![](\"https://i.postimg.cc/KzQ5rfBC/button.png\"width=\"150\")
\n",
+ " ![](\"https://i.postimg.cc/L4wPdG9N/join-2.png\")
\n",
+ " \n",
+ "⭐ Star us on [*Github*](https://github.com/camel-ai/camel), join our [*Discord*](https://discord.camel-ai.org) or follow our [*X*](https://x.com/camelaiorg)\n",
+ "\n",
+ " \n",
+ " \n",
+ "⭐ Star us on [*Github*](https://github.com/camel-ai/camel), join our [*Discord*](https://discord.camel-ai.org) or follow our [*X*](https://x.com/camelaiorg)\n",
+ "| 3 | \n", - "0.729900 | \n", + "0.729800 | \n", "
| 4 | \n", @@ -1736,7 +1740,7 @@ "||
| 5 | \n", - "0.690400 | \n", + "0.690500 | \n", "
| 6 | \n", @@ -1744,19 +1748,19 @@ "||
| 7 | \n", - "0.565400 | \n", + "0.565500 | \n", "
| 8 | \n", - "0.421000 | \n", + "0.421100 | \n", "
| 9 | \n", - "0.398200 | \n", + "0.398400 | \n", "
| 10 | \n", - "0.378000 | \n", + "0.378300 | \n", "
| 11 | \n", @@ -1764,11 +1768,11 @@ "||
| 12 | \n", - "0.267600 | \n", + "0.267700 | \n", "
| 13 | \n", - "0.225200 | \n", + "0.225400 | \n", "
| 14 | \n", @@ -1776,7 +1780,7 @@ "||
| 15 | \n", - "0.165100 | \n", + "0.165200 | \n", "
| 16 | \n", @@ -1784,7 +1788,7 @@ "||
| 17 | \n", - "0.134900 | \n", + "0.135000 | \n", "
| 18 | \n", @@ -1796,19 +1800,19 @@ "||
| 20 | \n", - "0.116200 | \n", + "0.116300 | \n", "
| 21 | \n", - "0.080900 | \n", + "0.081000 | \n", "
| 22 | \n", - "0.095500 | \n", + "0.095600 | \n", "
| 23 | \n", - "0.082200 | \n", + "0.082300 | \n", "
| 24 | \n", @@ -1816,7 +1820,7 @@ "||
| 25 | \n", - "0.044200 | \n", + "0.044300 | \n", "
| 26 | \n", @@ -1824,7 +1828,7 @@ "||
| 27 | \n", - "0.035800 | \n", + "0.035900 | \n", "
| 28 | \n", @@ -1832,7 +1836,7 @@ "||
| 29 | \n", - "0.040500 | \n", + "0.040600 | \n", "
| 30 | \n", @@ -1840,7 +1844,7 @@ "||
| 31 | \n", - "0.036500 | \n", + "0.036600 | \n", "
| 32 | \n", @@ -1848,19 +1852,19 @@ "||
| 33 | \n", - "0.027300 | \n", + "0.027400 | \n", "
| 34 | \n", - "0.019900 | \n", + "0.020000 | \n", "
| 35 | \n", - "0.023600 | \n", + "0.023700 | \n", "
| 36 | \n", - "0.017600 | \n", + "0.017500 | \n", "
| 37 | \n", @@ -1892,7 +1896,7 @@ "||
| 44 | \n", - "0.010500 | \n", + "0.010600 | \n", "
| 45 | \n", @@ -1908,7 +1912,7 @@ "||
| 48 | \n", - "0.007200 | \n", + "0.007300 | \n", "
| 49 | \n", @@ -1936,7 +1940,7 @@ "||
| 55 | \n", - "0.007600 | \n", + "0.007500 | \n", "
| 56 | \n", @@ -1982,19 +1986,19 @@ "base_uri": "https://localhost:8080/" }, "id": "f65fdb42", - "outputId": "f5d14989-b52b-444a-f7e4-258b03cdba9a" + "outputId": "265ef734-8f06-4508-9cc0-253630c1dac1" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "211.8509 seconds used for training.\n", - "3.53 minutes used for training.\n", - "Peak reserved memory = 2.852 GB.\n", - "Peak reserved memory for training = 1.247 GB.\n", - "Peak reserved memory % of max memory = 19.338 %.\n", - "Peak reserved memory for training % of max memory = 8.455 %.\n" + "204.267 seconds used for training.\n", + "3.4 minutes used for training.\n", + "Peak reserved memory = 2.207 GB.\n", + "Peak reserved memory for training = 0.672 GB.\n", + "Peak reserved memory % of max memory = 14.965 %.\n", + "Peak reserved memory for training % of max memory = 4.557 %.\n" ] } ], @@ -2032,7 +2036,7 @@ "base_uri": "https://localhost:8080/" }, "id": "BQ7pEtzR3OkV", - "outputId": "618e381d-ae09-4691-8a08-a1f9de3ee4cb" + "outputId": "cf9d8752-c85c-4d43-c6b9-9908692f664c" }, "outputs": [ { @@ -2163,49 +2167,49 @@ "base_uri": "https://localhost:8080/", "height": 131, "referenced_widgets": [ - "883f879d325a4a748cac05ea05bdc818", - "2d15180438034f16b5a5538ebc18c2ff", - "65e050fff2334626bd0d1501a544521b", - "fba20453154a4f5cb11118be8f67e0d8", - "ab8b2d5ed08940fbb499df84a6c246cf", - "44ac6c6360ca40c7ab56f2418a117f32", - "0ef6f1e943a94c55b82b610b60b2a9be", - "1aef77b568044c348373944fcc387e5b", - "8e48e0de3e744614bbdebce46d5130a3", - "458a2f67158148e19bbf35e56192b661", - "7469677a97834475878109a1388e0139", - "0b102cb5ec6646bcab35035311829755", - "db2307d2ccdb48c79bf9b4d92c229016", - "c87f2e1ede414b16a4c366005deddb0a", - "0629515ecf944f619fd19b4ecc7679f0", - "afb069118d5c4ac6b75d41188fab9ab2", - "0457c1749a7547ffb3a844d85f4be794", - "12a01ae5dbdc47e6bd3bac0f6e0602b8", - "8dd38510e36a4ac7bb4181f29be07dc5", - "8a2c34e5ffc24ec3901940d8cc2d1797", - "a6a5ac3b470c4835876fc21361418e07", - "0200213fb84d45228aea3e9f1c513f80", - "7e317990ce10406897bb0b5c43757240", - "f4ed00d4c17342e5a3658ddfdd10a046", - "5e482aef22404a6db58d3f6250ed73f3", - "e670e5d703b24663b1d6c4d788ef7f94", - "e5801dcc9a1943c0a490618e282d5b39", - "bbd92ef0918a4f78b641b5a5deaeea7f", - "df5787bd3c8a427ab5c4d4bd2a22be72", - "1063a6e136674eeb9789f30f57e02486", - "c3e44c4be55a4ad1aa7339eaa2689ff8", - "09bbc6656f6f40e8a321bbb62713cdfb", - "69dbadf0240343c5977dd07a54216b26" + "41a562f6eca54ebd8feb03912b58f499", + "c837ea6664ff47f7a9f36093dc4bb6a1", + "96b9e767294f444ca9229d0fb1a7ce16", + "57f7fdcd7fa148989c11215f888f0b36", + "557e29105ee142409e35b7fd4986c67e", + "5e4465774e9d4ee98a93f4934f2a2fa6", + "98e533ae225544ee844ccfcf79cd4bbe", + "74de6170124941b5a4d0e4ac60c9faf3", + "07e684b62ae04b08914797d90949e585", + "55330d82704142559d825ec88069c57a", + "0a6010a09ba2428b8134009ecdd70d92", + "89c5dbda9ae24622a70cd8b12b2650f2", + "b6cb78b42981483c875c86b9992c4953", + "7d7cf318745440d8a78894dec9d64f33", + "98c397a9e9bf4d7e9cdfaf961a515ff4", + "932f493ff7c541e99ebbf4d3f618bfd4", + "397724c3ff6549499cd259cf949e7787", + "44ac4322d33144f580dd63dff58ab6dd", + "e773feab1dee4c03a629994f60db22c3", + "92ae22adbdab447c945b6989cbc07064", + "ba078357ce3e4a509a5d3544347e00c6", + "a5e736d956ea4968911738340d0bf3f0", + "9e7432a259834b41ae3d302673df8908", + "71eb4b0a1c5c43e2a7b045dbf6e78ef4", + "d6b5d0c0044647d88ead617b74ddc2f2", + "d333db733fb048d3a84fb05d53215eca", + "2040fcb4700045af9ab87dda8c45112a", + "c41577a7eebd4fd6a7fce1261e24e685", + "4181120742874037b29597158c8a864f", + "0eb3e584b6c8423abe1143076c508a04", + "338fcd59ee6d440290373b4a6a0c6441", + "42a657f4e2154edfb59b0f04e2e549ea", + "dbf650311ba443c4bdb5e21eca0e639c" ] }, "id": "53b39972", - "outputId": "25916257-a1bd-4170-d1c4-172f74d6ff13" + "outputId": "f9b13271-2406-4eb8-fe42-d8d02dc117bf" }, "outputs": [ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "883f879d325a4a748cac05ea05bdc818", + "model_id": "41a562f6eca54ebd8feb03912b58f499", "version_major": 2, "version_minor": 0 }, @@ -2219,7 +2223,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "0b102cb5ec6646bcab35035311829755", + "model_id": "89c5dbda9ae24622a70cd8b12b2650f2", "version_major": 2, "version_minor": 0 }, @@ -2234,13 +2238,13 @@ "name": "stdout", "output_type": "stream", "text": [ - "Saved model to https://huggingface.co/zjrwtxtechstudio/qwen2.5-1.5b-math-test\n" + "Saved model to https://huggingface.co/zjrwtxtechstudio/qwen2.5-1.5b-cot\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "7e317990ce10406897bb0b5c43757240", + "model_id": "9e7432a259834b41ae3d302673df8908", "version_major": 2, "version_minor": 0 }, @@ -2255,8 +2259,8 @@ "source": [ "model.save_pretrained(\"lora_model\") # Local saving\n", "tokenizer.save_pretrained(\"lora_model\")\n", - "model.push_to_hub(\"zjrwtxtechstudio/qwen2.5-1.5b-math-test\", token = \"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\") # Online saving\n", - "tokenizer.push_to_hub(\"zjrwtxtechstudio/qwen2.5-1.5b-math-test\", token = \"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\") # Online saving" + "model.push_to_hub(\"zjrwtxtechstudio/qwen2.5-1.5b-cot\", token = \" \") # Online saving\n", + "tokenizer.push_to_hub(\"zjrwtxtechstudio/qwen2.5-1.5b-cot\", token = \" \") # Online saving" ] }, { @@ -2278,31 +2282,31 @@ "base_uri": "https://localhost:8080/", "height": 819, "referenced_widgets": [ - "85178a8201224c2b87173047ca51afb3", - "fc554ee941fc480781212ee242562da2", - "26c506290f3641df90d15f9718c2967e", - "45f554bddf82426589fb38227c91a7bf", - "f78b0852919d41feb382c74341b5863c", - "a651a33f7e2c4d569313878ea3fc7fb6", - "d7eb9024697c48b0b333c5a345d112b1", - "b8ef5811284a4313a2aa6dc2b4718f3d", - "d3808faedeb84d7f82c336a7b3f90163", - "4250c7192032407cb2ceb6bf74a785d1", - "3bb1a18fc16b43e3a1e95e97250cadd6" + "7c89f36061f742778c34c96a90d08fce", + "9c865068da7e488b9ed3f553277e5129", + "50d4f85319b54b06ad1e28a0fc43c95e", + "782a83d4b93549c298a105bae49da610", + "1f0fdbf7788a45e7a54026154b5339cf", + "3aa39df2d67d46e4b5b3f8409968a6ba", + "96da2777cf05468d864920c1fd3dc9bb", + "bfb51db311764295b14537530f8d3f0d", + "19affd5a6882484ba26568a32ab7af27", + "77def2f1a6f14bddaaed6f6461f779bb", + "d430927a5c9a4f95a1fd954ca01b4d1c" ] }, "id": "9150c9e3", - "outputId": "523655ad-da5b-435c-8949-956c064bfd2a" + "outputId": "3a41713f-fa86-4f62-e9c9-8c1d4b5f28b2" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "==((====))== Unsloth 2024.12.11: Fast Qwen2 patching. Transformers: 4.47.1.\n", + "==((====))== Unsloth 2025.1.5: Fast Qwen2 patching. Transformers: 4.47.1.\n", " \\\\ /| GPU: Tesla T4. Max memory: 14.748 GB. Platform: Linux.\n", "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 7.5. CUDA Toolkit: 12.1. Triton: 3.1.0\n", - "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29. FA2 = False]\n", + "\\ / Bfloat16 = FALSE. FA [Xformers = 0.0.29.post1. FA2 = False]\n", " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" ] @@ -2310,7 +2314,7 @@ { "data": { "application/vnd.jupyter.widget-view+json": { - "model_id": "85178a8201224c2b87173047ca51afb3", + "model_id": "7c89f36061f742778c34c96a90d08fce", "version_major": 2, "version_minor": 0 }, @@ -2368,7 +2372,7 @@ "if True:\n", " from unsloth import FastLanguageModel\n", " model, tokenizer = FastLanguageModel.from_pretrained(\n", - " model_name = \"zjrwtxtechstudio/qwen2.5-1.5b-math-test\", # YOUR MODEL YOU USED FOR TRAINING\n", + " model_name = \"zjrwtxtechstudio/qwen2.5-1.5b-cot\", # YOUR MODEL YOU USED FOR TRAINING\n", " max_seq_length = max_seq_length,\n", " dtype = dtype,\n", " load_in_4bit = load_in_4bit,\n", @@ -2423,14 +2427,29 @@ }, { "cell_type": "markdown", - "id": "oeH6__bN7Yt2", + "id": "xyK95yTFYku_", "metadata": { - "id": "oeH6__bN7Yt2" + "id": "xyK95yTFYku_" }, "source": [ - "⭐ **Star the Repo**\n", + "That's everything: Got questions about 🐫 CAMEL-AI? Join us on [Discord](https://discord.camel-ai.org)! Whether you want to share feedback, explore the latest in multi-agent systems, get support, or connect with others on exciting projects, we’d love to have you in the community! 🤝\n", "\n", - "If you find CAMEL useful or interesting, please consider giving it a star on [GitHub](https://github.com/camel-ai/camel)! Your stars help others find this project and motivate us to continue improving it." + "Check out some of our other work:\n", + "1. 🐫 Creating Your First CAMEL Agent [free Colab](https://docs.camel-ai.org/cookbooks/create_your_first_agent.html)\n", + "2. Graph RAG Cookbook [free Colab](https://colab.research.google.com/drive/1uZKQSuu0qW6ukkuSv9TukLB9bVaS1H0U?usp=sharing)\n", + "3. 🧑⚖️ Create A Hackathon Judge Committee with Workforce [free Colab](https://colab.research.google.com/drive/18ajYUMfwDx3WyrjHow3EvUMpKQDcrLtr?usp=sharing)\n", + "4. 🔥 3 ways to ingest data from websites with Firecrawl & CAMEL [free Colab](https://colab.research.google.com/drive/1lOmM3VmgR1hLwDKdeLGFve_75RFW0R9I?usp=sharing)\n", + "5. 🦥 Agentic SFT Data Generation with CAMEL and Mistral Models, Fine-Tuned with Unsloth [free Colab](https://colab.research.google.com/drive/1lYgArBw7ARVPSpdwgKLYnp_NEXiNDOd-?usp=sharingg)\n", + "\n", + "Thanks from everyone at 🐫 CAMEL-AI\n", + "\n", + "\n", + "