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"metadata": {} @@ -316,7 +316,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1d15efc3e5824f3aa3712a48659059ec" + "model_id": "8d1b37bac59f4534b2719efb9a1c9831" } }, "metadata": {} @@ -330,7 +330,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "956e555414cb4946aed91225b66f3e70" + "model_id": "7f5dbf97536b45dda3109887a137a4a4" } }, "metadata": {} @@ -344,7 +344,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "27b74014067f4e3991b70649187fa0f9" + "model_id": "27939884e1e344ed9287569785a5089b" } }, "metadata": {} @@ -358,7 +358,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "3a4baf26fe5146e7a69aa7e4deb49dbd" + "model_id": "7b9af692b0c3495e8a14f277aadd7dba" } }, "metadata": {} @@ -372,7 +372,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "9ae102f333954ae6927b972a269499cd" + "model_id": "8d3ce008ce5e4183852edf7e3187f05b" } }, "metadata": {} @@ -386,7 +386,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "bced5d42daa94cf7b702b0785de3c27b" + "model_id": "b800f7e606ae4e4a909210f30e04ea49" } }, "metadata": {} @@ -400,7 +400,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2de146aaf44b4e30be62a5af798ca0eb" + "model_id": "371d7b83e61b4dbe87169c04056ec7f9" } }, "metadata": {} @@ -414,7 +414,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "df8368ac573444eca453f0584b0dc6ae" + "model_id": "a08785e4b8944943aa94235b484053d9" } }, "metadata": {} @@ -428,7 +428,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "c62595813e5e403696f74385ed72ab48" + "model_id": "e016f5805449446985198ad9408518e6" } }, "metadata": {} @@ -442,7 +442,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "37a60705625e428596a3055c6fed0334" + "model_id": "0b712b69eaf440b58bdcb2d026c38320" } }, "metadata": {} @@ -544,7 +544,7 @@ "base_uri": "https://localhost:8080/" }, "id": "9jGeSb9bWe0k", - "outputId": "f070d726-1436-49ea-c7f5-0715bc96c71d" + "outputId": "18d0e1e2-5acb-467b-820b-9df46438c28f" }, "execution_count": null, "outputs": [ @@ -552,17 +552,16 @@ "output_type": "stream", "name": "stdout", "text": [ - "This adorable animal is a **sloth**! \n", - "\n", - "Sloths have appeared in a few films, most notably:\n", - "\n", - "* **Zootopia (2016):** The sloth Flash is a memorable character in this Disney movie.\n", - "* **Over the Hedge (2006):** A sloth named Stella makes a cameo appearance. \n", - "\n", - "They're often used for comedic effect due to their slow movements! \n", + "The animal in the image is a **sloth**. \n", "\n", + "Sloths have appeared in several films, often as comedic characters due to their slow movements. Here are a few examples:\n", "\n", + "* **Zootopia (2016):** Flash, a sloth, is a memorable character in this Disney film.\n", + "* **The Jungle Book 2 (2003):** Sloths are featured in this Disney sequel.\n", + "* **Curious George (2003):** Sloths appear in this animated film.\n", + "* **Many nature documentaries:** Sloths are frequently featured in nature documentaries due to their unique lifestyle. \n", "\n", + "Do you have any other questions about sloths? \n", "\n" ] } @@ -592,7 +591,7 @@ "base_uri": "https://localhost:8080/" }, "id": "R3ExuK8cWuT3", - "outputId": "fa0849db-b267-4d30-f556-ff22a12747f0" + "outputId": "c8899856-325d-4b40-bbf9-f08de5feedc1" }, "execution_count": null, "outputs": [ @@ -600,22 +599,22 @@ "output_type": "stream", "name": "stdout", "text": [ - "In emerald canopy, a gentle sway,\n", - "The sloth hangs suspended, come what may.\n", - "A master of stillness, a creature serene,\n", - "Living life at a pace rarely seen.\n", + "In emerald canopies, slow they reside,\n", + "The sloth, a creature of gentle tide.\n", + "A master of stillness, a verdant hue,\n", + "Living life at a pace both calm and true.\n", "\n", - "With fur of soft moss and a smile so slow,\n", - "He watches the world in a tranquil flow.\n", - "No rush, no hurry, no frantic chase,\n", - "Just peaceful existence in this leafy place.\n", + "He hangs upside down, a peaceful sight,\n", + "Ignoring the rush of day and night.\n", + "A furry embrace, a gentle sway,\n", + "He finds contentment in his own way.\n", "\n", - "He munches on leaves, a verdant delight,\n", - "And dreams in the sun, bathed in golden light.\n", - "A slow-moving marvel, a quirky design,\n", - "A symbol of patience, uniquely divine.\n", + "Leaves are his banquet, a leafy green feast,\n", + "Energy conserved, a quiet beast.\n", + "He moves with purpose, though slow he seems,\n", + "A patient observer of nature's dreams.\n", "\n", - "He blends with the\n" + "No need for hurry, no need for chase\n" ] } ] @@ -633,7 +632,7 @@ "cell_type": "code", "source": [ "from IPython.display import Audio, display\n", - "Audio(\"https://upload.wikimedia.org/wikipedia/en/6/61/I_Have_A_Dream_sample.ogg\")" + "Audio(\"https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3\")" ], "metadata": { "colab": { @@ -641,7 +640,7 @@ "height": 61 }, "id": "68crYajNZtw1", - "outputId": "68dde433-5e01-4b34-a6f1-ef8c40786943" + "outputId": "997423cb-e6a4-4443-ce87-89e7953c40be" }, "execution_count": null, "outputs": [ @@ -654,21 +653,21 @@ "text/html": [ "\n", " \n", " " ] }, "metadata": {}, - "execution_count": 7 + "execution_count": 20 } ] }, { "cell_type": "code", "source": [ - "!wget -qqq https://upload.wikimedia.org/wikipedia/en/6/61/I_Have_A_Dream_sample.ogg" + "!wget -qqq https://www.nasa.gov/wp-content/uploads/2015/01/591240main_JFKmoonspeech.mp3 -O audio.mp3" ], "metadata": { "id": "k3vrdoa0Z01X" @@ -679,7 +678,7 @@ { "cell_type": "code", "source": [ - "audio_file = \"I_Have_A_Dream_sample.ogg\"\n", + "audio_file = \"audio.mp3\"\n", "\n", "messages = [{\n", " \"role\" : \"user\",\n", @@ -695,7 +694,7 @@ "base_uri": "https://localhost:8080/" }, "id": "BJr_D4O9Z2Zh", - "outputId": "4a9b561f-8bac-4d9f-9a73-c67d5af8b97f" + "outputId": "b08643d9-6c1b-4dda-af99-8589940e7dcf" }, "execution_count": null, "outputs": [ @@ -703,9 +702,7 @@ "output_type": "stream", "name": "stdout", "text": [ - "The audio is a recording of a portion of Martin Luther King Jr.'s famous \"I Have a Dream\" speech. \n", - "\n", - "The speech expresses his dream for a future where his children will be judged not by the color of their skin but by the content of their character. It's a powerful message of equality and hope.\n" + "The audio is a quote from President John F. Kennedy's famous speech to Congress on May 25, 1961. In this speech, Kennedy boldly declares the nation's commitment to achieving the goal of landing a man on the moon and returning him safely to Earth before the end of the decade. This speech is considered a pivotal moment in American history and a defining moment of the Space Race.\n" ] } ] @@ -738,7 +735,7 @@ "base_uri": "https://localhost:8080/" }, "id": "is37bsDZaRwV", - "outputId": "d8a0cef5-5dd8-4545-cb73-5fe6bb984790" + "outputId": "a198b9b6-8670-4383-bd4f-eccdd2e5e652" }, "execution_count": null, "outputs": [ @@ -746,15 +743,19 @@ "output_type": "stream", "name": "stdout", "text": [ - "The audio is a recording of **Martin Luther King Jr.'s famous \"I Have a Dream\" speech**. \n", + "The audio is a famous excerpt from President John F. Kennedy's inaugural address in 1961. In this speech, he passionately calls on the nation to commit to a goal of landing a man on the moon and returning him safely to Earth before the end of the decade.\n", "\n", - "The image shows a **sloth hanging upside down from a tree branch**. \n", + "The image shows a sloth hanging upside down from a tree branch in a lush, green environment. \n", "\n", - "The connection between the audio and the image is **symbolic and metaphorical**. The \"I Have a Dream\" speech is about a future where people are judged by their character, not their skin color. The sloth, known for its slow and peaceful nature, can be seen as a symbol of patience and hope for this future. \n", + "**How they are related:**\n", "\n", - "The image might be used to evoke a sense of calm and perseverance, mirroring the enduring hope and struggle for equality expressed in King's speech. It could also be a way to visually represent the slow but steady progress towards a more just and equitable society. \n", + "The connection between the audio and the image is **metaphorical and likely intended to convey the slowness and deliberate nature of the space race.** \n", "\n", - "It's important to note that this connection is not literal. There's no direct relationship between a sloth and the content of King's speech. However, the image serves as a powerful visual metaphor for the themes of hope, patience, and the long journey towards achieving a dream of equality.\n" + "The phrase \"landing a man on the moon\" represents a monumental and ambitious goal, while the sloth, known for its incredibly slow movement, serves as a humorous contrast. \n", + "\n", + "The image likely aims to playfully suggest that even with the urgency of the space race, there's a certain inherent slowness or patience required to achieve such a grand ambition. It's a lighthearted way to juxtapose the extraordinary goal with something very ordinary and slow-moving. \n", + "\n", + "It's not a direct literal connection, but rather a clever visual metaphor playing on the contrast between human ambition and natural processes.\n" ] } ] @@ -784,7 +785,7 @@ "execution_count": null, "metadata": { "id": "6bZsfBuZDeCL", - "outputId": "52472f8e-cd55-47e7-82ed-b03264ad1b3d", + "outputId": "41e67287-1439-484f-c866-a6dab36a89d0", "colab": { "base_uri": "https://localhost:8080/" } @@ -867,42 +868,42 @@ "base_uri": "https://localhost:8080/", "height": 169, "referenced_widgets": [ - "4f9d977850ea479488a18af2fbd2c606", - "a91cba78c2a741d8bed7520261b5fc87", - "3b66a241808a43d293fc22ce2d2c050b", - "6eae87e4d07a4f37bceae0307ac9d483", - "0c2621b6599c4b8c94b93bc0cf2472cb", - "a2b2e5a6a0c949cfb5f35bb28c4ef9a6", - "5c058455293647d3a7b0e9490eefb96f", - "6766c08e64a84176a19895ba1a2e2988", - "c27cb86cd4f2400696d40a6823f80adc", - "bb88da6977e94694882057c1ec9ace1f", - "ab545986a8ed4ac2b49c161761201f6e", - "1bf90be3e82a4b4b8fdd81ca719f9d94", - "a5ad0bad814d4eef9ea8236699e6e071", - "0fd9c71795604fceaeac592dfb7152f5", - "9d8cf731a8054b41a97f00504fb66152", - "fc3a42d6dfda47a7b9cd261e0e9ba3b3", - "0ef0d5f9cf774d78b080e6549c01a9dc", - "0da5249afae34862b707dd2d9f08ce7f", - "6f2e9e1d6a984e049bb070d67602b649", - "f5ff2981d51149318b2bbef9197c64dd", - "6ae77f61172a47a7892dc40683939dad", - "e77cea13dc0645bbbe7b548690945f73", - "21a737636bd34890b63f748e2ad6aea7", - "007d58e515b34d80a303e8493fb6e726", - "eef95ebc8cf243a4a1f65dc9878f6305", - "8419bfdccb384abba5cc926e24325bfe", - "7341640e892e4df5bd39cb0c0429cdc5", - "033e3a93c052418487e32e2cabc12990", - "b0cf479ea0f24cbd820e4c09b4c44d97", - "dbd8322d1e574c289524cc82017f61cc", - "457b79de10214f51b5f178daff77bda0", - "42bac23a3f8e4be49e56aaccbc49d655", - "6f69db3b37fa401dbd3cf659bc83c6f5" + "1a5f63e9aadd42a98dd323d8afcd6f81", + "c52e7f6043bb4e09998da74beb9ff375", + "52d55892494449fca142c9326f91d869", + "82b84bff5f5247d6828565a7d66e5652", + "ac7476ff81cb46cc90f34fb91b948fdc", + "b13867d1757e4701979e369779a36b1d", + "248c1d4fd7954504a162cd65e2654659", + "db3c5e83433a4695a2eda04522adb860", + "a74d374942f941bdb95bbf8183bfe561", + "15ffea52c16541a7b5608a5e45065645", + "e5d96f8995ff45c4b8c1dfaa413b1cb0", + "05188c52d25f4122a15616def13bc6b2", + "9eb43df67c384979a683247a58491c10", + "fa7c4ea60c974f50acedabc2d39c7ec8", + "789ef01b508747449146c9cc9e9a1ec3", + "e11b9d49eb3b48ccbc1cd7c2a5a58e5e", + "d164d30c9c944e5da83aa012cb8492b5", + "2505d695a3284911be8d2f87a2210189", + "67b7dbd6f45d429290d2491804aa152c", + "46c6bb01e8834b6c8043a47b39dcc996", + "bb9ec874ecd941a39977288772b49e4d", + "7b25a228342647579be74885507b7b79", + "563dd15c351a4d9ebcd253a0f6f4f711", + "3fdd2677b12f49eea4423e41ed313677", + "5472d226dd5446939549c4479da544b1", + "065c923a447b43feb98c160b2a9931ba", + "cbead554d2e94f1fb9ad816456bfbd91", + "7f6fe07a34ff498b83c858a598663c50", + "7961267807694e3384974b8734ac8cfd", + "a692b687ab054611ac084b68c07436d8", + "572915e568774e25bf6d23fcc9ff5ab5", + "a26e8d8d74434037a2855b4205c5e16f", + "5b5b02f392ae487b8e228d136bf1bbf5" ] }, - "outputId": "c8e19834-144d-4a3a-8b0f-a6fc6434df89" + "outputId": "24ba3102-09e4-48d5-b0b4-3035c8bfe0c5" }, "outputs": [ { @@ -914,7 +915,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "4f9d977850ea479488a18af2fbd2c606" + "model_id": "1a5f63e9aadd42a98dd323d8afcd6f81" } }, "metadata": {} @@ -928,7 +929,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "1bf90be3e82a4b4b8fdd81ca719f9d94" + "model_id": "05188c52d25f4122a15616def13bc6b2" } }, "metadata": {} @@ -942,7 +943,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "21a737636bd34890b63f748e2ad6aea7" + "model_id": "563dd15c351a4d9ebcd253a0f6f4f711" } }, "metadata": {} @@ -971,20 +972,20 @@ "base_uri": "https://localhost:8080/", "height": 77, "referenced_widgets": [ - "7ba879ca444c40fcbc01c3e9bb0f3208", - "62aa619438e641aabfed848db4a843ab", - "efcd31b9d5b844d6bccae677edbeedd3", - "4fa5b1e3dfc549669694562b7d7e841e", - "4f0214a0fa1a4f2cb964b4572ef7bb0f", - "cc7a2dd92513482aa8c53633f5b72de4", - "8d590d64d491471eb526a42f57d426e8", - "d10479198a784d37b45bb09c427d6778", - "ed5830a1365e4d4ca48daf1d6b2d38d9", - "18c44f03461949b9aceb85dedc6b1292", - "360d2fd311604523b275be411d999ffb" + "ab27c043128341b68316a68a12e9e076", + "2e2ad2cc081740008faa6702f5f59b44", + "a759e80646a04569b623055621ec7c2e", + "392cf965aeb64b60add7e397200c4866", + "4193d905e8294ed086f5b1902a55f443", + "f740d4720e47404fab6a302c70647d96", + "deb0fa3589ea456dae2034da0f3c8c07", + "be0519a0fd5d4da5a38891860d91a827", + "955a0a85cf694f03bfd9875c7f1be2ab", + "d148202b13274d99a055ddfb94680b64", + "c978550e41f1436bac7e5123855b1495" ] }, - "outputId": "e565645e-1836-4e2d-9294-0644bd02db37" + "outputId": "3ffdb364-f90d-4ab1-bb6a-4a9eec9e0002" }, "outputs": [ { @@ -996,7 +997,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "7ba879ca444c40fcbc01c3e9bb0f3208" + "model_id": "ab27c043128341b68316a68a12e9e076" } }, "metadata": {} @@ -1024,7 +1025,7 @@ "base_uri": "https://localhost:8080/" }, "id": "dzE1OEXi7s3P", - "outputId": "9b14adb6-9f08-459a-beb6-2086acc412d4" + "outputId": "76a882d1-3978-4163-8de2-91ed5cdd12a0" }, "outputs": [ { @@ -1040,7 +1041,7 @@ ] }, "metadata": {}, - "execution_count": 15 + "execution_count": 28 } ], "source": [ @@ -1065,20 +1066,20 @@ "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ - "61414149c23e4cb6ba732efcdbeee408", - "af0213a2f1974b19948b169fa3abec7e", - "41d65ff1b5b84534b92a81a0a44e0610", - "e523826f7ede43958876fcb0d9c300c1", - "c835f882ec4c45d0bf95d3f6d17dd6d1", - "841fdfca229b413b90774789b8c90686", - "d3166e5eccdc43048f4e4b09725c1592", - "477c62ae6efb4c1fb2fa0e8b6ae70642", - "78bd3f2909d446caa30681de1bcb8793", - "0f63130623f548c7a30efaede52ee68d", - "47979d0b42dd40a8acc67f2b3fdac756" + "9f0054a51d9340e2907e53a80785b296", + "ebb491b75b1c415c80da59551defbd80", + "b43e8c761c4347e1a01c7aac5a937b9f", + "5080a79d9096444fa7ec24615b0d007d", + "ebb46c3a3eec4f14922bb2915e613bea", + "eacfcd95ef1c4b49a6ff8897b815efdd", + "35289ddb889645dfbb6c1688d8622d38", + "d880e1aec916469c94b4ffd907953f1b", + "bf561e6f36734965b3b3ca7b1049f6b6", + "23eddb8e8a1144258c7e6e409576e299", + "769edf9db000411a86fc4988b9940106" ] }, - "outputId": "0998981a-5404-4a13-a30d-4d11765df229" + "outputId": "094ebc7f-397a-4140-9a1d-82ee433ddc35" }, "outputs": [ { @@ -1090,7 +1091,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "61414149c23e4cb6ba732efcdbeee408" + "model_id": "9f0054a51d9340e2907e53a80785b296" } }, "metadata": {} @@ -1123,7 +1124,7 @@ "height": 210 }, "id": "gGFzmplrEy9I", - "outputId": "6fbf9365-198d-4346-8281-465f4840f207" + "outputId": "bb37d7ea-e713-439e-f099-4d7deb9b3102" }, "outputs": [ { @@ -1137,7 +1138,7 @@ } }, "metadata": {}, - "execution_count": 17 + "execution_count": 30 } ], "source": [ @@ -1163,21 +1164,21 @@ "base_uri": "https://localhost:8080/", "height": 77, "referenced_widgets": [ - "47419a17294948ceaa267ef9527aa837", - "bd0fdbe7f0604573ad8c3bc37a9dc198", - "ab03e3818d5748a4896575fd185340d0", - "9c30a95489304187968dc32e9d13f54d", - "9ec7beff192f4fb0ac423db202d290e3", - "212893e31d954120ba2ce19da79ab668", - "65da1d31689644ecb6d39ad3cadb180e", - "5cf72421beee4f409d0e4fc9bb3aac48", - "45ac61fb76df43f9aa4ea0b3691a9575", - "6bc32a460ccc4c468a5a92c026296c82", - "0e41ec04e37b45fba9cf6ecd03f4f708" + "48fdf447a1824adb8ee0b3f22f95b610", + "501c9d8673a9449dacf217b24912dfcf", + "64ed5d5d40924b52a1b93e52624306c5", + "88f995d2771c4ae2b6a8a26669634d1d", + "3b8cf2a785e14020b020284880f8b726", + "f4ac75045a5247fa913c47271437a910", + "c2cd9da6de8046bda143e1bf07c64913", + "d54c4382a5c44cc5bfae0f303dea15d3", + "391c6c8e88b74a97969e523513d16e96", + "ceb779b86cee4a168af951d0e5d9bbb3", + "8085aa45c2f64a6395c3ddfbc450accf" ] }, "id": "95_Nn-89DhsL", - "outputId": "5b9ee0a6-5856-4407-bcf6-d5f0def28572" + "outputId": "c3130963-d202-470e-832a-e1b604f0536a" }, "outputs": [ { @@ -1189,7 +1190,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "47419a17294948ceaa267ef9527aa837" + "model_id": "48fdf447a1824adb8ee0b3f22f95b610" } }, "metadata": {} @@ -1208,7 +1209,7 @@ " gradient_accumulation_steps = 4, # Use GA to mimic batch size!\n", " warmup_steps = 5,\n", " # num_train_epochs = 1, # Set this for 1 full training run.\n", - " max_steps = 30,\n", + " max_steps = 60,\n", " learning_rate = 2e-4, # Reduce to 2e-5 for long training runs\n", " logging_steps = 1,\n", " optim = \"adamw_8bit\",\n", @@ -1237,21 +1238,21 @@ "base_uri": "https://localhost:8080/", "height": 77, "referenced_widgets": [ - "2ef65ea327604296940f795c2bf48387", - "3c7c2b96ea81487489b43820a272ee38", - "0068588d59344fc2b8d30cef2ac3c774", - "ff6935e7285c4832985112e5322ae685", - "607904535f92469b891b03a9528010ec", - "360e0864919a44fcbdef5c0ea97bd965", - "680d6ff6187e4764bb85a85039bef9e1", - "2d21750180ad4241b9b76763e3e98be2", - "ce35ba2a1a5547e6954aa38c93ee249c", - "ee4c9bb74fc14cc2a10d7233f10b4524", - "112088813549415f8760ec33459f8304" + "42634f2e7a7e4d5ab0992bec45e6607d", + "17c8a4feff59499f86d82a09ab54c017", + "5c570bdcfcdf4e57a49ae752ceff50f2", + "1d1fb691357544a3a29032b14bbdd6b9", + "a4d8211a756646119b748641ea5d4b83", + "d25c40d318e24f7c832f4ebb9a884b9e", + "286de8ee29bd4c77a32446c2194777ef", + "88575c0ee2fa46349ac642642b37800f", + "b8c2462293dd480c85503bc08b194e09", + "2062ecc4fca44ad58b8963e84f7f09e4", + "3e5bc0d900e6454ba7d1d6a30839fa01" ] }, "id": "juQiExuBG5Bt", - "outputId": "cc73dd7a-171a-4d21-c26f-2e5d5c4c959e" + "outputId": "f25a861e-5ec5-4c2a-8e41-319522a73c0f" }, "outputs": [ { @@ -1263,7 +1264,7 @@ "application/vnd.jupyter.widget-view+json": { "version_major": 2, "version_minor": 0, - "model_id": "2ef65ea327604296940f795c2bf48387" + "model_id": "42634f2e7a7e4d5ab0992bec45e6607d" } }, "metadata": {} @@ -1296,7 +1297,7 @@ "height": 210 }, "id": "LtsMVtlkUhja", - "outputId": "f3ff4268-9a5e-4298-f757-9a124697c149" + "outputId": "bcdfba15-5bbf-43d3-c8f0-cf2355569af2" }, "outputs": [ { @@ -1310,7 +1311,7 @@ } }, "metadata": {}, - "execution_count": 20 + "execution_count": 33 } ], "source": [ @@ -1335,7 +1336,7 @@ "height": 210 }, "id": "_rD6fl8EUxnG", - "outputId": "94c75714-f5db-4854-f4cc-d832768ac5e2" + "outputId": "a4235fa9-2fbf-450b-c93d-ec6cf374504c" }, "outputs": [ { @@ -1349,7 +1350,7 @@ } }, "metadata": {}, - "execution_count": 21 + "execution_count": 34 } ], "source": [ @@ -1365,7 +1366,7 @@ "base_uri": "https://localhost:8080/" }, "id": "2ejIt2xSNKKp", - "outputId": "b2007315-61b6-4c50-f44f-128182fca757" + "outputId": "99592e08-f375-42b0-ecb0-0acf240a2720" }, "outputs": [ { @@ -1406,7 +1407,7 @@ "height": 1000 }, "id": "yqxqAZ7KJ4oL", - "outputId": "dd9488e9-eaba-4628-b89f-4b90a8663ad4" + "outputId": "8c368cf7-5f59-4de1-8a42-b5eb30c14ee4" }, "outputs": [ { @@ -1414,7 +1415,7 @@ "name": "stderr", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs used = 1\n", - " \\\\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 30\n", + " \\\\ /| Num examples = 3,000 | Num Epochs = 1 | Total steps = 60\n", "O^O/ \\_/ \\ Batch size per device = 1 | Gradient accumulation steps = 4\n", "\\ / Data Parallel GPUs = 1 | Total batch size (1 x 4 x 1) = 4\n", " \"-____-\" Trainable parameters = 19,210,240 of 4,000,000,000 (0.48% trained)\n", @@ -1438,8 +1439,8 @@ "\n", "
\n", " \n", - " \n", - " [30/30 03:57, Epoch 0/1]\n", + " \n", + " [60/60 07:15, Epoch 0/1]\n", "
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" @@ -1589,15 +1710,15 @@ "base_uri": "https://localhost:8080/" }, "id": "pCqnaKmlO1U9", - "outputId": "bf1534bd-8217-47ac-aefe-f655c1d0941b" + "outputId": "14236050-bc1c-4ca8-a9ab-1f66635839dc" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "338.048 seconds used for training.\n", - "5.63 minutes used for training.\n", + "542.1338 seconds used for training.\n", + "9.04 minutes used for training.\n", "Peak reserved memory = 12.592 GB.\n", "Peak reserved memory for training = 0.0 GB.\n", "Peak reserved memory % of max memory = 85.422 %.\n", @@ -1640,18 +1761,18 @@ "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", - "outputId": "098ca4bf-5590-4e46-a098-c1f04bbd7b1c" + "outputId": "758f6ed8-6c93-4bd5-aa3b-16b6546c6e1e" }, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ - "['user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\nmodel\\nThe sequence is the Fibonacci sequence. Each number is the sum of the two preceding numbers.\\n\\n1, 1, 2, 3, 5, 8, **13, 21, 34...**\\n\\nSo the next number is 13.\\n']" + "['user\\nContinue the sequence: 1, 1, 2, 3, 5, 8,\\nmodel\\n13, 21, 34, 55, 89, 144, 233, 377, 610, 987, 1597, 2584, 4181, 6']" ] }, "metadata": {}, - "execution_count": 25 + "execution_count": 38 } ], "source": [ @@ -1700,17 +1821,14 @@ "base_uri": "https://localhost:8080/" }, "id": "e2pEuRb1r2Vg", - "outputId": "1a8234b0-ac1e-4d75-bf57-65da5a4f7f67" + "outputId": "1dcf3cec-7485-497b-b19b-fa126bcbf223" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "The sky is blue because of a phenomenon called **Rayleigh scattering**. Here's a breakdown of what happens:\n", - "\n", - "* **Sunlight is made of all colors:** White sunlight is actually composed of all the colors of the rainbow (red, orange, yellow, green, blue, indigo, violet).\n", - "* **\n" + "The sky is blue because of a phenomenon called Rayleigh scattering. Sunlight is made up of all the colors of the rainbow, but blue light has a shorter wavelength than red light. When sunlight enters the Earth's atmosphere, it collides with tiny air molecules (mostly nitrogen and oxygen). This collision causes the blue light to\n" ] } ], @@ -1758,7 +1876,7 @@ "base_uri": "https://localhost:8080/" }, "id": "upcOlWe7A1vc", - "outputId": "2c2aa03d-6116-4bb5-fdfa-0527718f8ffd" + "outputId": "f8bc2909-78be-4999-d1e8-b9fc72d2c377" }, "outputs": [ { @@ -1769,7 +1887,7 @@ ] }, "metadata": {}, - "execution_count": 27 + "execution_count": 40 } ], "source": [ @@ -1796,17 +1914,18 @@ "base_uri": "https://localhost:8080/" }, "id": "MKX_XKs_BNZR", - "outputId": "3c3453e6-24ce-4080-a9de-e4936872eb20" + "outputId": "c428817d-ffe8-4470-9ed6-4fd5118d4bca" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ - "Gemma-3N is a family of open-weights large language models (LLMs) created by the Gemma team at Google DeepMind. Here's a breakdown of what that means:\n", + "Gemma-3N is a **new open-weight large language model (LLM)** developed by **Google DeepMind**. It's designed to be **accessible to a wider range of users**, including researchers and developers, by being **open-weight**. This means the model's weights are publicly available, allowing for more experimentation and customization.\n", + "\n", + "Here's a breakdown of what Gemma-3N is:\n", "\n", - "* **Open-weights:** This is a key aspect. \"Open-weights\" means the model's parameters (the learned values within the model) are publicly available. This allows researchers, developers, and enthusiasts to download, use, and fine-tune the model for their own purposes, fostering innovation and transparency in the field of AI.\n", - "* **Large Language Model (LLM):** Gemma-3N is a\n" + "* **Large Language Model (LLM):** Gemma-3N is a powerful AI model trained on a massive dataset of text and code. This allows it to understand and generate human-like text, translate\n" ] } ], @@ -1990,7 +2109,7 @@ }, "widgets": { "application/vnd.jupyter.widget-state+json": { - "40ed46c9e0624662827489732729b513": { + "a18e4f1b115043238b23dfa89d4aadfc": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -2005,14 +2124,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - "IPY_MODEL_1348559ff1864cd8b9cd95c9802916f7", - "IPY_MODEL_a845170c1d2f44a6b60a6e28d3b99afd", - "IPY_MODEL_d0d728ebd0d540e8973180cf9a048b93" + "IPY_MODEL_b89a448d6f6a4740a6669e97362ff010", + "IPY_MODEL_d523dd9488c74a9ea7b54b6ded8d5995", + "IPY_MODEL_450e64cd49264777a7aa9b21340cd7f4" ], - "layout": "IPY_MODEL_13b7efc84b2c43418866422966afee79" + "layout": "IPY_MODEL_f73eeed5ba2b426fb221945733e48a5a" } }, - "1348559ff1864cd8b9cd95c9802916f7": { + "b89a448d6f6a4740a6669e97362ff010": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -2027,13 +2146,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_d2af29bc58994b0d8c930e4e1c718c64", + "layout": "IPY_MODEL_59218d73bed84d77b48ea4560f759634", "placeholder": "​", - "style": "IPY_MODEL_f0aa2b81446a41f4b387753f5361aec2", + "style": "IPY_MODEL_c55fec1fce124357bd96dfc09e0f77a4", "value": "model.safetensors.index.json: " } }, - "a845170c1d2f44a6b60a6e28d3b99afd": { + "d523dd9488c74a9ea7b54b6ded8d5995": { "model_module": "@jupyter-widgets/controls", "model_name": "FloatProgressModel", "model_module_version": "1.5.0", @@ -2049,15 +2168,15 @@ "bar_style": "success", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_203f0a1dca8347fa8095b9e656965d4a", + "layout": "IPY_MODEL_32a3f300caba43e8bf90e29a8729c0df", "max": 1, "min": 0, "orientation": "horizontal", - "style": "IPY_MODEL_d2ec7f8181f44b61a2f943417a4b6791", + "style": "IPY_MODEL_cebf26dc6eba4886a20888574ad69fdc", "value": 1 } }, - "d0d728ebd0d540e8973180cf9a048b93": { + "450e64cd49264777a7aa9b21340cd7f4": { "model_module": "@jupyter-widgets/controls", "model_name": "HTMLModel", "model_module_version": "1.5.0", @@ -2072,13 +2191,13 @@ "_view_name": "HTMLView", "description": "", "description_tooltip": null, - "layout": "IPY_MODEL_93de22d41af74a3cba1b32c4f3eec264", + "layout": "IPY_MODEL_a46ffff5a95c489ab77f538cc0a776d1", "placeholder": "​", - "style": "IPY_MODEL_62e353c496784d3eafff5153085e7c35", - "value": " 370k/? [00:00<00:00, 19.1MB/s]" + "style": "IPY_MODEL_e15f8583441842d8b9bdc4de9b590950", + "value": " 370k/? [00:00<00:00, 21.7MB/s]" } }, - "13b7efc84b2c43418866422966afee79": { + "f73eeed5ba2b426fb221945733e48a5a": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2130,7 +2249,7 @@ "width": null } }, - "d2af29bc58994b0d8c930e4e1c718c64": { + "59218d73bed84d77b48ea4560f759634": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2182,7 +2301,7 @@ "width": null } }, - "f0aa2b81446a41f4b387753f5361aec2": { + "c55fec1fce124357bd96dfc09e0f77a4": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -2197,7 +2316,7 @@ "description_width": "" } }, - "203f0a1dca8347fa8095b9e656965d4a": { + "32a3f300caba43e8bf90e29a8729c0df": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2249,7 +2368,7 @@ "width": "20px" } }, - "d2ec7f8181f44b61a2f943417a4b6791": { + "cebf26dc6eba4886a20888574ad69fdc": { "model_module": "@jupyter-widgets/controls", "model_name": "ProgressStyleModel", "model_module_version": "1.5.0", @@ -2265,7 +2384,7 @@ "description_width": "" } }, - "93de22d41af74a3cba1b32c4f3eec264": { + "a46ffff5a95c489ab77f538cc0a776d1": { "model_module": "@jupyter-widgets/base", "model_name": "LayoutModel", "model_module_version": "1.2.0", @@ -2317,7 +2436,7 @@ "width": null } }, - "62e353c496784d3eafff5153085e7c35": { + "e15f8583441842d8b9bdc4de9b590950": { "model_module": "@jupyter-widgets/controls", "model_name": "DescriptionStyleModel", "model_module_version": "1.5.0", @@ -2332,7 +2451,7 @@ "description_width": "" } }, - "c8e48086a51b43949f57d59246fb630a": { + "c35e11c09d394901ad68692c6ef7c7bb": { "model_module": "@jupyter-widgets/controls", "model_name": "HBoxModel", "model_module_version": "1.5.0", @@ -2347,14 +2466,14 @@ "_view_name": "HBoxView", "box_style": "", "children": [ - 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