{ "cells": [ { "cell_type": "code", "source": [ "!pip -q install git+https://github.com/huggingface/transformers # need to install from github\n", "!pip install -q datasets loralib sentencepiece\n", "!pip -q install bitsandbytes accelerate xformers einops\n", "!pip -q install langchain" ], "metadata": { "id": "TOyVaq6r3oVB", "colab": { "base_uri": "https://localhost:8080/" }, "outputId": "a623741c-16bd-4f2c-bebc-fb22899eb42c" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n", " Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n", " Preparing metadata (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m268.8/268.8 kB\u001b[0m \u001b[31m4.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m7.8/7.8 MB\u001b[0m \u001b[31m97.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m76.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h Building wheel for transformers (pyproject.toml) ... \u001b[?25l\u001b[?25hdone\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m492.2/492.2 kB\u001b[0m \u001b[31m7.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.3/1.3 MB\u001b[0m \u001b[31m78.3 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m115.3/115.3 kB\u001b[0m \u001b[31m17.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m212.5/212.5 kB\u001b[0m \u001b[31m26.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m134.8/134.8 kB\u001b[0m \u001b[31m19.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m92.6/92.6 MB\u001b[0m \u001b[31m20.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m244.2/244.2 kB\u001b[0m \u001b[31m30.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m109.1/109.1 MB\u001b[0m \u001b[31m16.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m42.2/42.2 kB\u001b[0m \u001b[31m5.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.4/1.4 MB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m90.0/90.0 kB\u001b[0m \u001b[31m12.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m49.4/49.4 kB\u001b[0m \u001b[31m6.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", "\u001b[?25h" ] } ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "hdVSk5iZ1DVB", "outputId": "d531353e-e58f-459e-ed70-6e07f4c87df7" }, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Tue Jul 25 09:53:49 2023 \n", "+-----------------------------------------------------------------------------+\n", "| NVIDIA-SMI 525.105.17 Driver Version: 525.105.17 CUDA Version: 12.0 |\n", "|-------------------------------+----------------------+----------------------+\n", "| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n", "| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n", "| | | MIG M. |\n", "|===============================+======================+======================|\n", "| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\n", "| N/A 32C P0 43W / 400W | 0MiB / 40960MiB | 0% Default |\n", "| | | Disabled |\n", "+-------------------------------+----------------------+----------------------+\n", " \n", "+-----------------------------------------------------------------------------+\n", "| Processes: |\n", "| GPU GI CI PID Type Process name GPU Memory |\n", "| ID ID Usage |\n", "|=============================================================================|\n", "| No running processes found |\n", "+-----------------------------------------------------------------------------+\n" ] } ], "source": [ "!nvidia-smi" ] }, { "cell_type": "code", "source": [ "from huggingface_hub import notebook_login\n", "\n", "notebook_login()\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 358, "referenced_widgets": [ "5d0a4aedc4754fe386d03027b33a2102", "ec3b0ba4e150408683701b859af6adc8", "72a9f450065d41a3a54f1f701b2f4cf5", "6a06f6d560a04080b8ee541417c84a54", "a3c53169efde4d17828bc3ff08499a89", "3de9043534fe44d6acbacbd1b83d04b1", "5246edf90f0d422b99fc157ce12f9b66", "b5cf8a1623384f24a8e3b9f91c07f1c4", "3d1648611a43406c9142acb6dc46a54d", "995ebd585d194b738642b12f661bc0e9", "4286fa9759414e1d9606d4bfd07a9db8", "097d1f5f32484c40b108aac2426aed80", "e76fe9ce54874baeb9adb015e789cb9e", "d708343b76ae46e69e36aa6d30192a21", "a0ea158a46b84a6ea1e0dba6037c97eb", "33b766da2cd94e62a663e40ce89e8784", "07ce995691b149d7953eda6a94c56d37" ] }, "id": "NItbG3Ooedqs", "outputId": "0094a5be-9672-4424-ebba-ff408a56d0e7" }, "execution_count": null, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "VBox(children=(HTML(value='
>\\n\", \"\\n<>\\n\\n\"\n", "DEFAULT_SYSTEM_PROMPT = \"\"\"\\\n", "You are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\n", "\n", "If a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\"\"\"\n", "\n", "\n", "\n", "def get_prompt(instruction, new_system_prompt=DEFAULT_SYSTEM_PROMPT ):\n", " SYSTEM_PROMPT = B_SYS + new_system_prompt + E_SYS\n", " prompt_template = B_INST + SYSTEM_PROMPT + instruction + E_INST\n", " return prompt_template\n", "\n", "def cut_off_text(text, prompt):\n", " cutoff_phrase = prompt\n", " index = text.find(cutoff_phrase)\n", " if index != -1:\n", " return text[:index]\n", " else:\n", " return text\n", "\n", "def remove_substring(string, substring):\n", " return string.replace(substring, \"\")\n", "\n", "\n", "\n", "def generate(text):\n", " prompt = get_prompt(text)\n", " with torch.autocast('cuda', dtype=torch.bfloat16):\n", " inputs = tokenizer(prompt, return_tensors=\"pt\").to('cuda')\n", " outputs = model.generate(**inputs,\n", " max_new_tokens=512,\n", " eos_token_id=tokenizer.eos_token_id,\n", " pad_token_id=tokenizer.eos_token_id,\n", " )\n", " final_outputs = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]\n", " final_outputs = cut_off_text(final_outputs, '')\n", " final_outputs = remove_substring(final_outputs, prompt)\n", "\n", " return final_outputs#, outputs\n", "\n", "def parse_text(text):\n", " wrapped_text = textwrap.fill(text, width=100)\n", " print(wrapped_text +'\\n\\n')\n", " # return assistant_text\n" ] }, { "cell_type": "code", "source": [ "instruction = \"What is the temperature in Melbourne?\"\n", "\n", "get_prompt(instruction)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 104 }, "id": "oTf-vPB8wPGs", "outputId": "dcf15769-13f5-4dd5-dd42-c54d7053cef5" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "\"[INST]<>\\nYou are a helpful, respectful and honest assistant. Always answer as helpfully as possible, while being safe. Your answers should not include any harmful, unethical, racist, sexist, toxic, dangerous, or illegal content. Please ensure that your responses are socially unbiased and positive in nature.\\n\\nIf a question does not make any sense, or is not factually coherent, explain why instead of answering something not correct. If you don't know the answer to a question, please don't share false information.\\n<>\\n\\nWhat is the temperature in Melbourne?[/INST]\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "instruction = \"Summarize the following text for me {text}\"\n", "\n", "system_prompt = \"You are an expert and summarization and reducing the number of words used\"\n", "\n", "get_prompt(instruction, system_prompt)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 52 }, "id": "R7n4grsO1UN1", "outputId": "f1355c09-6cef-4f97-dfe7-13671e9c0cba" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'[INST]<>\\nYou are an expert and summarization and reducing the number of words used\\n<>\\n\\nSummarize the following text for me {text}[/INST]'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "markdown", "source": [ "## LangChain basics" ], "metadata": { "id": "-fTKdQRCdis7" } }, { "cell_type": "code", "source": [ "from langchain import HuggingFacePipeline\n", "from langchain import PromptTemplate, LLMChain\n" ], "metadata": { "id": "SP4Bk5YBf1mI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "llm = HuggingFacePipeline(pipeline = pipe, model_kwargs = {'temperature':0})" ], "metadata": { "id": "LL7JGQ5iCzIy" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "system_prompt = \"You are an advanced assistant that excels at translation. \"\n", "instruction = \"Convert the following text from English to French:\\n\\n {text}\"\n", "template = get_prompt(instruction, system_prompt)\n", "print(template)\n", "\n", "prompt = PromptTemplate(template=template, input_variables=[\"text\"])\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "dTBrt6XmuY3Y", "outputId": "4d58cb1c-922b-41c4-d029-cecf41b9b43d" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[INST]<>\n", "You are an advanced assistant that excels at translation. \n", "<>\n", "\n", "Convert the following text from English to French:\n", "\n", " {text}[/INST]\n" ] } ] }, { "cell_type": "code", "source": [ "text = \"how are you today?\"\n", "output = llm_chain.run(text)\n", "\n", "parse_text(output)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "B0fUGp-muY6f", "outputId": "bf505c9b-13c4-4a1d-be33-a587747e6839" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ " Bien sûr! Here is the translation of \"how are you today?\" in French: Comment ça va aujourd'hui?\n", "\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "### Summarization" ], "metadata": { "id": "e38eSqFb3hET" } }, { "cell_type": "code", "source": [ "instruction = \"Summarize the following article for me {text}\"\n", "system_prompt = \"You are an expert and summarization and expressing key ideas succintly\"\n", "\n", "template = get_prompt(instruction, system_prompt)\n", "print(template)\n", "\n", "prompt = PromptTemplate(template=template, input_variables=[\"text\"])\n", "llm_chain = LLMChain(prompt=prompt, llm=llm)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "Dt4mn5CJuY90", "outputId": "1f9c79d3-0222-471e-98e7-4ad6bb562757" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[INST]<>\n", "You are an expert and summarization and expressing key ideas succintly\n", "<>\n", "\n", "Summarize the following article for me {text}[/INST]\n" ] } ] }, { "cell_type": "code", "source": [ "def count_words(input_string):\n", " words = input_string.split(\" \")\n", " return len(words)\n", "\n", "text = '''Twitter (now X) CEO Linda Yaccarino claims usage at ‘all time high’ in memo to staff\n", "Twitter’s (now X’s) newly established CEO Linda Yaccarino touts the company’s success and X’s future plans in a company-wide memo obtained by CNBC. The exec once again claims, without sharing any specific metrics, that the service’s usage is at an “all time high,” and hints at what’s to come in terms of new product experiences for the newly rebranded platform.\n", "\n", "The service formerly known as Twitter has been working to become more than just a social network and more of an “everything app,” as owner Elon Musk dubbed it.\n", "\n", "As the Telsa and Space X exec explained in October 2022, telegraphing Twitter’s eventual rebranding, buying Twitter was meant to be “an accelerant to creating X, the everything app.”\n", "\n", "\n", "His grand plan has been to create an app that allows creators to monetize their content, then later moves into payments services and even banking, Musk remarked during a Twitter Spaces livestream with advertisers in November. At the time, he even mentioned the possibility of establishing money market accounts on Twitter that would pay a high-interest rate to attract consumers to X.\n", "\n", "Those possible product concepts were again referenced in Yaccarino’s new missive, when she writes, “Our usage is at an all time high and we’ll continue to delight our entire community with new experiences in audio, video, messaging, payments, banking – creating a global marketplace for ideas, goods, services, and opportunities.”\n", "\n", "Twitter, now X, has already implemented some of Musk’s ideas around videos and creator monetization. In May, the company began allowing subscribers to upload two-hour videos to its service, which advertiser Apple then leveraged when it released the entire first episode of its hit Apple TV+ show “Silo” on the platform. Fired Fox News host Tucker Carlson had been posting lengthy videos to Twitter as well, until ordered to stop by the network.\n", "\n", "In addition, earlier this month, Twitter began sharing ad revenue with verified creators.\n", "\n", "However, all is not well at Twitter X, whose traffic — at least by third-party measurements — has been dropping. Data from web analytics firm Similarweb indicated Twitter’s web traffic declined 5% for the first two days its latest rival, Instagram Threads, became generally available, compared with the week prior. Plus, Similarweb said Twitter’s web traffic was down 11% compared with the same days in 2022. Additionally, Cloudflare CEO Matthew Prince earlier this month tweeted a graph of traffic to the Twitter.com domain that showed “Twitter traffic tanking,” he said.\n", "\n", "\n", "Yaccarino subtly pushed back at those reports at the time, claiming that Twitter had its largest usage day since February in early July. She did not share any specific metrics or data. At the same time, however, the company was quietly blocking links to Threads.net in Twitter searches, suggesting it was concerned about the new competition.\n", "\n", "Today, Yaccarino repeats her vague claims around X’s high usage in her company-wide memo even as analysts at Forrester are predicting X will either shut down or be acquired within the next 12 months and numerous critics concur that the X rebrand is destined to fail.\n", "\n", "Yaccarino’s memo, otherwise, was mostly a lot of cheerleading, applauding X’s team for their work and touting X’s ability to “impress the world all over again,” as Twitter once did.\n", "\n", "The full memo, courtesy of CBNC, is below:\n", "\n", "Hi team,\n", "\n", "What a momentous weekend. As I said yesterday, it’s extremely rare, whether it’s in life or in business, that you have the opportunity to make another big impression. That’s what we’re experiencing together, in real time. Take a moment to put it all into perspective.\n", "\n", "17 years ago, Twitter made a lasting imprint on the world. The platform changed the speed at which people accessed information. It created a new dynamic for how people communicated, debated, and responded to things happening in the world. Twitter introduced a new way for people, public figures, and brands to build long lasting relationships. In one way or another, everyone here is a driving force in that change. But equally all our users and partners constantly challenged us to dream bigger, to innovate faster, and to fulfill our great potential.\n", "\n", "With X we will go even further to transform the global town square — and impress the world all over again.\n", "\n", "Our company uniquely has the drive to make this possible. Many companies say they want to move fast — but we enjoy moving at the speed of light, and when we do, that’s X. At our core, we have an inventor mindset — constantly learning, testing out new approaches, changing to get it right and ultimately succeeding.\n", "\n", "With X, we serve our entire community of users and customers by working tirelessly to preserve free expression and choice, create limitless interactivity, and create a marketplace that enables the economic success of all its participants.\n", "\n", "The best news is we’re well underway. Everyone should be proud of the pace of innovation over the last nine months — from long form content, to creator monetization, and tremendous advancements in brand safety protections. Our usage is at an all time high and we’ll continue to delight our entire community with new experiences in audio, video, messaging, payments, banking – creating a global marketplace for ideas, goods, services, and opportunities.\n", "\n", "Please don’t take this moment for granted. You’re writing history, and there’s no limit to our transformation. And everyone, is invited to build X with us.\n", "\n", "Elon and I will be working across every team and partner to bring X to the world. That includes keeping our entire community up to date, ensuring that we all have the information we need to move forward.\n", "\n", "Now, let’s go make that next big impression on the world, together.\n", "\n", "Linda'''\n", "\n", "count_words(text)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "gykQOf4OuZBK", "outputId": "a0fbeaa2-508d-429b-f230-d79b50d40225" }, "execution_count": null, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "940" ] }, "metadata": {}, "execution_count": 20 } ] }, { "cell_type": "code", "source": [ "output = llm_chain.run(text)\n", "print(count_words(output))\n", "parse_text(output)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "W4VDYh3VuZD_", "outputId": "030f9295-f8b9-48ac-eaca-f27527029ebd" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "124\n", " Sure, here is a summary of the article in 400 words or less: Twitter's newly appointed CEO, Linda\n", "Yaccarino, has claimed in a memo to staff that the platform's usage is at an all-time high. She also\n", "highlighted the company's grand plans to expand beyond social media and become an \"everything app\"\n", "as envisioned by owner Elon Musk. Yaccarino touted the company's innovation in areas such as long-\n", "form content, creator monetization, and brand safety protections. Despite reports of traffic\n", "decline, Yaccarino remained optimistic and emphasized the company's commitment to preserving free\n", "expression and creating a global marketplace for ideas and opportunities. She also acknowledged the\n", "challenges ahead and encouraged the team to work together to make the next big impression on the\n", "world.\n", "\n", "\n" ] } ] }, { "cell_type": "markdown", "source": [ "## Simple Chatbot" ], "metadata": { "id": "MsPFAOBK74Yi" } }, { "cell_type": "code", "source": [ "from langchain.memory import ConversationBufferMemory\n", "from langchain import LLMChain, PromptTemplate" ], "metadata": { "id": "Inghmejz_Tkj" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "instruction = \"Chat History:\\n\\n{chat_history} \\n\\nUser: {user_input}\"\n", "system_prompt = \"You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\"\n", "\n", "template = get_prompt(instruction, system_prompt)\n", "print(template)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "U7DAJoLEAOU7", "outputId": "c8e5e0b6-846c-4361-d63e-c5537ae9152a" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "{chat_history} \n", "\n", "User: {user_input}[/INST]\n" ] } ] }, { "cell_type": "code", "source": [ "\n", "prompt = PromptTemplate(\n", " input_variables=[\"chat_history\", \"user_input\"], template=template\n", ")\n", "memory = ConversationBufferMemory(memory_key=\"chat_history\")" ], "metadata": { "id": "Qv7iPmbI_TnI" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "llm_chain = LLMChain(\n", " llm=llm,\n", " prompt=prompt,\n", " verbose=True,\n", " memory=memory,\n", ")" ], "metadata": { "id": "CGjjiqxz_TqG" }, "execution_count": null, "outputs": [] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"Hi, my name is Sam\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 295 }, "id": "X3VadjbM_Tsx", "outputId": "ebbe3172-7442-45db-9add-bfb970d2f0c0" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", " \n", "\n", "User: Hi, my name is Sam[/INST]\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "\" Hello Sam! It's nice to meet you. How can I assist you today?\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 35 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"Can you tell me about yourself.\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 330 }, "id": "ENqnCnun_TvC", "outputId": "beb2b1c5-2dbf-4e97-b4fd-ff6f6a583d81" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today? \n", "\n", "User: Can you tell me about yourself.[/INST]\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "\" Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today?\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 36 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"Today is Friday. What number day of the week is that?\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 402 }, "id": "_HIqTiLc_Tx2", "outputId": "f514dbd9-89cc-4414-a38e-3a1d5c047e00" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today?\n", "Human: Can you tell me about yourself.\n", "AI: Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today? \n", "\n", "User: Today is Friday. What number day of the week is that?[/INST]\u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1089: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "' AI: Great question! Friday is the 5th day of the week.'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 37 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"what is the day today?\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 402 }, "id": "r5Kn6p15Ky2F", "outputId": "a0b1c489-479b-4895-e551-c95349fbdbb6" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today?\n", "Human: Can you tell me about yourself.\n", "AI: Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today?\n", "Human: Today is Friday. What number day of the week is that?\n", "AI: AI: Great question! Friday is the 5th day of the week. \n", "\n", "User: what is the day today?[/INST]\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "' AI: Today is Friday! 😊'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 38 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"What is my name?\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 437 }, "id": "CGXX3RoT74qz", "outputId": "17be86ac-6cab-484a-a12c-b2f29c7eadc8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today?\n", "Human: Can you tell me about yourself.\n", "AI: Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today?\n", "Human: Today is Friday. What number day of the week is that?\n", "AI: AI: Great question! Friday is the 5th day of the week.\n", "Human: what is the day today?\n", "AI: AI: Today is Friday! 😊 \n", "\n", "User: What is my name?[/INST]\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "' AI: Hello! My pleasure to assist you. Your name is Sam. 😊'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 39 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"Can you tell me about the olympics\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 610 }, "id": "YCBnZHm9BOQU", "outputId": "5c385c4a-5346-44a4-c030-9c505386fd2e" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today?\n", "Human: Can you tell me about yourself.\n", "AI: Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today?\n", "Human: Today is Friday. What number day of the week is that?\n", "AI: AI: Great question! Friday is the 5th day of the week.\n", "Human: what is the day today?\n", "AI: AI: Today is Friday! 😊\n", "Human: What is my name?\n", "AI: AI: Hello! My pleasure to assist you. Your name is Sam. 😊 \n", "\n", "User: Can you tell me about the olympics[/INST]\u001b[0m\n", "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "\" Of course, I'd be happy to help! The Olympics are a major international sporting event that takes place every 4 years, featuring athletes from around the world competing in a variety of sports. The Olympic Games are overseen by the International Olympic Committee (IOC) and are held in a different city each time.\\nThe Olympics include a wide range of sports, such as track and field, swimming, gymnastics, basketball, and many others. The events are held in various venues within the host city, and athletes compete for medals in each event. The country with the most medals at the end of the games is awarded the Olympic flag, and the host country is awarded the Olympic torch.\\nThe Olympics have a rich history, dating back to 1896 in Athens, Greece. The games have evolved over the years, with new sports being added and others being removed. The Olympics have also been host to some of the most iconic moments in sports history, including Jesse Owens' four gold medals in Berlin in 1936 and Michael Phelps' record-breaking performance in Beijing in 2008.\\nI hope that helps! Let me know if you have any other questions.\"" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 40 } ] }, { "cell_type": "code", "source": [ "llm_chain.predict(user_input=\"What have we talked about in this Chat?\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 732 }, "id": "Nf2QRotKBOTq", "outputId": "eb593c9e-a9fa-4806-a107-6d1f148bd9f8" }, "execution_count": null, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "\n", "\n", "\u001b[1m> Entering new LLMChain chain...\u001b[0m\n", "Prompt after formatting:\n", "\u001b[32;1m\u001b[1;3m[INST]<>\n", "You are a helpful assistant, you always only answer for the assistant then you stop. read the chat history to get context\n", "<>\n", "\n", "Chat History:\n", "\n", "Human: Hi, my name is Sam\n", "AI: Hello Sam! It's nice to meet you. How can I assist you today?\n", "Human: Can you tell me about yourself.\n", "AI: Of course! I'm just an AI designed to assist and provide helpful responses. I'm here to help you with any questions or tasks you may have. How can I assist you today?\n", "Human: Today is Friday. What number day of the week is that?\n", "AI: AI: Great question! Friday is the 5th day of the week.\n", "Human: what is the day today?\n", "AI: AI: Today is Friday! 😊\n", "Human: What is my name?\n", "AI: AI: Hello! My pleasure to assist you. Your name is Sam. 😊\n", "Human: Can you tell me about the olympics\n", "AI: Of course, I'd be happy to help! The Olympics are a major international sporting event that takes place every 4 years, featuring athletes from around the world competing in a variety of sports. The Olympic Games are overseen by the International Olympic Committee (IOC) and are held in a different city each time.\n", "The Olympics include a wide range of sports, such as track and field, swimming, gymnastics, basketball, and many others. The events are held in various venues within the host city, and athletes compete for medals in each event. The country with the most medals at the end of the games is awarded the Olympic flag, and the host country is awarded the Olympic torch.\n", "The Olympics have a rich history, dating back to 1896 in Athens, Greece. The games have evolved over the years, with new sports being added and others being removed. The Olympics have also been host to some of the most iconic moments in sports history, including Jesse Owens' four gold medals in Berlin in 1936 and Michael Phelps' record-breaking performance in Beijing in 2008.\n", "I hope that helps! Let me know if you have any other questions. \n", "\n", "User: What have we talked about in this Chat?[/INST]\u001b[0m\n" ] }, { "output_type": "stream", "name": "stderr", "text": [ "/usr/local/lib/python3.10/dist-packages/transformers/pipelines/base.py:1089: UserWarning: You seem to be using the pipelines sequentially on GPU. In order to maximize efficiency please use a dataset\n", " warnings.warn(\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "\n", "\u001b[1m> Finished chain.\u001b[0m\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "\" Of course! Here's what we've discussed in this chat:\\n1. The assistant introduced themselves and asked how they could assist the user.\\n2. The user asked the assistant to tell them about themselves, to which the assistant replied that they are just an AI designed to assist and provide helpful responses.\\n3. The user asked what day of the week it is, and the assistant replied that it is Friday, which is the 5th day of the week.\\n4. The user asked their name, and the assistant replied that their name is Sam.\\n5. The user asked the assistant to tell them about the Olympics, and the assistant provided a detailed response about the Olympics, including their history, sports included, and the awarding of medals.\\n6. 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