{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Fine-tune DeepSeek with a Synthetic Reasoning Dataset\n", "\n", "This notebook demonstrates the fine-tuning process of `unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit` using a synthetic reasoning dataset.\n", "\n", "It provides a complete walkthrough of the fine-tuning process after generating synthetic data using the Synthetic Data Generator. For a comprehensive explanation of the methodology and additional details, refer to the blog post: [Fine-tune DeepSeek with a Synthetic Reasoning Dataset](https://huggingface.co/blog/sdiazlor/fine-tune-deepseek-with-a-synthetic-reasoning-data)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Getting Started" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Install the Dependencies" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "!pip install datasets\n", "!pip install unsloth" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Import the Required Libraries" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import torch\n", "from datasets import load_dataset\n", "from transformers import TrainingArguments\n", "from trl import SFTTrainer\n", "from unsloth import is_bfloat16_supported, FastLanguageModel" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Configure the Environment" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "MODEL = \"unsloth/DeepSeek-R1-Distill-Qwen-1.5B-unsloth-bnb-4bit\"\n", "REPO_NAME = \"sdiazlor\" # your HF username here\n", "MODEL_NAME = \"deepseek-r1-distill-qwen-1.5-unsloth-sft-python\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Load the Model and Tokenizer" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 316, "referenced_widgets": [ "5b30f4877b554974bb8cdfc9814bb967", "1b216f51a8714e9dab82dc844fed17bf", "a87fd32f965c41e68249ba9bf4234691", "0b7b5a1ecb964a9cbd035b559438fb52", "453b1c07c5e345fbac74856842c28784", "5718ee0ebe1f4cadaf79c14d2a8e5339", "a03e6373939e489c87c8ed6546dc03d6", "b9974277d199419699e9b2a584f6198f", "210d0c13cbec434097f0a9b6b68d456d", "31abf79b170443fabbbb924ca4220e4c", "0142b4aeab9b4d87819b86c26a908f7a", "a4362f76cffa45a89675d66f8f51d440", "178df7b3c6b74583b70b6ebfd2272336", "921c02b29a564833ae10ac54f220c1d4", "4b8d88e2c0634338affb3b4d81e77974", "67dfa13c01f64b589e04c053f89ec671", "2bdd42e1bb57415584d2a38edfe8ea6b", "398508c460594d22b7fdbf3be15d761b", "a383ed5654194fefa9312b747d5d6ed6", "03ea6c67112843c9933b3e1a768b77f6", "b7c04ccedc1c48adb34f615f24b8c424", "db31df39f56546b6a64a55f97b91a8f2", "f187dd6f0dbf4f49bca90bd394f27e23", "1a0a4ae3d6c147c3bbb72e703858aedc", "d3de80a9bafa4b4b9c13c4361d6213db", "84a304df3ea4459ebcd3706f57404eba", "6904076b530c44ae8a34b2fd424b89c0", "5e51b3b8054d477fa3af1cf1682d4304", "213c0e1e238849f7a071b2f9bcf9c433", "dc84dfd9af984f02a09770dffc8f3001", "38e3a03149754b048a0459f4e5d00e2a", "f74d4bc9f00b4cac8de83792f405ad5a", "4ddc8e1a851c4e9da6ede032d40908f0", "24dfeff577da4393bdaa5de47bf5d096", "0b722c2760634ce3856dbf2f3ac329fe", "8d652113d0964829a65fbbcab211e141", "d0eb3820302646e9a848da2f936aa9eb", "d089d26bfd1a4a8488133f0f82b120c3", "5094f58022ca4565873196fde589bec8", "d180a951e872463c8c39f28f61b3b734", "02302f414c384422920e3e310f597063", "a8001e449dca4533bd56918d8da1a69d", "19b6b7f26e394d9fb7c2992d71b1fc14", "007ba2f3f37c48f8a5e7e21a4a2e5a71", "65a71e24010b4ba4884dc0bb2fe37e1f", "99831f0a5b204b24923d5ca3d30c42a9", "52b547101e8848cb9e5881c3e63eca3c", "b6e67b2b00be4835962dc5939e25a289", "747b3c80a240453c986bc8db01072394", "29d52353241742bf882ade4bc4eeb402", "c2cd6a947bda4a09ad56b0c2069a9166", "02db395a497443adad709a5a5b2ee463", "28b224e830e24bbdb9abf547b3789769", "a76024ba39194881b63302a11383e84e", "40f0399f117845b08eff5c5ed84f137d" ] }, "id": "QmUBVEnvCDJv", "outputId": "977ab27e-7773-4b20-e16b-7df3f1d8994e" }, "outputs": [], "source": [ "# Load the 4bit pre quantized model of deepseek and the tokenizer\n", "\n", "model, tokenizer = FastLanguageModel.from_pretrained(\n", " model_name = MODEL,\n", " max_seq_length = 2048,\n", " dtype = None,\n", " load_in_4bit = True,\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "6bZsfBuZDeCL", "outputId": "4bdd6bfa-47c5-47cd-cd45-475107258a89" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Unsloth 2025.2.5 patched 28 layers with 28 QKV layers, 28 O layers and 28 MLP layers.\n" ] } ], "source": [ "# We add the LORA adapters to the model\n", "model = FastLanguageModel.get_peft_model(\n", " model,\n", " r=16,\n", " target_modules=[\n", " \"q_proj\",\n", " \"k_proj\",\n", " \"v_proj\",\n", " \"o_proj\",\n", " \"gate_proj\",\n", " \"up_proj\",\n", " \"down_proj\",\n", " ],\n", " lora_alpha=16,\n", " lora_dropout=0,\n", " bias=\"none\",\n", " use_gradient_checkpointing=\"unsloth\",\n", " random_state=3407,\n", " use_rslora=False,\n", " loftq_config=None,\n", ")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Pre-process the Synthetic Data" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 154, "referenced_widgets": [ "e6ffb622331241239867b1477d3da76b", "5d835002db074abea3d5b87e0ce72618", "8b79b01a42614f4e9ae6550703561236", "a9f65983f1f24d7c9cceaf65c2191c7d", "560be4516ac74cd5a29a0b8304976810", "679a9d5ae6fb4916a2261f556969f3a0", "09095674ac234d19be3f0454146201c2", "225b460bd91b4995b81cb967a7f8c42c", "04b79a83f42745449910b1733a8496c7", "36f85ae64a1e440b8b15d0c5625bd23f", "fd3643434d0f477f90a59dc8e0bf64f2" ] }, "id": "LjY75GoYUCB8", "outputId": "35602bba-6cdf-4cb5-ac87-967679f10350" }, "outputs": [ { "data": { "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" }, "text/plain": [ "'Below is an instruction that describes a task, paired with a question that provides further context. \\nWrite a response that appropriately answer the question. \\nBefore answering, think carefully but concisely about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.\\n\\n### Instruction:\\nYou are an expert programmer with advanced knowledge in Python. Your task is to provide concise and easy-to-understand solutions. Please answer the following python question. \\n\\n### Question:\\nHow can I solve a Rubik\\'s Cube? \\n\\n\\n\\n### Response:\\nOkay, so I want to solve a Rubik\\'s Cube, but I\\'m not really sure where to start. I remember seeing people solve them quickly, but it looks so complicated. Let me try to break it down.\\n\\nFirst, I think I need to understand the structure of the cube. It has six faces, each with nine smaller squares of different colors. The goal is to have each face all one color. But how do the pieces move?\\n\\nI\\'ve heard about algorithms before. Maybe that\\'s the key. I guess algorithms are specific sequences of moves that solve certain parts of the cube. But I\\'m not sure which ones to use or in what order.\\n\\nI remember someone mentioning something called the cross. That\\'s probably the first step. So, I should start by making a cross on one face. Let\\'s pick the white face for the cross. I need to get all the white edges aligned with their corresponding center colors.\\n\\nOnce the cross is done, the next step might be to solve the corners of that face. Each corner has three colors, so I need to make sure they match the adjacent center colors. Maybe there\\'s a specific algorithm for inserting the corners correctly without messing up the cross.\\n\\nAfter the first layer is done, I think the next part is the middle layer. This involves moving the edge pieces between the first and last layers. I\\'ve heard terms like F, R, U, etc., which stand for front, right, up, etc. Maybe the algorithm for the middle layer involves moving a piece from the top to the correct position.\\n\\nThen comes the last layer, which is the trickiest part. I think this involves orienting the edges so they all face the right way, and then permuting the corners. I remember something about the \"OLL\" and \"PLL\" steps. OLL is orienting the last layer, and PLL is permuting it. There are a lot of algorithms for each, so it\\'s important to learn the common ones first.\\n\\nI\\'m a bit confused about how to recognize when to use each algorithm. Maybe I should start with the most common ones, like the cross, then F2L, OLL, and PLL. Each step builds on the previous one, so I shouldn\\'t skip ahead.\\n\\nI also wonder about the notation. F means moving the front face clockwise, F\\' is counterclockwise, and F2 is turning it twice. Understanding this notation is crucial for following the algorithms.\\n\\nI should probably practice each step separately. Start with solving the cross, then move on to the corners, and so on. It might take a while to get each step right, but with practice, it should become easier.\\n\\nWait, what if I get stuck? Maybe I should look up some tutorials or guides that break it down step by step. There are probably detailed explanations and videos that can help me visualize the moves better.\\n\\nIn summary, solving a Rubik\\'s Cube seems to involve a series of structured steps, each with specific algorithms. I need to learn each step, practice the moves, and gradually build up to solving the entire cube. It might be challenging, but breaking it down into smaller parts makes it manageable.\\n\\n\\nTo solve a Rubik\\'s Cube, follow this structured approach:\\n\\n1. **Understand the Structure**: Familiarize yourself with the cube\\'s layout, noting that each face has nine smaller squares and six faces in total.\\n\\n2. **Learn Notation**: Understand the basic move notations (F, R, U, etc.) and their directions (clockwise, counterclockwise, and 180-degree turns).\\n\\n3. **Step 1: Solve the Cross**:\\n - Begin with the white face.\\n - Align the white edges with their corresponding center colors.\\n\\n4. **Step 2: Solve the Corners**:\\n - Position the corners so they match adjacent center colors, using specific algorithms to insert them correctly without disrupting the cross.\\n\\n5. **Step 3: Solve the Middle Layer**:\\n - Move edge pieces between the first and last layers, using algorithms to place them correctly.\\n\\n6. **Step 4: Orient the Last Layer (OLL)**:\\n - Use algorithms to orient the last layer\\'s edges so they all face the correct way.\\n\\n7. **Step 5: Permute the Last Layer (PLL)**:\\n - Apply algorithms to permute the corners, ensuring they are in the correct positions.\\n\\n8. **Practice and Resources**:\\n - Practice each step separately, gradually building up skills.\\n - Use tutorials or guides for detailed explanations and visual aids.\\n\\nBy following these steps and practicing each algorithm, you can systematically solve the Rubik\\'s Cube.\\n<｜end▁of▁sentence｜>'" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Prepare the dataset\n", "\n", "prompt_style = \"\"\"Below is an instruction that describes a task, paired with a question that provides further context.\n", "Write a response that appropriately answer the question.\n", "Before answering, think carefully but concisely about the question and create a step-by-step chain of thoughts to ensure a logical and accurate response.\n", "\n", "### Instruction:\n", "You are an expert programmer with advanced knowledge in Python. Your task is to provide concise and easy-to-understand solutions. Please answer the following python question.\n", "\n", "### Question:\n", "{}\n", "\n", "### Response:\n", "{}\n", "\"\"\"\n", "\n", "EOS_TOKEN = tokenizer.eos_token\n", "\n", "\n", "def formatting_prompts_func(examples):\n", " prompts = examples[\"prompt\"]\n", " completions = examples[\"completion\"]\n", " texts = []\n", " for prompt,completion in zip(prompts, completions):\n", " text = prompt_style.format(prompt, completion) + EOS_TOKEN\n", " texts.append(text)\n", " return {\n", " \"text\": texts,\n", " }\n", "\n", "\n", "dataset = load_dataset(\"sdiazlor/python-reasoning-dataset\", split=\"train\")\n", "dataset = dataset.map(formatting_prompts_func, batched = True,)\n", "dataset[\"text\"][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Train the Model" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 49, "referenced_widgets": [ "e865b621b4704786998d9e5511bcb522", "08220a32e37f4b1dab636ea06a786a1f", "2a76087509dd482a81c297ba44e2ae2e", "1b7f8aad80c0464b819ee74b7f76d40e", "6849f7a48eff4ab8a7e01a3e26da7645", "6b9552cc29ab4ac7936618bf1f0b1c5c", "706d0954642a4ef5b8e3d4ece2fa556e", "9eea1e4a483b4298a6ad67dfca0ff122", "2f1ede630a654aeebe7d80640ee9674a", "e411f85b5a0047c4b510565585215493", "fb6566ed643a473e892e68b55fa3ef29" ] }, "id": "95_Nn-89DhsL", "outputId": "9d245291-dc8e-48be-d05c-beeb65b3769f" }, "outputs": [], "source": [ "# Configure the trainer\n", "trainer = SFTTrainer(\n", " model=model,\n", " tokenizer=tokenizer,\n", " train_dataset=dataset,\n", " dataset_text_field=\"text\",\n", " max_seq_length=2048,\n", " dataset_num_proc=2,\n", " packing=False,\n", " args=TrainingArguments(\n", " per_device_train_batch_size=2,\n", " gradient_accumulation_steps=4,\n", " warmup_steps=5,\n", " num_train_epochs=3,\n", " learning_rate=2e-4,\n", " fp16=not is_bfloat16_supported(),\n", " bf16=is_bfloat16_supported(),\n", " logging_steps=1,\n", " optim=\"adamw_8bit\",\n", " weight_decay=0.01,\n", " lr_scheduler_type=\"linear\",\n", " seed=3407,\n", " output_dir=\"outputs\",\n", " report_to=\"none\",\n", " ),\n", ")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 1000 }, "id": "yqxqAZ7KJ4oL", "outputId": "bb329be2-eced-4843-cb86-00ed8395e992" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "==((====))== Unsloth - 2x faster free finetuning | Num GPUs = 1\n", " \\\\ /| Num examples = 500 | Num Epochs = 3\n", "O^O/ \\_/ \\ Batch size per device = 2 | Gradient Accumulation steps = 4\n", "\\ / Total batch size = 8 | Total steps = 186\n", " \"-____-\" Number of trainable parameters = 18,464,768\n" ] }, { "data": { "text/html": [ "\n", "
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StepTraining Loss
10.828400
20.784400
30.838800
40.849500
50.732300
60.670100
70.709900
80.688500
90.613600
100.626500
110.729400
120.682100
130.540100
140.591000
150.604800
160.611600
170.604000
180.617800
190.610100
200.651400
210.580700
220.620100
230.664400
240.675600
250.513200
260.498600
270.646800
280.501700
290.537800
300.592300
310.488400
320.533100
330.567700
340.565900
350.638700
360.564400
370.479600
380.644000
390.486400
400.598800
410.595200
420.508000
430.504900
440.613700
450.517800
460.571700
470.568900
480.507400
490.536600
500.681900
510.469500
520.530200
530.601400
540.531000
550.470400
560.535800
570.615800
580.557500
590.620600
600.497700
610.556100
620.561300
630.607200
640.556200
650.538400
660.529800
670.580100
680.573100
690.466100
700.498400
710.590800
720.632500
730.472400
740.523400
750.562500
760.552200
770.548400
780.523300
790.593300
800.483600
810.585400
820.554700
830.413900
840.589400
850.484100
860.461000
870.570700
880.545900
890.542300
900.502100
910.554100
920.554000
930.468700
940.535800
950.539100
960.479600
970.499100
980.518300
990.593800
1000.573200
1010.546400
1020.599600
1030.465200
1040.472400
1050.556100
1060.498800
1070.486900
1080.529000
1090.480100
1100.525900
1110.489700
1120.510600
1130.628300
1140.413200
1150.577800
1160.515000
1170.539300
1180.459200
1190.533700
1200.501700
1210.528400
1220.475900
1230.437600
1240.551700
1250.464600
1260.442300
1270.611100
1280.425300
1290.516900
1300.469100
1310.486200
1320.492100
1330.511100
1340.559500
1350.537600
1360.426800
1370.474200
1380.543500
1390.539800
1400.481500
1410.481400
1420.562000
1430.409100
1440.440900
1450.437700
1460.427300
1470.393100
1480.480300
1490.509300
1500.450200
1510.530500
1520.475300
1530.521300
1540.519500
1550.539400
1560.433300
1570.495400
1580.415200
1590.608800
1600.524700
1610.438700
1620.504800
1630.455700
1640.455100
1650.592300
1660.565700
1670.480800
1680.546100
1690.463100
1700.573400
1710.500700
1720.516700
1730.572000
1740.411700
1750.452700
1760.424900
1770.489200
1780.574300
1790.479700
1800.487800
1810.513700
1820.492800
1830.535100
1840.501100
1850.450400
1860.484500

" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Train the model\n", "trainer_stats = trainer.train()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 501, "referenced_widgets": [ "75dc3defc03044d5a3d5327f982f4ad9", "69a1c928a91b485e899218b4c48bbd0f", "2cd1ab0231be43078bf2b0861c66756b", "7d6ef80065f2457ca6a584dea0ee193a", "8deac706db694fd9af003a52d9e65c18", "d1fdaab3815b4beebd51e5240208c14c", "0c677a8b3f234e0480995a3f3f511cfc", "bf95bf76909b418db87bed807a8fcd2e", "60362ddf26274bbda10f888cae24b527", "cf64d3a6baf545bebc84c62b2863a2a3", "a023e723ac13430087c8f019e7bd2946", "37b277adce0f47f58ce4f153ebbf9885", "ae4269c9c5434ee79eaa31d197e382c0", "d7ef28d78e19450e82bd3e42a9fab928", "0b0ce59c58f947c5ab6b456c4b2b8fd8", "c05b434f65a04a07880f82d5dda991e3", "aa8a0d882d1047e8a6a08b04df1214ea", "344f6b55db5e43c7b98750e4cbf907d1", "58aa7a36dbd14511a43268242bf3486c", "9b0488d548f3446d8d20af6d3ac56739", "2c966a7792594a9bace7cada70bc6e20", "eb694d1a1a674598bccff290c5c96077", "343312cb90c84c6bbfec606bed32765a", "4cbc59fddff34f44a0e99e15ff1ae102", "932c5742442b421389363d2e216626d7", "f57fce9a80e549609474d84b7d935b33", "53f6ce3049474fbda959fd52fe9b5890", "71cc82a978664061b4778a73ac25afdb", "d4f41624cbea494f803bd76cd78f315f", "8f02d26e18614cd38e891d5ee019bc3d", "fefdd42e29544fdba8ba1dd7f28cc85d", "ca279528c62c41559fff7d150a229567", "3abc1f45407c4c8c9c6b6d676246aa3b", "7b9eed20c6554407afb846170e2d946d", "27cacb0adaa944c19d80cd9cb0ddffa9", "b5037ef5dcdc43a9850bb073addc947f", "cc7ce94c244548c8954b35bb92936bdd", "3bcfc01fccce49ef87f62b0046d54059", "4be3ffa40ad14461adeb5bfafad806f6", "b3f9eb630a6a4b88857e4d9e1869ffca", "91a556fcd64843fe877a580eb3e21826", "6d93c3a9f44342bdbf840b6ce3d04ab6", "ea83b9bb0b134206a99628d3fe66503a", "a018604c1f624fb49e1064696a5533fd", "8b9066519c9546a5b8ad95b239828b89", "9ec9215968024471a69fa7a9a62db7bb", "901c6eca0c4a40e7a9ed9fa968874787", "687438cfca3140d4bc132b6b91ab616c", "c16a355a4e2443509f3362504882ab7c", "0e372476bcc04cd5a89a7b0626742c7e", "56aac0b6cad64e7a9d672583c633c8bf", "f2da104837ae4a3289e24a1bd7de2415", "95a67d05e14c40c2a5bdcb322bc61af8", "f8d6262c216f4226b6d26ad9961b9622", "00b6d832a0c546c2afb059bd29e54991", "76af8b2071e8453794f432a7c0bf63d0", "51730e19fe0c4ead871b9b6cf7895e05", "2c1cdcb9a80446539b3e229c5cc8ce33", "7b1889d55328460fbd9c348a2a72e3c8", "8bb5bfadd1984e5a8c0b27dfbb16bf9a", "624a5a04f510474e90ac9aaf7ed264b1", "405277a1c6944a35b1cb082de3b9dbdb", "32b541d4aa6d4dac8a9dc0e5cdf9ba44", "2cf66ab1c0f34d71b90ca6ac10eec6d6", "c9ab627f27d7456d9de249610ffef4a1", "4b5fa3674a8f467aba1089a39e2978b5", "1cc3ff808e1543eb9716deba509c37ee", "426ba1f107354dc78ded5f1ba400895b", "02ddf97d03404b92a09a0b6a869847d8", "9b22f445c87a44f9afe39de4a20f08f4", "9f197e2d7fb54261acbb2d1ddb8b6a30", "6d9294f45ab14b139f968dae24663783", "bd5608c4250a4d35aca16aebd1dba7f2", "92e84bbf66f34be3aa0da2ff046daf94", "75a890e9c93543eeb2917c165f0d5d20", "3d4c73330cc44748a4525c7901a281a1", "4df8ff8e37004b53adcb9a5b32c40fb2" ] }, "id": "WBeMl1mh1ArJ", "outputId": "4e869fbe-da2b-417b-b1d3-78c913efc4cb" }, "outputs": [], "source": [ "# Save to the local directory and push it to the Hub\n", "model.save_pretrained(MODEL_NAME)\n", "tokenizer.save_pretrained(MODEL_NAME)\n", "model.save_pretrained_merged(MODEL_NAME, tokenizer, save_method=\"merged_16bit\")\n", "\n", "fine_tuned_model = f\"{REPO_NAME}/{MODEL_NAME}\"\n", "model.push_to_hub(fine_tuned_model, safe_serialization=None)\n", "tokenizer.push_to_hub(fine_tuned_model, safe_serialization=None)\n", "model.push_to_hub_merged(fine_tuned_model, tokenizer, save_method=\"merged_16bit\") # for vLLM\n", "model.push_to_hub_gguf(\n", " f\"{fine_tuned_model}_q4_k_m\", tokenizer, quantization_method=\"q4_k_m\"\n", ") # as gguf" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Run Inference" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kR3gIAX-SM2q", "outputId": "a8416e33-b27f-4056-8886-ddfec6c891d9" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Okay, so I need to find all the prime numbers between 0 and 125. Hmm, primes are numbers greater than 1 that have no divisors other than 1 and themselves. So, first, I should probably start by listing all numbers from 0 to 125 and then eliminate the non-primes.\n", "\n", "Wait, but 0 and 1 aren't primes. So I can ignore them. The smallest prime is 2. So maybe I should start checking from 2 onwards.\n", "\n", "I remember that one method to check for primes is the Sieve of Eratosthenes. That's an efficient algorithm for finding all primes up to a certain limit. Let me think about how that works. The idea is to create a list of all numbers up to the limit and then iteratively mark the multiples of each prime starting from 2. The numbers that remain unmarked are primes.\n", "\n", "So, applying this to the range 0-125. I'll create a list of booleans from 0 to 125, initializing them all to True except index 0 and 1 which are False. Then, for each number starting from 2, if it's still marked as prime, I'll mark all its multiples as not prime.\n", "\n", "Let me outline the steps:\n", "\n", "1. Create a list of size 126 (since it's up to 125) and initialize all to True.\n", "2. Set the indices 0 and 1 to False.\n", "3. For each number i starting from 2 up to the square root of 125 (which is around 11.18), if the number is still True, mark all multiples of i as False.\n", "4. After processing, the indices that are still True are primes.\n", "\n", "Wait, but the square root is a bit more precise. The Sieve of Eratosthenes only needs to check up to the square root of the limit because any factor larger than that would have a corresponding factor smaller than the square root.\n", "\n", "So, for 125, the square root is about 11.18, so I'll loop i from 2 to 11.\n", "\n", "Let me try to simulate this:\n", "\n", "- Start with all True except 0 and 1.\n", "- i=2: mark multiples 4,6,8,... up to 124.\n", "- i=3: mark multiples 9,12,15,... up to 123.\n", "- i=4: already marked, so skip.\n", "- i=5: mark 25,30, etc., but since 5 is a prime, it should only mark multiples beyond 25.\n", "- And so on until i=11.\n", "\n", "After this, the indices that are True are the primes.\n", "\n", "Once I have the list of primes, I can collect them into a list and return that list.\n", "\n", "So, putting this into Python code, I'll create the sieve, then extract the primes.\n", "\n", "Let me think about how to implement this. I'll write a function called get_primes_up_to_n(n) which returns the list of primes up to n.\n", "\n", "Wait, but the user wants primes from 0 to 125. So the function should handle that.\n", "\n", "Alternatively, I can create a function that takes a number and returns the primes up to that number.\n", "\n", "So, the steps in code:\n", "\n", "1. Define a function get_primes(n) that returns primes up to n.\n", "2. Inside the function, create a list of booleans from 0 to n, initializing to True, then setting 0 and 1 to False.\n", "3. For each i from 2 to sqrt(n) + 1, check if it's still True. If so, mark all multiples of i as False.\n", "4. Collect all indices that are still True and return them as a list.\n", "\n", "Wait, but in Python, the square root can be calculated using math.sqrt, but since n is an integer, I should take the integer part. Or, better, loop up to int(math.sqrt(n)) + 1.\n", "\n", "Wait, no. The sieve only needs to check up to sqrt(n) because any composite number would have a factor less than or equal to sqrt(n). So for n=125, sqrt is ~11.18, so i should go up to 11.\n", "\n", "So, in code:\n", "\n", "import math\n", "\n", "def get_primes_up_to_n(n):\n", " if n < 2:\n", " return []\n", " sieve = [True] * (n + 1)\n", " sieve[0] = sieve[1] = False\n", " for i in range(2, int(math.sqrt(n)) + 1):\n", " if sieve[i]:\n", " for j in range(i * i, n + 1, i):\n", " sieve[j] = False\n", " primes = [i for i, is_prime in enumerate(sieve) if is_prime]\n", " return primes\n", "\n", "Then, call get_primes_up_to_n(125) to get the primes.\n", "\n", "Testing this function with n=125 should give me all primes from 2 up to 125.\n", "\n", "Let me see, for example, 2 is a prime, 3,5,7, etc., up to 125.\n", "\n", "I think this should work. So the code is straightforward.\n", "\n", "\n", "To find all prime numbers from 0 to 125, we can use the Sieve of Eratosthenes algorithm, which efficiently identifies primes by marking non-prime numbers. Here's a concise Python solution:\n", "\n", "```python\n", "import math\n", "\n", "def get_primes_up_to_n(n):\n", " if n < 2:\n", " return []\n", " sieve = [True] * (n + 1)\n", " sieve[0] = sieve[1] = False\n", " for i in range(2, int(math.sqrt(n)) + 1):\n", " if sieve[i]:\n", " for j in range(i * i, n + 1, i):\n", " sieve[j] = False\n", " primes = [i for i, is_prime in enumerate(sieve) if is_prime]\n", " return primes\n", "\n", "primes = get_primes_up_to_n(125)\n", "print(primes)\n", "```\n", "\n", "This code initializes a boolean list for numbers up to 125, marks non-primes, and returns the primes. The result is a list of primes from 2 to 125.\n", "<｜end▁of▁sentence｜>\n" ] } ], "source": [ "# Run inference\n", "question = \"How can I get the prime numbers from 0 to 125?\"\n", "\n", "FastLanguageModel.for_inference(model)\n", "inputs = tokenizer([prompt_style.format(question, \"\")], return_tensors=\"pt\").to(\"cuda\")\n", "\n", "outputs = model.generate(\n", " input_ids=inputs.input_ids,\n", " attention_mask=inputs.attention_mask,\n", " max_new_tokens=2048,\n", " use_cache=True,\n", ")\n", "response = tokenizer.batch_decode(outputs)\n", "print(response[0].split(\"### Response:\")[1])\n" ] } ], "metadata": { "accelerator": "GPU", "colab": { "gpuType": "T4", "provenance": [] }, "kernelspec": { "display_name": "Python 3", "name": "python3" }, "language_info": { "name": "python" }, "widgets": { "application/vnd.jupyter.widget-state+json": { "007ba2f3f37c48f8a5e7e21a4a2e5a71": { "model_module": "@jupyter-widgets/controls", "model_module_version": "1.5.0", "model_name": 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