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"\u001b[?25hInstalling collected packages: xxhash, shtab, protobuf, hf_transfer, fsspec, dill, multiprocess, tyro, xformers, datasets, cut_cross_entropy, bitsandbytes, trl, unsloth_zoo, unsloth\n", - " Attempting uninstall: protobuf\n", - " Found existing installation: protobuf 4.25.5\n", - " Uninstalling protobuf-4.25.5:\n", - " Successfully uninstalled protobuf-4.25.5\n", - " Attempting uninstall: fsspec\n", - " Found existing installation: fsspec 2024.10.0\n", - " Uninstalling fsspec-2024.10.0:\n", - " Successfully uninstalled fsspec-2024.10.0\n", - "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "tensorflow-metadata 1.16.1 requires protobuf<6.0.0dev,>=4.25.2; python_version >= \"3.11\", but you have protobuf 3.20.3 which is incompatible.\n", - "grpcio-status 1.62.3 requires protobuf>=4.21.6, but you have protobuf 3.20.3 which is incompatible.\n", - "gcsfs 2024.10.0 requires fsspec==2024.10.0, but you have fsspec 2024.9.0 which is incompatible.\u001b[0m\u001b[31m\n", - "\u001b[0mSuccessfully installed bitsandbytes-0.45.0 cut_cross_entropy-25.1.1 datasets-3.2.0 dill-0.3.8 fsspec-2024.9.0 hf_transfer-0.1.9 multiprocess-0.70.16 protobuf-3.20.3 shtab-1.7.1 trl-0.13.0 tyro-0.9.11 unsloth-2025.1.6 unsloth_zoo-2025.1.4 xformers-0.0.29.post1 xxhash-3.5.0\n" - ] - }, - { - "output_type": "display_data", - "data": { - "application/vnd.colab-display-data+json": { - "pip_warning": { - "packages": [ - "google" - ] - }, - "id": "4b27fccea4bc457284a959874ed44405" - } - }, - "metadata": {} - }, - { - "output_type": "stream", - 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" Stored in directory: /root/.cache/pip/wheels/d0/a3/f9/48d2706cb2eac05ec0dc144bf6954fe47bb3c2cd0de280765e\n", - "Successfully built flash-attn\n", - "Installing collected packages: ninja, flash-attn\n", - "Successfully installed flash-attn-2.7.3 ninja-1.11.1.3\n" - ] - } - ] - }, - { - "cell_type": "code", - "source": [ - "! pip install unsloth --upgrade --no-cache-dir\n", - "import torch\n", - "if torch.cuda.get_device_capability()[0] >= 8:\n", - " !pip install --no-deps packaging ninja einops \"flash-attn>=2.6.3\"" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "tKd4YOAuYn80", - "outputId": "b74e2198-6d4f-46b0-ba17-31b23cf97301" - }, - "execution_count": 15, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: unsloth in /usr/local/lib/python3.11/dist-packages (2025.1.6)\n", - "Requirement already satisfied: unsloth_zoo>=2025.1.4 in /usr/local/lib/python3.11/dist-packages (from unsloth) (2025.1.4)\n", - 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] - } - ] - }, - { - "cell_type": "code", - "source": [ - "from unsloth import FastLanguageModel\n", - "import torch\n", - "max_seq_length = 2048\n", - "dtype = None\n", - "load_in_4bit = False\n", - "model, tokenizer = FastLanguageModel.from_pretrained(model_name = \"Qwen/Qwen2.5-14B-Instruct\", max_seq_length = max_seq_length, dtype = dtype, load_in_4bit = load_in_4bit,)\n", - "\n", - "################## MODELS THAT CAN BE USED : ~2-3B RANGE ###########################\n", - "# google/gemma-2-2b-it\n", - "# meta-llama/Llama-3.2-3B-Instruct\n", - "# Qwen/Qwen2.5-3B-Instruct\n", - "# microsoft/Phi-3.5-mini-instruct\n", - "################## MODELS THAT CAN BE USED : ~7-9B RANGE ###########################\n", - "# meta-llama/Llama-3.1-8B-Instruct\n", - "# google/gemma-2-9b-it\n", - "# Qwen/Qwen2.5-7B-Instruct\n", - "# mistralai/Mistral-7B-Instruct-v0.3\n", - "################## MODELS THAT CAN BE USED : ~12-14B RANGE ###########################\n", - "# Qwen/Qwen2.5-14B-Instruct\n", - "# mistralai/Mistral-Nemo-Instruct-2407\n", - "# microsoft/phi-4" - ], - "metadata": { - "colab": { - "base_uri": "https://localhost:8080/", - "height": 742, - "referenced_widgets": [ - "60ac3e9d2b7547429c567198fa3661f8", - "d829a3acb4c744878af6e7c3a1295ef3", - "21e60cb9b9234053ad6159c2e2913fd2", - "d02b79c15a444065aa2a4487032d696b", - "4abd0eb5dbf94eccb2c524b0669fd3bf", - "17c1d7e96f874ec890cffeda2748acdf", - "819a882940d74b1c8d658a8784601a8d", - "8ad4bb0f96fd4f6ea38808aebdb1a99d", - "14ec58338dab46dbb743da367098f0ae", - "33efa28f07d84c6497e3718376761899", - "cd007214255f4a55a9cfe4e97e54eb84", - "23e025fb5d914ee7aa3c6a94332fdfe7", - "a7aeb478726b48b4a86ca2383952c240", - "b41732d34c334280b7bb81fb940fc0ea", - "f4c83428f7014a80ac2f27c0ac51074d", - "a2d97fae517c4cc5a9ab1bc6f1d310b9", - "13ee59d1658e4a8d916293b6cf7296e1", - "849b6ba59e2a4a1baed39d81d2dda502", - "a1a21580e34f43b994008037176ce546", - "4be4f08dd38140f8ba31f5161f9a6b82", - "20e2396870be4c9fbe1a9bfb651aa0b7", - "32499dc184fa49acb229ef6caad64aba", - 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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" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "0it [00:00, ?it/s]" - ], - "application/vnd.jupyter.widget-view+json": { - "version_major": 2, - "version_minor": 0, - "model_id": "60ac3e9d2b7547429c567198fa3661f8" - } - }, - "metadata": {} - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "🦥 Unsloth Zoo will now patch everything to make training faster!\n", - "==((====))== Unsloth 2025.1.6: Fast Qwen2 patching. Transformers: 4.47.1.\n", - " \\\\ /| GPU: NVIDIA A100-SXM4-40GB. Max memory: 39.564 GB. Platform: Linux.\n", - "O^O/ \\_/ \\ Torch: 2.5.1+cu121. CUDA: 8.0. CUDA Toolkit: 12.1. Triton: 3.1.0\n", - "\\ / Bfloat16 = TRUE. FA [Xformers = 0.0.29.post1. FA2 = True]\n", - " \"-____-\" Free Apache license: http://github.com/unslothai/unsloth\n", - "Unsloth: Fast downloading is enabled - ignore downloading bars which are red colored!\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "model.safetensors.index.json: 0%| | 0.00/47.5k [00:00\n", - "
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InputOutputLANGtypesubject_nametopic;subject
0Sometimes the object module produced by a comp...AenNaNNaNNaNcollege_computer_science
1चेन्नई में लोकप्रिय व्यंजन कौन से हैं? ### लंब...डोसा, इडली, चावल, मिठाई।hiNaNNaNNaNNaN
2A student has to obtain 60% of the total marks...Let's denote the maximum marks as M.\\n\\nTo pas...enNaNNaNNaNNaN
3Karwar Style Ambade Udid Methi Recipe - Hog Pl...To begin making the Karwar Style Ambade Udid M...enNaNNaNNaNNaN
4How does string theory propose to solve the in...String theory proposes to solve the informatio...enNaNNaNString theoryNaN
........................
43385What role does tax policy play in the regulati...Tax policy plays a significant role in regulat...enNaNNaNNaNNaN
43386एक पायथन फ़ंक्शन लिखें जो दिए गए ऐरे से सबसे छ...def find_First_Missing(array,start,end): \\r\\n ...hiNaNNaNNaNNaN
43387\"How do gravitational waves affect the orbital...Gravitational waves are ripples in the fabric ...enNaNNaNGravitational wavesNaN
43388Plants need iron to grow. What parts of plants...AenNaNNaNNaNNaN
43389Let $a$ and $b$ be positive real numbers such ...By AM-HM,\\n\\[\\frac{a + b + b}{3} \\ge \\frac{3}{...enIntermediate AlgebraNaNNaNNaN
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Which of the following soil changes is due only to natural causes? ### ['A: degradation of nutrients due to pesticides', 'B: formation of deserts due to tree felling', 'C: flooding due to dam construction', 'D: removal of nutrients due to heavy rains'] ### MCQ ### \",\n \"Given a set of symptoms, provide a list of potential diagnoses that could explain the symptoms. ### Symptoms: Nausea, vomiting, abdominal pain, fever ### LONG RESPONSE ### \"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"Output\",\n \"properties\": {\n \"dtype\": \"string\",\n \"num_unique_values\": 23514,\n \"samples\": [\n \"To calculate the kinetic energy gained by a proton as it undergoes relativistic acceleration, we need to use the relativistic kinetic energy formula:\\n\\nK = (\\u03b3 - 1)mc^2\\n\\nwhere K is the kinetic energy, \\u03b3 (gamma) is the Lorentz factor, m is the rest mass of the proton, and c is the speed of light.\\n\\nFirst, we need to find the Lorentz factor (\\u03b3) using the formula:\\n\\n\\u03b3 = 1 / sqrt(1 - v^2/c^2)\\n\\nwhere v is the final speed of the proton (0.8c).\\n\\n\\u03b3 = 1 / sqrt(1 - (0.8c)^2/c^2)\\n\\u03b3 = 1 / sqrt(1 - 0.64)\\n\\u03b3 = 1 / sqrt(0.36)\\n\\u03b3 = 1 / 0.6\\n\\u03b3 \\u2248 1.667\\n\\nNow, we can calculate the kinetic energy:\\n\\nK = (\\u03b3 - 1)mc^2\\nK = (1.667 - 1)(1.67\\u00d710^-27 kg)(3\\u00d710^8 m/s)^2\\nK \\u2248 0.667 \\u00d7 1.67\\u00d710^-27 kg \\u00d7 9\\u00d710^16 m^2/s^2\\nK \\u2248 1.001 \\u00d7 10^-10 J\\n\\nSo, the kinetic energy gained by the proton is approximately 1.001 \\u00d7 10^-10 Joules.\",\n \"Cosmic rays are high-energy particles, primarily protons and atomic nuclei, originating from outer space. They constantly bombard the Earth and its atmosphere, and can also interact with satellite electronics in orbit. The effects of cosmic rays on satellite electronics can be detrimental, causing various issues such as:\\n\\n1. Single Event Effects (SEEs): Cosmic rays can cause temporary or permanent malfunctions in electronic components by depositing their energy in the semiconductor material. This can lead to single event upsets (SEUs), single event latch-ups (SELs), and single event burnouts (SEBs), which can disrupt the normal operation of the satellite.\\n\\n2. Total Ionizing Dose (TID): The cumulative ionizing radiation dose received by the satellite electronics over time can cause degradation of the semiconductor material, leading to performance issues and reduced lifetime of the components.\\n\\n3. Displacement Damage: Cosmic rays can displace atoms in the semiconductor lattice, causing defects that can affect the performance of electronic devices, such as increased leakage current and reduced carrier mobility.\\n\\nTo mitigate the effects of cosmic rays on satellite electronics, several strategies can be employed:\\n\\n1. Shielding: Using shielding materials, such as aluminum or polyethylene, can help absorb or deflect some of the incoming cosmic rays, reducing their impact on the electronics. However, this can also increase the satellite's mass, which may not be desirable.\\n\\n2. Radiation-hardened components: Designing and using radiation-hardened electronic components can help resist the effects of cosmic rays. These components are specifically designed to withstand the harsh radiation environment in space.\\n\\n3. Error detection and correction: Implementing error detection and correction techniques, such as parity checks and redundant systems, can help identify and correct errors caused by cosmic rays, ensuring the continued operation of the satellite.\\n\\n4. Redundancy: Incorporating redundant systems and components can help maintain the functionality of the satellite in case of a failure caused by cosmic rays. This can include using multiple identical components or having backup systems in place.\\n\\n5. Regular monitoring and maintenance: Continuously monitoring the performance of satellite electronics and conducting regular maintenance can help identify and address any issues caused by cosmic rays before they become critical.\\n\\nIn summary, cosmic rays can cause various issues in satellite electronics, affecting their function and performance. To mitigate these effects, a combination of shielding, radiation-hardened components, error detection and correction, redundancy, and regular monitoring and maintenance can be employed.\",\n \"Day 1: Marina Beach, Kapaleeshwarar Temple. Day 2: Mahabalipuram, Fort St. George. Day 3: Guindy National Park, DakshinaChitra. Day 4: Day of rest or local exploration. Day 5: Vadapalani Murugan Temple, Koyambedu Velankanni Church.\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"LANG\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 2,\n \"samples\": [\n \"hi\",\n \"en\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"type\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 7,\n \"samples\": [\n \"Counting & Probability\",\n \"Precalculus\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject_name\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 21,\n \"samples\": [\n \"Radiology\",\n \"ENT\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"topic;\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 97,\n \"samples\": [\n \"Set theory\",\n \"Ecology\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n },\n {\n \"column\": \"subject\",\n \"properties\": {\n \"dtype\": \"category\",\n \"num_unique_values\": 57,\n \"samples\": [\n \"college_computer_science\",\n \"philosophy\"\n ],\n \"semantic_type\": \"\",\n \"description\": \"\"\n }\n }\n ]\n}" - } - }, - "metadata": {}, - "execution_count": 18 - } - ] - }, - { - "cell_type": "code", - "source": [ - "from datasets import Dataset\n", - "df_train['Output'] = df_train['Output'].astype(str)\n", - "dataset = Dataset.from_pandas(df_train)" - ], - "metadata": { - "id": "zYNCBXnxEvGg" - }, - "execution_count": 19, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "alpaca_prompt = \"\"\"### INPUT: {} RESPONSE: {}\"\"\"" - ], - "metadata": { - "id": "orRdPSsSE7u2" - }, - "execution_count": 20, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "EOS_TOKEN = tokenizer.eos_token\n", - "def formatting_prompts_func(examples):\n", - " inputs = examples[\"Input\"]\n", - " outputs = examples[\"Output\"]\n", - " texts = []\n", - " for input_text, output_text in zip(inputs, outputs):\n", - " text = alpaca_prompt.format(input_text, output_text) + EOS_TOKEN\n", - " texts.append(text)\n", - " return {\"text\": texts}\n", - "dataset = dataset.map(formatting_prompts_func, batched=True)" - ], - "metadata": { - "id": "3fax1umiFFG6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 49, - "referenced_widgets": [ - "8a48fd7a8c80454b851a01e2cb621b4d", - "6ded06e2b40842bf9191e5f3dd3c6b49", - "fe71b747686448648b9daba805984c8e", - "1d3473666e06409d800dd15b277fa977", - "435a3b4cdf394b4f992610cbfc4e03d4", - "a6180ec2c99049a09bcd291139294505", - "eb2002e8e34945f0bd3bd2f8f6e2135e", - "709fd57bfa9a4ae8af71643b823ca550", - "c33bb5042c77481fa57e0d995d7b6f37", - "82f3cd86d7044b56a20d0769df343352", - "0422039ab8de4a0f824a65b4bec4c605" - ] - }, - "outputId": "0991b2a0-0298-44a8-cec6-0eb40eeae001" - }, - "execution_count": 21, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "Map: 0%| | 0/43390 [00:00" - ], - "text/html": [ - "\n", - "
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" \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
StepTraining Loss
11.240000
21.350800
31.116300
41.317000
50.807700
61.824800
71.075800
81.310600
91.503600
101.329600
112.008900
120.964000
131.326700
141.808500
151.764800
161.011500
171.133200
180.785800
191.147200
201.295900
211.146300
221.270700
231.081600
241.135700
251.051700
261.730500
271.075500
281.029400
291.028200
301.389800
311.273400
321.357600
331.047000
341.070100
351.082300
361.251400
370.989400
380.821800
391.164900
401.370100
410.884400
421.128400
430.901000
441.199300
450.671100
460.882800
470.998800
480.798100
490.924600
500.777900
511.150900
521.243700
530.795500
540.932300
550.816100
560.808800
570.830400
581.094000
590.906100
600.847400
610.614200
620.842600
630.787200
641.190500
650.866800
660.934600
670.782700
681.123500
690.861000
700.945300
710.786500
721.072200
730.944000
740.962000
750.769000
760.767500
770.987300
780.878900
791.014900
801.054100
810.643000
820.714700
830.965600
840.747300
850.864300
860.680800
870.793200
880.714200
890.593100
900.832900
910.876900
920.836600
930.877300
940.895000
951.103700
961.107700
970.891800
980.861100
990.539400
1000.797800
1010.751400
1020.876100
1031.405600
1040.730200
1050.886400
1060.665900
1070.630700
1080.692800
1090.748100
1101.145600
1110.779600
1120.740800
1130.860300
1140.950600
1150.695200
1161.113000
1170.642600
1180.996300
1191.122700
1200.823400
1210.910000
1220.786100
1230.751700
1240.806900
1250.955600
1260.660400
1270.708100
1280.794800
1291.058000
1300.782200
1311.092600
1320.635800
1330.909900
1340.850200
1350.629000
1360.987500
1370.693300
1380.496500
1390.759500
1400.716600
1411.118700
1420.691000
1430.749400
1441.050400
1450.592200
1460.781900
1471.018400
1480.924800
1490.779000
1500.742400
1510.453600
1521.075800
1530.587400
1540.819700
1550.675500
1560.678700
1570.892900
1580.934900
1590.715400
1600.808800
1610.989300
1620.823800
1631.105200
1640.654600
1650.803700
1661.116900
1670.665100
1680.781200
1690.951200
1700.777300
1710.953900
1720.671700
1730.698000
1741.037800
1750.595100
1760.583200
1770.730100
1780.843800
1790.774900
1800.580500
1810.921400
1820.737400
1830.756100
1840.954000
1850.662700
1860.688600
1870.606400
1880.815700
1891.081600
1900.715400
1911.071000
1920.793300
1930.696400
1940.763400
1950.652800
1960.587500
1971.249500
1980.707300
1990.806200
2000.513600
2010.760700
2021.184000
2030.804100
2040.709200
2051.081000
2060.645100
2070.925200
2080.776100
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" - ] - }, - "metadata": {} - } - ] - }, - { - "cell_type": "markdown", - "source": [ - "#**MODEL SAVING**" - ], - "metadata": { - "id": "xj6XdJWNF6Pb" - } - }, - { - "cell_type": "code", - "source": [ - "!wait" - ], - "metadata": { - "id": "ULl7losCFj9L" - }, - "execution_count": 26, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "if True: model.push_to_hub_merged(\"Siddartha10/Qwen-14B-B10\", tokenizer, save_method = \"merged_16bit\")\n", - "# USE YOU HF ACCOUNT AND NOT THE ORG\n", - "# NAME FORMAT : HINDI-MODELNAME-EXTENSION-A/B-00 - USE A/B is BENCHMARK DATASETS WERE SET TO TRUE/FALSE IN FLAGS , THE NUMBER IS THE RATIO USED ABOVE" - ], - "metadata": { - "id": "cEGk9Cm1FmoH", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 573, - "referenced_widgets": [ - "2bfc0f9bb6ed4d15bacc632993a6224e", - "f863eb5a3c1e42fbba285d1d5a260e26", - "50ee5ce7879e4cea8335f6ec53dde5a7", - "f395997ef7194fbf954ee8cb4cc9e763", - "07e03fffa3824a1489a1f48443327efd", - "5fbc2296fc3744a288206e5d6059e008", - "d718a0e4a3a44601ba63ecde12ac142d", - "9354462914fb431b8bad8fbad99eb0df", - "879fd664403248d290e66ad4a0ebabe6", - "0da240b656494659aa666f75c9382023", - "0adfd0a2b0db4794ae366385017d4810", - "dbeb1f45201543f88a23ea6f46bbf909", - "990db7c2b5af4a9e95fb9936c736da0a", - "5db637bd69174a56bd9b47a1d706953b", - "77a06c97515c47b29a6807c7d56a7485", - "a0cb9948d95e48b48be486f8589f3f88", - "e761b206854442eba6a8c7b8043195ee", - "57bd3df69de343f7b598d2678126db7d", - "72b77603874843df8caf40a623663b75", - "f002fc4fd0dd478f812a63382da91c21", - "db408f750ca9478481e9491e3feded09", - "647ae3e3ec2c44028921ba834bd5bb15", - "e6cd05c140cf4a2a9df7edced314087d", - "28aca682739146ed802a8ad3ce817231", - "571c46e385fb483586e29d2ae7c17d47", - "ce5fb9932de64ae99f0ae86ab05edd17", - "dcf5fb94fd94484a91d9a158eddd648d", - "fd85e2a90b5046ceb17cbc816453b76b", - "2b97a3df27b440b7bf82854a657cea20", - "adb4e1c1103245f5b723bd8e52b18c7e", - "29c6abd6cfe849b8ae4c38cf4bc225d1", - "5fe29cea49fb4fb38eec9fd55b195b3c", - "4c183d1d22c34e2e9ac0c63e8bf73a7d", - "7ca3caa1c7f648d5b9b494d07990c043", - "3305d85c7bfe4742afd790c00e2daeff", - "d6e418850f2047479c1150322f9f3db6", - "f636e97a64754f2d8bae1ee8dad6f5c5", - "22ea7f2166cc413a9fa5f97e0aa9616b", - "18d97ad1a1e34723b16e94e49daa89d4", - "bf2e8ecfd4a74eab835907c44f8908bf", - "02a7f56b66d244a28497ad062b69826b", - "24fc8a48f5d34e63b14015d709a2ed32", - "9d3013d1a4ef49f1bc360c49815f7009", - "9719fec6b25242f9b7ef007adb4fe7db", - "8edc11107f174fb1bd5cf1569b714d09", - "bfb8d783d18344cbaf0f54076259f8fa", - "fb5d03e1370d4f6ca6cbfaf72f67a518", - "b962bcf45d274c80a27a16fad5f3c9d2", - "d0b75cc731b145a8b5e9eb04bcfda9cf", - "cc905a70695e404d9bd8825c640f655b", - "dbe472752c6d43eb916b5528f428571d", - "b8bea385afc94c58ae2eb0abcd4e9250", - "48ed93c5223c46c58ce9ffefd4cccbbb", - "69d5c06524054a59a9090010370b0d98", - "a73a516a3939425ca3d6c723422f5d72", - "70b095db377c491798c2d9a4cb7fb8a4", - "cb285ca38e2f4a608d5938cf3d1dc110", - "3f339992fa134af083377526af6b0cb3", - "df492e362bd348919d87502c7d6987a5", - "85f5ce0c63284cca85de0b3079b83444", - "0581a7205ab84ae9bc9541017697e76b", - "ad179c9eaac443c685f4de9b379322e8", - "ab7b2cef998c4c29833ee3ca31ec42e9", - "2ac75a34b8a64caaa11ec04f82e4366f", - "4a3e2631d24b4ee38a19931075ad02e1", - "f6ee963f3cbb43fa8efc5e68deac6bcd", - "8588720c377b4ef18b18cf793fa77041", - "54a4d79173814533b213059f5199fec4", - "605cee968ce34226b9758304893a197a", - "189924c4ed684e2cb025dcd810b6d883", - "b66ca0d341c949e582b17393b556e27f", - "0cfb4c77991740ac81f05c584fd4af70", - "20582127470f4afeb5b4cea2fbee0d56", - "fcf005dde4d04e6ca90be8a03b1912de", - "8de57df30765489884c0dff4f740bafc", - "8b8cf07a055a4a7c99de9f57479af829", - "1d3c37dd9afe4cb381724ce19083c3f6", - "0277fea801e54be29c68d6e7ac512c3d", - "3d505de5e13441289456450563141590", - "b084ad037ef54c88823afc712a9f3e58", - "26acd2801f1e4ea4a4bbe5e81f4cf461", - "41fd180102b54ae8986132672f040e1f", - "d62d60d5507c405daac82a48e337022d", - "e0e0d00aa8604b099f84f18db5421dd3", - "4ba3f3c7fea4422395f099fe43257bf2", - "c5904e0fe4fd4ce7ba4ffc8a1a157c7c", - "e357db66ed004e1199befc1f4a23f2f1", - "c31f0e7b43cf4dec9360c8a08efe8259", - "eb8e328e6bee4d0b871296ff60034454", - "903a7dfdfaaa41fb8ecc6dcf38f7737c", - "190a4ca3752e47e7b4761f141c6e70c6", - "08651150aa6846ddbfd3e1c5c0e209d3", - "fd406650b2d74920bb17d540c65470fd", - "66c78c91d1b247baa69f064e0be135a3", - "af544e332aca4ab7998245fdf97f96fd", - "345efc4fe64b449f947158063ed09bb3", - "9b5379ecb11c41d18b8913244e1d1db0", - "ee5220beacff4c1597f2acb3a0b5dbd7", - "ca4765cf769f48559bf9a986f50fe032" - ] - }, - "outputId": "b55fff73-bd4e-4f0c-940e-934cf40a3f90" - }, - "execution_count": 27, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "Unsloth: You are pushing to hub, but you passed your HF username = Siddartha10.\n", - "We shall truncate Siddartha10/Qwen-14B-B10 to Qwen-14B-B10\n", - "Unsloth: Kaggle/Colab has limited disk space. We need to delete the downloaded\n", - "model which will save 4-16GB of disk space, allowing you to save on Kaggle/Colab.\n", - "Unsloth: Will remove a cached repo with size 29.5G\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Unsloth: Merging 4bit and LoRA weights to 16bit...\n", - "Unsloth: Will use up to 42.86 out of 83.48 RAM for saving.\n", - "Unsloth: Saving model... This might take 5 minutes ...\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - " 2%|▏ | 1/48 [00:00<00:05, 8.59it/s]\n", - "We will save to Disk and not RAM now.\n", - "100%|██████████| 48/48 [01:06<00:00, 1.39s/it]\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Unsloth: Saving tokenizer..." - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "tokenizer.json: 0%| | 0.00/11.4M [00:00> THE CSV SAVED IN YOUR RUNTIME" - ], - "metadata": { - "id": "U5gGbbSNF1oB" - }, - "execution_count": 28, - "outputs": [] - }, - { - "cell_type": "code", - "source": [ - "from google.colab import runtime\n", - "runtime.unassign()" - ], - "metadata": { - "id": "PPD-EQ8fYM07" - }, - "execution_count": null, - "outputs": [] - }, - { - "cell_type": "code", - "source": [], - "metadata": { - "id": "kcbK6-hMYNpC" - }, - "execution_count": null, - "outputs": [] - } - ] -} \ No newline at end of file