/)来记录我们的训练进度。让我们将 Notebook 与W&B连接，以便在训练过程中捕获重要信息。"
+        "我们将使用[trackio](https://huggingface.co/blog/trackio)来记录我们的训练进度。让我们将 Notebook 与W&B连接，以便在训练过程中捕获重要信息。"
       ]
     },
     {
@@ -1382,62 +1350,16 @@
       },
       "outputs": [],
       "source": [
-        "import wandb\n",
+        "import trackio\n",
         "\n",
-        "wandb.init(\n",
-        "    project=\"qwen2-7b-instruct-trl-sft-ChartQA\",  # change this\n",
-        "    name=\"qwen2-7b-instruct-trl-sft-ChartQA\",  # change this\n",
+        "trackio.init(\n",
+        "    project=\"qwen2-7b-instruct-trl-sft-ChartQA\",\n",
+        "    name=\"qwen2-7b-instruct-trl-sft-ChartQA\",\n",
         "    config=training_args,\n",
+        "    space_id=training_args.output_dir + \"-trackio\"\n",
         ")"
       ]
     },
-    {
-      "cell_type": "markdown",
-      "metadata": {
-        "id": "ucTUbGURV2_-"
-      },
-      "source": [
-        "我们需要一个整理器函数来在训练过程中正确地检索和批量处理数据。这个函数将负责格式化我们的数据集输入，确保它们以适当的结构提供给模型。接下来，让我们定义整理器函数。\n",
-        "\n",
-        "👉 了解更多细节，请查看TRL官方示例[脚本](https://github.com/huggingface/trl/blob/main/examples/scripts/sft_vlm.py#L87)。"
-      ]
-    },
-    {
-      "cell_type": "code",
-      "execution_count": 65,
-      "metadata": {
-        "id": "pAzDovzylQeZ"
-      },
-      "outputs": [],
-      "source": [
-        "# Create a data collator to encode text and image pairs\n",
-        "def collate_fn(examples):\n",
-        "    # Get the texts and images, and apply the chat template\n",
-        "    texts = [processor.apply_chat_template(example, tokenize=False) for example in examples]  # Prepare texts for processing\n",
-        "    image_inputs = [process_vision_info(example)[0] for example in examples]  # Process the images to extract inputs\n",
-        "\n",
-        "    # Tokenize the texts and process the images\n",
-        "    batch = processor(text=texts, images=image_inputs, return_tensors=\"pt\", padding=True)  # Encode texts and images into tensors\n",
-        "\n",
-        "    # The labels are the input_ids, and we mask the padding tokens in the loss computation\n",
-        "    labels = batch[\"input_ids\"].clone()  # Clone input IDs for labels\n",
-        "    labels[labels == processor.tokenizer.pad_token_id] = -100  # Mask padding tokens in labels\n",
-        "\n",
-        "    # Ignore the image token index in the loss computation (model specific)\n",
-        "    if isinstance(processor, Qwen2VLProcessor):  # Check if the processor is Qwen2VLProcessor\n",
-        "        image_tokens = [151652, 151653, 151655]  # Specific image token IDs for Qwen2VLProcessor\n",
-        "    else:\n",
-        "        image_tokens = [processor.tokenizer.convert_tokens_to_ids(processor.image_token)]  # Convert image token to ID\n",
-        "\n",
-        "    # Mask image token IDs in the labels\n",
-        "    for image_token_id in image_tokens:\n",
-        "        labels[labels == image_token_id] = -100  # Mask image token IDs in labels\n",
-        "\n",
-        "    batch[\"labels\"] = labels  # Add labels to the batch\n",
-        "\n",
-        "    return batch  # Return the prepared batch"
-      ]
-    },
     {
       "cell_type": "markdown",
       "metadata": {
@@ -1729,7 +1651,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 79,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1756,12 +1678,12 @@
         }
       ],
       "source": [
-        "train_dataset[0][:2]"
+        "train_dataset[0]['messages'][:2]"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 80,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1785,7 +1707,7 @@
         }
       ],
       "source": [
-        "train_dataset[0][1]['content'][0]['image']"
+        "train_dataset[0]['images'][0]"
       ]
     },
     {
@@ -1837,7 +1759,7 @@
     },
     {
       "cell_type": "code",
-      "execution_count": 82,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/"
@@ -1864,12 +1786,12 @@
         }
       ],
       "source": [
-        "test_dataset[10][:2]"
+        "test_dataset[10]['messages'][:2]"
       ]
     },
     {
       "cell_type": "code",
-      "execution_count": 83,
+      "execution_count": null,
       "metadata": {
         "colab": {
           "base_uri": "https://localhost:8080/",
@@ -1892,7 +1814,7 @@
         }
       ],
       "source": [
-        "test_dataset[10][1]['content'][0]['image']"
+        "test_dataset[10]['images'][0]"
       ]
     },
     {