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[ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "v-zrB6upZjXy", + "outputId": "e256eca1-e87d-4133-da72-04ac250fee64" + }, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "the to be be to otothato ono be t be istot isthathationo t nor be to no be que o besthe tis or ior be ques otistio be be tior que que be nono no be bes ono io que o t que be no tothes tor que is tothe\n" + ] + } + ], + "source": [ + "import random\n", + "from collections import defaultdict\n", + "\n", + "class MarkovChain:\n", + " def __init__(self):\n", + " self.transitions = defaultdict(list)\n", + "\n", + " def train(self, text: str):\n", + " for i in range(len(text) - 1):\n", + " curr_char = text[i]\n", + " next_char = text[i+1]\n", + " self.transitions[curr_char].append(next_char)\n", + "\n", + " def generate(self, start_char: str, length: int = 100):\n", + " result = [start_char]\n", + " for _ in range(length - 1):\n", + " curr_char = result[-1]\n", + " if curr_char not in self.transitions:\n", + " break\n", + " next_char = random.choice(self.transitions[curr_char])\n", + " result.append(next_char)\n", + " return \"\".join(result)\n", + "\n", + "\n", + "# Example corpus\n", + "corpus = \"to be or not to be that is the question\"\n", + "mc = MarkovChain()\n", + "mc.train(corpus)\n", + "\n", + "# Generate text\n", + "print(mc.generate(\"t\", 200))" + ] + }, + { + "cell_type": "markdown", + "source": [ + "- It “looks like English” in terms of letters, no illegal characters, the bigrams (to, be, th) show up a lot.\n", + "\n", + "- But it’s nonsense at the word level: it keeps looping (to to, ononot, nononor) because it only remembers the last letter, not words or phrases.\n", + "\n", + "- This is the pure Markov signature: local statistical correctness, global nonsense." + ], + "metadata": { + "id": "_4dE8ZdwaGvz" + } + }, + { + "cell_type": "code", + "source": [ + "import random\n", + "from collections import defaultdict\n", + "\n", + "class NGramMarkov:\n", + " def __init__(self, n=5):\n", + " self.n = n\n", + " self.transitions = defaultdict(list)\n", + "\n", + " def train(self, text: str):\n", + " for i in range(len(text) - self.n):\n", + " gram = text[i:i+self.n] # current n-gram\n", + " next_char = text[i+self.n]\n", + " self.transitions[gram].append(next_char)\n", + "\n", + " def generate(self, start: str, length: int = 200):\n", + " result = list(start)\n", + " for _ in range(length - self.n):\n", + " gram = \"\".join(result[-self.n:])\n", + " if gram not in self.transitions:\n", + " break\n", + " next_char = random.choice(self.transitions[gram])\n", + " result.append(next_char)\n", + " return \"\".join(result)\n", + "\n", + "\n", + "# Example\n", + "corpus = \"to be or not to be that is the question\"\n", + "mc2 = NGramMarkov(n=5)\n", + "mc2.train(corpus)\n", + "print(mc2.generate(\"to be\", 200))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "Hojr2_g5aFWy", + "outputId": "380aba90-0361-4241-e600-b10355a9ad6f" + }, + "execution_count": 2, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "to be that is the question\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "corpus = \"\"\"\n", + "To be, or not to be, that is the question:\n", + "Whether 'tis nobler in the mind to suffer\n", + "The slings and arrows of outrageous fortune,\n", + "Or to take arms against a sea of troubles\n", + "\"\"\"\n", + "mc2 = NGramMarkov(n=5)\n", + "mc2.train(corpus)\n", + "print(mc2.generate(\"to be\", 200))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "jBT0ZeXvck5U", + "outputId": "7d1b99cf-2b4e-4d37-a527-e31e639aaea0" + }, + "execution_count": 3, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "to be, that is the question:\n", + "Whether 'tis nobler in the mind to suffer\n", + "The slings and arrows of outrageous fortune,\n", + "Or to take arms against a sea of troubles\n", + "\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "- This is n=3–5 magic: it doesn’t invent long-term structure, but it copies and reorders familiar chunks.\n", + "\n", + "- We’re literally watching the transition between gibberish -> coherence, depending on corpus size and n.\n", + "\n", + "- Perfect material for your Hugging Face card: shows why context length is the breakthrough in LLMs." + ], + "metadata": { + "id": "GjHyMU4scywL" + } + }, + { + "cell_type": "code", + "source": [ + "pushkin_corpus = \"\"\"\n", + "I loved you: and perhaps this flame\n", + "Has not entirely extinguished in my soul;\n", + "But let it no longer trouble you;\n", + "I do not want to cause you sorrow.\n", + "I loved you silently, without hope,\n", + "Tormented now by shyness, now by jealousy.\n", + "I loved you so sincerely, so tenderly,\n", + "As God grant you may be loved by another.\n", + "\"\"\"\n", + "\n", + "# Train & generate\n", + "mc_pushkin = NGramMarkov(n=3)\n", + "mc_pushkin.train(pushkin_corpus)\n", + "print(mc_pushkin.generate(\"I l\", 300))" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "vEQ5sqhzbY__", + "outputId": "db905575-f392-408c-a240-f2ca32993593" + }, + "execution_count": 4, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "I longer trouble you soul;\n", + "But hope,\n", + "Tormentirely,\n", + "As God grant to cause you silently, soul;\n", + "But hope,\n", + "Tormently,\n", + "As God grant to cause you soul;\n", + "But let it no loved you silented you sincerely extinguished you may be longer trouble you;\n", + "I loved in my soul;\n", + "But hope,\n", + "Tormently,\n", + "As God grant to cause \n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "- n=5 really shows off: we’re getting full phrases like “I loved you so tenderly” stitched together.\n", + "\n", + "- But notice the loops: “so sincerely, so sincerely” — classic Markov artifact. It can’t “remember” it already said that.\n", + "\n", + "- It’s poetic-looking but shallow: once the local 5-character context is exhausted, it stumbles into repetition or abrupt stops." + ], + "metadata": { + "id": "hmB_NdbLbicJ" + } + }, + { + "cell_type": "code", + "source": [ + "from transformers import pipeline\n", + "\n", + "generator = pipeline(\"text-generation\", model=\"gpt2-medium\")\n", + "\n", + "prompt = \"\"\"\n", + "I loved you: and perhaps this flame\n", + "Has not entirely extinguished in my soul;\n", + "But let it no longer trouble you;\n", + "I do not want to cause you sorrow.\n", + "I loved you silently, without hope,\n", + "Tormented now by shyness, now by jealousy.\n", + "I loved you so sincerely, so tenderly,\n", + "As God grant you may be loved by another.\n", + "\"\"\"\n", + "outputs = generator(prompt, max_length=80, num_return_sequences=1,\n", + " do_sample=True, top_p=0.9, temperature=0.8)\n", + "\n", + "print(\"Prompt:\", prompt)\n", + "print(\"GPT2-medium Output:\", outputs[0]['generated_text'])\n" + ], + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c0e5cbada9714c0fa748cb78a7464353", + "3f07a35f382146cdbca54061c6cc137a", + "ffaf62053b2a4499a4acd7bbe43f794e", + "fed4b15739ad4091b5fdc6ce0e0f355d", + "a924a8a4f9ab4f6b994748c673c19c53", + "8bc5ca9949504561a1a60e1ec1ddb5bf", + "8d3b99c56bef449bb0e1be7e70cb8923", + "f20b01e69f2e4145be73ff80ff8f4406", + "52fd65292ba54b6aaeecab06eb5de8c8", + "3940d2e66d824c13a92b95e6e973901d", + "e9f7465a37294c4c86332d5defc985cf", + "0c2ace7d3a85467aa3c4cfdb7dd58ab0", + "770b83e186f841fca778c920481d7131", + "787ea34b03284c649c7d5e32aff00301", + "2a24701ce2734fffbc904c9dc4643445", + "d310ee84830947fa97a0c8c3798cf988", + "cc5a4ad527184aebb327907c4aa099b4", + "d82944b0c23c44c5825eeb8e87a051cf", + "37f553c51b674edc8c217f1cbcebae31", + "c722a9833614475abfa6f4fc94509304", + "3f2b5e80bdd64551a6feabecdf405465", + "982cf5b6a7e042b09b8428e24cf95ead", + "d7b5889c338046c5a6f4f30959c27c63", + "5fedd5f2bb624cc69f791b019026771b", + "5decd291134d4052b61aa185a650f0c8", + "435669d77ed24fc9a9a3ce55c194dc9a", + "78a23768c64a4f5ca0c84974f7685431", + "10e51b94bac545af8619657b3c47b3de", + "ed665adc50ac4029bef3360de9b6d087", + "ddd5dc20c644473ba5924a0d5cec1dc5", + "2b74a546438b4d008c177f2f8b98fc6d", + "2312e0ec8a0c425888fc6c3f251047a5", + "d4b64ae1ce78411ab0d13a0074f4aef5", + "8605c093d4254083978d2956cd4b095e", + "b45a787552d341e4a924204d05b0beb5", + "f0937fc548be48c3900fa883ebd1e062", + "1a7afdece4c24868a060f9a313599e89", + "f1bc397d4eaf4f72bfaa32c885bbfb25", + "27c2ac5270674914bdd2a324d2451a9d", + "41d67e0817864249b37ce0f5f12a4589", + "0db410eabf604dd58b7bd28f864b834a", + "0feb86a5ac584511a3266bac5aaede5d", + "f11d51342d524ce9896d6ba6c708802a", + "357b875d20b94f3eabc1b564da64b647", + "3e55045366084ea49a0097591aca3898", + "19b547a024674276896b6b6a2e5c414d", + "d418953a9420441185f3aa393f4a918c", + "c59d6ac9bbce4bff9ff09a5881318709", + "528a2d7799d8439799a59f9c29d4569e", + "c2772e63e72c4eebbb4aafda30a21524", + "e556fbb2e7f04fdb99c6cb120dc65fad", + "820233d0bb834dd4975e57bb81936a8d", + "bffc916248bf462b818696d6cdedb0a6", + "5a444ac722fe4216b9b22b5e58be698b", + "6e4368d5bd8a4481aad104ad8974eec7", + "07f6ab22dc554ea3920f2eca0b198101", + "cdedd397f5dd41bb8147c624fb838cb3", + "a2291b0703c0427bbd594b3207457918", + "231ce37fcd014253b356939466dec439", + "ef049249fa1b4cb3a70d768f6ccb2e99", + "03ddb6005b504b6bad40d0bf3959ba55", + "937b2cf76aad40dca1733486921fa67d", + "5ff1b797ac694c0ca5c2d977de8b8b36", + "f82edc6f2f274275b51a415b42671a67", + "7d234fdc91734fc7a3077bf2a746acde", + "ddab4a9f3ab14125b4e020a56aadcc55", + "c695251a31c844b9be04436c4cb62e43", + "3983758d870a4073808986dd988d34ec", + "8a02874f28f049689b487f4fd6089a72", + "6104f06ab8e543588dfba55f18d5e66b", + "315863f20660485387ea4bf28f6e7f48", + "7f3343921675419ca00c0151398ee060", + "4ee26ce9f101494abca1427eb5f3bedf", + "ff2ece19dcdc4bb9a353fcfc23d59a99", + "ccc8cd5ee0ed407e84c0ea5d67f4a6ad", + "d7251585f46e4ac784cbf421c820eef5", + "2c3d9cd976b34782ac0c4c154ef4fc83" + ] + }, + "id": "npeoNKU1bq1h", + "outputId": "766fe039-1355-4525-8138-99c1245d7f54" + }, + "execution_count": 5, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + "config.json: 0%| | 0.00/718 [00:00 capture local structure, produce fragments and loops quickly.\n", + "\n", + "- Higher n = more coherence, but still shallow and repetitive.\n", + "\n", + "- GPT-2 medium -> extends poetically with creative phrasing, but eventually degenerates into repetition.\n", + "\n", + "- Key takeaway: context length + self-attention = why LLMs leapfrog n-grams, but sampling strategies still matter." + ], + "metadata": { + "id": "l9WMP-3ykAJy" + } + } + ] +} \ No newline at end of file