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It’s a strong general-purpose instruct model, typically reliable for structured prompting and multi-lingual-ish behavior in practice (depending on the prompt).\n", "\n", "#### Mistral-Nemo-Instruct-2407\n", "\n", "An instruction-tuned model from Mistral AI + NVIDIA collaboration, designed to be competitive in its size range. It’s a good choice to compare “Mistral-style” instruction-following and robustness.\n", "\n", "#### Gemma-2-9b-it\n", "\n", "Google’s Gemma 2 instruction-tuned 9B model (text-to-text, decoder-only). It can be excellent, but it’s often gated on Hugging Face (students may need access approval/token)." ], "metadata": { "id": "Fi73X-fanRDj" } }, { "cell_type": "markdown", "source": [ "## 1) Environment setup (one-time)" ], "metadata": { "id": "0npkiPS8pPdy" } }, { "cell_type": "markdown", "source": [ "### 1.1 Install packages" ], "metadata": { "id": "_ByIU3QVpXPS" } }, { "cell_type": "code", "source": [ "# install required libraries (run once)\n", "!pip install -q transformers accelerate torch huggingface_hub" ], "metadata": { "id": "QKie0_3Vo80I" }, "execution_count": 26, "outputs": [] }, { "cell_type": "code", "source": [ "# import library used to login to Hugging Face\n", "from huggingface_hub import login\n", "\n", "# access secure secrets stored in Google Colab\n", "from google.colab import userdata\n", "\n", "# load the Hugging Face token from Colab secrets\n", "hf_token = userdata.get(\"HF_TOKEN\")\n", "\n", "# authenticate to Hugging Face\n", "# this is required for gated models such as Gemma\n", "login(token=hf_token)" ], "metadata": { "id": "KR_FkrpUtyG2" }, "execution_count": 27, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 2) model loader" ], "metadata": { "id": "je7asyDFpl04" } }, { "cell_type": "code", "source": [ "# import libraries used to load LLMs\n", "from transformers import AutoTokenizer, AutoModelForCausalLM\n", "import torch\n", "\n", "# model identifier on Hugging Face\n", "model_id = \"Qwen/Qwen2-7B-Instruct\"\n", "\n", "# load the tokenizer\n", "# tokenizer converts text → tokens (numbers the model understands)\n", "tokenizer_qwen = AutoTokenizer.from_pretrained(\n", " model_id,\n", " use_fast=True\n", ")\n", "\n", "# load the model\n", "model_qwen = AutoModelForCausalLM.from_pretrained(\n", "\n", " model_id,\n", "\n", " # bfloat16 reduces memory usage on GPU\n", " torch_dtype=torch.bfloat16,\n", "\n", " # automatically place model layers on GPU if available\n", " device_map=\"auto\"\n", ")\n", "\n", "print(\"Qwen model loaded\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 67, "referenced_widgets": [ "557cded45d2f4985b1bf88d24a53fa5b", "01048abb466d40ad9d5eebb933e31d2e", "0070a9cb654c454db2d50b6a45a0564d", "8f0c8bd7c2314d3fa975b1ab0e2b11c3", "61c37f92a6884a8b9040f5cd94455907", "10b53f45b8b64f3f937059478260da97", "24ffc90e04ca4e58934ad06c7a30a21a", "d8c02766a73142579e2d4b2e33d0e359", "2152c0b928f54feeb0305eb8a3b5cedf", "059143add03442859bc657741923fe3d", "fe4c4516392840729ec9ccb7f427de83" ] }, "id": "EzXi0YpwpEMt", "outputId": "7026ae2b-18a3-45cd-c944-4ece19082b0e" }, "execution_count": 28, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "Loading weights: 0%| | 0/339 [00:00 tokens)\n", "inputs = tokenizer_qwen(prompt, return_tensors=\"pt\")\n", "\n", "# move inputs to the same device as the model (GPU/CPU)\n", "inputs = inputs.to(model_qwen.device)\n", "\n", "# generate a short output (we only need a label)\n", "output = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=6, # small because output should be one word\n", " do_sample=False # deterministic decoding (good for evaluation)\n", ")\n", "\n", "# decode tokens back to text\n", "decoded = tokenizer_qwen.decode(output[0], skip_special_tokens=True)\n", "\n", "# extract the part after \"Label:\" (simple parsing)\n", "prediction_qwen = decoded.split(\"Label:\")[-1].strip().splitlines()[0].strip()\n", "\n", "print(\"Qwen prediction:\", prediction_qwen)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gZAWBub_xzmL", "outputId": "6a6b15f1-071a-4786-8a7b-ce2adc4d0e09" }, "execution_count": 36, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "The following generation flags are not valid and may be ignored: ['temperature', 'top_p', 'top_k']. Set `TRANSFORMERS_VERBOSITY=info` for more details.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Qwen prediction: Hopelessness\n" ] } ] }, { "cell_type": "code", "source": [ "#Run zero-shot classification with mistralai/Mistral-Nemo-Instruct-2407\n", "inputs = tokenizer_mistral(prompt, return_tensors=\"pt\")\n", "inputs = inputs.to(model_mistral.device)\n", "\n", "output = model_mistral.generate(\n", " **inputs,\n", " max_new_tokens=6,\n", " do_sample=False\n", ")\n", "\n", "decoded = tokenizer_mistral.decode(output[0], skip_special_tokens=True)\n", "prediction_mistral = decoded.split(\"Label:\")[-1].strip().splitlines()[0].strip()\n", "\n", "print(\"Mistral-Nemo prediction:\", prediction_mistral)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hdf-YrBSxziL", "outputId": "d5f1b361-4c10-4c13-8c5b-d4b5e3de8d13" }, "execution_count": 37, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "Mistral-Nemo prediction: Hopelessness\n" ] } ] }, { "cell_type": "code", "source": [ "#Run zero-shot classification with google/gemma-2-9b-it\n", "inputs = tokenizer_gemma(prompt, return_tensors=\"pt\")\n", "inputs = inputs.to(model_gemma.device)\n", "\n", "output = model_gemma.generate(\n", " **inputs,\n", " max_new_tokens=6,\n", " do_sample=False\n", ")\n", "\n", "decoded = tokenizer_gemma.decode(output[0], skip_special_tokens=True)\n", "prediction_gemma = decoded.split(\"Label:\")[-1].strip().splitlines()[0].strip()\n", "\n", "print(\"Gemma prediction:\", prediction_gemma)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "akdUBmbXypwd", "outputId": "6fa4e641-2cbe-40b5-a098-e86f74b62ebd" }, "execution_count": 40, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Gemma prediction: Hopelessness\n" ] } ] }, { "cell_type": "markdown", "source": [ "### 6) Experiment B — Few-shot learning (FSL)" ], "metadata": { "id": "KZVjtihY1SIR" } }, { "cell_type": "code", "source": [], "metadata": { "id": "UCGqT9Aq1T4_" }, "execution_count": 60, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "ZMgDm0do1xDw" }, "execution_count": 61, "outputs": [] }, { "cell_type": "code", "source": [ "labels = [\"Hope\", \"Hopelessness\", \"None\"]\n", "\n", "content = f\"\"\"\n", "You are a text classifier.\n", "\n", "Allowed labels: {labels}\n", "Return ONLY one label. No explanation, no punctuation.\n", "\n", "Examples:\n", "Text: I can't wait for tomorrow. I believe things will improve.\n", "Label: Hope\n", "\n", "Text: Nothing will ever get better. I feel like giving up.\n", "Label: Hopelessness\n", "\n", "Text: I went to the grocery store and bought milk.\n", "Label: None\n", "\n", "Now classify:\n", "Text: I hope I pass my exam next week.\n", "Label:\n", "\"\"\".strip()" ], "metadata": { "id": "LemqfSpn1Ti0" }, "execution_count": 71, "outputs": [] }, { "cell_type": "code", "source": [ "prompt_qwen = f\"\"\"System: You follow instructions exactly.\n", "User: {content}\n", "Assistant:\"\"\"\n", "\n", "\n", "inputs = tokenizer_qwen(prompt_qwen, return_tensors=\"pt\").to(model_qwen.device)\n", "\n", "out = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=10,\n", " do_sample=False,\n", " pad_token_id=tokenizer_qwen.eos_token_id\n", ")\n", "\n", "decoded = tokenizer_qwen.decode(out[0], skip_special_tokens=True)\n", "print(decoded[len(prompt_qwen):].strip())" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "BZXoLvkZ4F4L", "outputId": "42150c05-a754-42eb-87c1-7fdc958babfa" }, "execution_count": 74, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Hope\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "l0w0oqDa4Fud" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Prompt Sensitivity Experiment\n", "\n", "\n" ], "metadata": { "id": "wIQ9GpoH5HCv" } }, { "cell_type": "code", "source": [ "def run_models(models, prompt, max_tokens=150):\n", "\n", " \"\"\"\n", " models: dictionary where\n", " key = model name (string)\n", " value = tuple (tokenizer, model)\n", "\n", " prompt: text prompt we want to send to all models\n", "\n", " max_tokens: maximum number of tokens the model can generate\n", " \"\"\"\n", "\n", " # iterate through each model in the dictionary\n", " for name, (tokenizer, model) in models.items():\n", "\n", " print(\"\\n==============================\")\n", " print(\"MODEL:\", name)\n", " print(\"==============================\")\n", "\n", " # convert the text prompt into tokens\n", " inputs = tokenizer(prompt, return_tensors=\"pt\")\n", "\n", " # move tokens to the same device as the model (GPU or CPU)\n", " inputs = inputs.to(model.device)\n", "\n", " # generate output text\n", " output = model.generate(\n", "\n", " **inputs,\n", "\n", " # maximum number of tokens to generate\n", " max_new_tokens=max_tokens,\n", "\n", " # deterministic decoding (no randomness)\n", " do_sample=False\n", " )\n", "\n", " # convert tokens back to human-readable text\n", " decoded = tokenizer.decode(output[0], skip_special_tokens=True)\n", "\n", " # print the result\n", " print(decoded)" ], "metadata": { "id": "M59a5XM14FlW" }, "execution_count": 99, "outputs": [] }, { "cell_type": "code", "source": [ "\n", "models = {\n", "\n", " \"Qwen\": (tokenizer_qwen, model_qwen),\n", "\n", " \"Mistral Nemo\": (tokenizer_mistral, model_mistral),\n", "\n", " \"Gemma\": (tokenizer_gemma, model_gemma)\n", "\n", "}" ], "metadata": { "id": "i2zAyjhCxzfL" }, "execution_count": 100, "outputs": [] }, { "cell_type": "code", "source": [ "prompt = \"\"\"\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\"\"\"\n", "\n", "\n", "\n", "run_models(models, prompt, max_tokens=10)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ZNy2CGhCxzcb", "outputId": "8cbc99ff-dcac-4d26-f939-7cb198299bd1" }, "execution_count": 101, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "==============================\n", "MODEL: Qwen\n", "==============================\n", "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "However, they may lack common sense or fail to\n", "\n", "==============================\n", "MODEL: Mistral Nemo\n", "==============================\n", "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "However, they can also produce factually incorrect or\n", "\n", "==============================\n", "MODEL: Gemma\n", "==============================\n", "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\n", "\n", "\n", "Large language models are powerful AI systems\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"\"\"\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\n", "Summary:\n", "\"\"\"\n", "\n", "\n", "models = {\n", "\n", " \"Qwen\": (tokenizer_qwen, model_qwen),\n", "\n", " \"Mistral Nemo\": (tokenizer_mistral, model_mistral),\n", "\n", " \"Gemma\": (tokenizer_gemma, model_gemma)\n", "\n", "}\n", "\n", "run_models(models, prompt)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "_AtQJkthxzZi", "outputId": "2c2baf22-18fc-43dc-8f3a-c1e46edacbac" }, "execution_count": 103, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "==============================\n", "MODEL: Qwen\n", "==============================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\n", "Summary:\n", "Large language models are neural networks trained on extensive text data that learn language patterns to generate coherent text, answer questions, translate languages, and support various natural language processing tasks.\n", "\n", "==============================\n", "MODEL: Mistral Nemo\n", "==============================\n", "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\n", "Summary:\n", "Large language models are neural networks trained on vast text data to perform various NLP tasks.\n", "\n", "==============================\n", "MODEL: Gemma\n", "==============================\n", "\n", "Summarize the following text in one short sentences.\n", "\n", "Text:\n", "Large language models are neural networks trained on massive text corpora.\n", "They learn statistical patterns in language and can generate coherent text,\n", "answer questions, translate languages, and assist in many NLP tasks.\n", "\n", "Summary:\n", "Large language models are powerful AI systems trained on text data to perform various language-related tasks. \n", "\n", "\n", "\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"\"\"\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me ONLY final answer.\n", "\"\"\"\n", "\n", "\n", "models = {\n", "\n", " \"Qwen\": (tokenizer_qwen, model_qwen),\n", "\n", " \"Mistral Nemo\": (tokenizer_mistral, model_mistral),\n", "\n", " \"Gemma\": (tokenizer_gemma, model_gemma)\n", "\n", "}\n", "\n", "run_models(models, prompt)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "HS0ZntMvxzWQ", "outputId": "29470232-4b71-485e-c3f3-b0e6f36fe346" }, "execution_count": 104, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "==============================\n", "MODEL: Qwen\n", "==============================\n", "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me ONLY final answer.\n", "assistant: 120 km/h\n", "\n", "==============================\n", "MODEL: Mistral Nemo\n", "==============================\n", "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me ONLY final answer.\n", "10\n", "\n", "==============================\n", "MODEL: Gemma\n", "==============================\n", "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me ONLY final answer.\n", " \n", " 120\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"\"\"\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me answer step by step.\n", "\"\"\"\n", "\n", "\n", "models = {\n", "\n", " \"Qwen\": (tokenizer_qwen, model_qwen),\n", "\n", " \"Mistral Nemo\": (tokenizer_mistral, model_mistral),\n", "\n", " \"Gemma\": (tokenizer_gemma, model_gemma)\n", "\n", "}\n", "\n", "run_models(models, prompt)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "kz8LPGBsxzTJ", "outputId": "27eb98d3-df41-40ea-f7f1-1ec427525975" }, "execution_count": 105, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "==============================\n", "MODEL: Qwen\n", "==============================\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:2 for open-end generation.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me answer step by step.\n", "Answer: The train's speed is 120 km/h.\n", "\n", "Step-by-step explanation:\n", "\n", "1. **Convert time to hours**: Since the speed needs to be in km/h (kilometers per hour), we need to convert the time from minutes to hours. There are 60 minutes in an hour, so 30 minutes is equal to \\( \\frac{30}{60} = 0.5 \\) hours.\n", "\n", "2. **Calculate speed**: Speed is calculated using the formula:\n", "\\[ \\text{Speed} = \\frac{\\text{Distance}}{\\text{Time}} \\]\n", "\n", "Given that the distance is 60 km and the time is 0.5 hours, we can substitute these values into the\n", "\n", "==============================\n", "MODEL: Mistral Nemo\n", "==============================\n", "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me answer step by step.\n", "To find the speed of the train, we need to divide the distance traveled by the time taken. Let's do this step by step:\n", "\n", "1. First, convert the time from minutes to hours:\n", " Time = 30 minutes\n", " To convert minutes to hours, divide by 60 (since there are 60 minutes in an hour):\n", " Time in hours = 30 minutes ÷ 60 = 0.5 hours\n", "\n", "2. Now, we can calculate the speed using the formula:\n", " Speed = Distance ÷ Time\n", " Speed = 60 km ÷ 0.5 hours\n", "\n", "3. Perform the division to find the speed:\n", " Speed = 120 km/h\n", "\n", "So\n", "\n", "==============================\n", "MODEL: Gemma\n", "==============================\n", "\n", "If a train travels 60 km in 30 minutes, what is its speed in km/h? give me answer step by step.\n", "Here's how to calculate the speed:\n", "\n", "**1. Convert minutes to hours:**\n", "\n", "* There are 60 minutes in 1 hour.\n", "* So, 30 minutes is equal to 30/60 = 0.5 hours.\n", "\n", "**2. Calculate speed:**\n", "\n", "* Speed is calculated by dividing distance by time.\n", "* Speed = Distance / Time\n", "* Speed = 60 km / 0.5 hours\n", "* Speed = 120 km/h\n", "\n", "\n", "**Therefore, the speed of the train is 120 km/h.**\n", "\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "nCSJ2n7XxzPf" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "v4_iT2vOxzLC" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Experiment — Schema-constrained output (JSON) + validation" ], "metadata": { "id": "wLDg5xMkrUgw" } }, { "cell_type": "code", "source": [ "!pip install -q pydantic" ], "metadata": { "id": "Hr7_vrI4rR15" }, "execution_count": 106, "outputs": [] }, { "cell_type": "code", "source": [ "import json\n", "import re\n", "from pydantic import BaseModel, ValidationError\n", "from typing import Literal" ], "metadata": { "id": "huZAvtXyrqXk" }, "execution_count": 107, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 1) Define the JSON schema (once)" ], "metadata": { "id": "_GbpZHfU7Uvw" } }, { "cell_type": "code", "source": [ "class OutputSchema(BaseModel):\n", " # primary label must be exactly one of these values\n", " label: Literal[\"Hope\", \"Hopelessness\", \"None\"]\n", "\n", " # exact supporting span copied from the original text (can be empty for None)\n", " evidence_span: str\n", "\n", " # short technical explanation (1–2 sentences)\n", " rationale: str" ], "metadata": { "id": "u0lzqnMI7Cjz" }, "execution_count": 108, "outputs": [] }, { "cell_type": "markdown", "source": [ "### 2) Create the schema prompt (once)" ], "metadata": { "id": "3lAuT-7Z7af6" } }, { "cell_type": "code", "source": [ "text = \"I really hope I can finish my thesis this semester, but I feel overwhelmed sometimes.\"\n", "\n", "schema_prompt_content = f\"\"\"\n", "Return ONLY a valid JSON object (no markdown, no extra text) with exactly these keys:\n", "- label: one of [\"Hope\",\"Hopelessness\",\"None\"]\n", "- evidence_span: copy the exact phrase from the text that supports the label (or \"\" if label is None)\n", "- rationale: 1-2 technical sentences explaining the decision\n", "\n", "Text:\n", "{text}\n", "\n", "JSON:\n", "\"\"\".strip()\n", "\n", "print(schema_prompt_content)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "MkK-ZFCm7Jzr", "outputId": "53bd4e13-2a25-4440-ea3b-a41e6bacc26a" }, "execution_count": 109, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Return ONLY a valid JSON object (no markdown, no extra text) with exactly these keys:\n", "- label: one of [\"Hope\",\"Hopelessness\",\"None\"]\n", "- evidence_span: copy the exact phrase from the text that supports the label (or \"\" if label is None)\n", "- rationale: 1-2 technical sentences explaining the decision\n", "\n", "Text:\n", "I really hope I can finish my thesis this semester, but I feel overwhelmed sometimes.\n", "\n", "JSON:\n" ] } ] }, { "cell_type": "markdown", "source": [ "### 3) Qwen: generate JSON + validate\n", "#### 3.1 Wrap prompt as plain string (Qwen-friendly)" ], "metadata": { "id": "bzdV27GF7hup" } }, { "cell_type": "code", "source": [ "prompt_qwen = f\"\"\"System: You follow instructions exactly.\n", "User: {schema_prompt_content}\n", "Assistant:\"\"\"\n", "\n", "\n", "inputs = tokenizer_qwen(prompt_qwen, return_tensors=\"pt\").to(model_qwen.device)\n", "\n", "out = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=220,\n", " do_sample=False,\n", " pad_token_id=tokenizer_qwen.eos_token_id\n", ")\n", "\n", "decoded = tokenizer_qwen.decode(out[0], skip_special_tokens=True)\n", "\n", "# only the generated continuation (so prompt text doesn't confuse parsing)\n", "gen = decoded[len(prompt_qwen):].strip()\n", "print(gen)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Svm-45lV7OFS", "outputId": "79aa3bf3-868c-45d5-ab09-c2646740fc80" }, "execution_count": 110, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{\"label\": \"Hope\", \"evidence_span\": \"I really hope I can finish my thesis this semester\", \"rationale\": \"The text expresses a positive sentiment and expectation about completing a task, indicating hope.\"}\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "wEu-BTgL7ryk" }, "execution_count": null, "outputs": [] }, { "cell_type": "markdown", "source": [ "### Deterministic vs Sampling (temperature)" ], "metadata": { "id": "y5Moo46wESOq" } }, { "cell_type": "code", "source": [ "prompt = \"Write one paragraph explaining what overfitting is. one short sentence\"\n", "\n", "inputs = tokenizer_qwen(prompt, return_tensors=\"pt\").to(model_qwen.device)\n", "\n", "out = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=140,\n", " do_sample=False,\n", " temperature=0.1, # lower = less randomness\n", " pad_token_id=tokenizer_qwen.eos_token_id\n", ")\n", "\n", "print(tokenizer_qwen.decode(out[0], skip_special_tokens=True))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "H4q3IahuAy1S", "outputId": "b263b6bc-d63c-4a9f-a8dd-8ebc132002dc" }, "execution_count": 113, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Write one paragraph explaining what overfitting is. one short sentence: Overfitting occurs when a statistical model or machine learning algorithm captures the noise of the data in addition to the underlying pattern, resulting in poor generalization to new, unseen data. This typically happens when a model is overly complex or trained for too many iterations on a limited dataset, leading it to fit the training data too closely, including its random fluctuations and outliers, rather than learning the true relationship between the input features and the target variable. As a result, the model performs exceptionally well on the training data but fails to accurately predict outcomes on new data, thus reducing its predictive power and reliability. To mitigate overfitting, techniques such as cross-validation, regularization, early stopping, and increasing the\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"Write one paragraph explaining what overfitting is. one short sentence\"\n", "\n", "inputs = tokenizer_qwen(prompt, return_tensors=\"pt\").to(model_qwen.device)\n", "\n", "out = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=140,\n", " do_sample=True,\n", " temperature=0.5, # lower = less randomness\n", " pad_token_id=tokenizer_qwen.eos_token_id\n", ")\n", "\n", "print(tokenizer_qwen.decode(out[0], skip_special_tokens=True))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "ebA25DZuECDL", "outputId": "eb4bcb3a-a88e-4030-fc59-2bd42a839269" }, "execution_count": 116, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Write one paragraph explaining what overfitting is. one short sentence that explains overfitting is when a statistical model or machine learning algorithm captures the noise of the data in addition to the underlying pattern, resulting in poor generalization to new, unseen data. This occurs when the model becomes too complex and learns the specific details and random fluctuations in the training data, rather than identifying the broader relationships that hold true for the entire population. As a result, while the model may perform exceptionally well on the dataset it was trained on, its predictive power significantly diminishes when applied to new data, leading to inaccurate predictions and decisions. To mitigate overfitting, techniques such as cross-validation, regularization, and increasing the size of the training dataset are commonly employed to ensure the model\n" ] } ] }, { "cell_type": "code", "source": [ "prompt = \"Write one paragraph explaining what overfitting is. one short sentence\"\n", "\n", "inputs = tokenizer_qwen(prompt, return_tensors=\"pt\").to(model_qwen.device)\n", "\n", "out = model_qwen.generate(\n", " **inputs,\n", " max_new_tokens=140,\n", " do_sample=True, # turn on sampling\n", " temperature=0.8, # higher = more randomness\n", "\n", " pad_token_id=tokenizer_qwen.eos_token_id\n", ")\n", "\n", "print(tokenizer_qwen.decode(out[0], skip_special_tokens=True))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "DYN8TtkXA0Af", "outputId": "f978889a-b4dc-433f-f2a4-038b89aa9e16" }, "execution_count": 114, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Write one paragraph explaining what overfitting is. one short sentence explaining why it is a problem.\n", "\n", "Overfitting occurs when a machine learning model learns the training data too well, to the point where it starts to capture the noise and outliers in the data rather than the underlying pattern. This results in a model that performs exceptionally well on the training data but poorly on new, unseen data, making it unreliable for making accurate predictions or decisions. It's a problem because the ultimate goal of machine learning is to generalize well to new data, and an overfitted model fails to achieve this objective.\n" ] } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "KCJFZsxkBr4G" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [], "metadata": { "id": "p0oxSDcyE-A8" }, "execution_count": null, "outputs": [] } ] }