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"@jupyter-widgets/base", "_view_module_version": "1.2.0", "_view_name": "StyleView", "description_width": "" } } } } }, "cells": [ { "cell_type": "markdown", "source": [ "# Huggingface With Langchain\n", "\n", "Announcement Link: https://huggingface.co/blog/langchain\n" ], "metadata": { "id": "FMLcVSNYtyHl" } }, { "cell_type": "code", "source": [ "## Libraries Required\n", "!pip install langchain-huggingface\n", "## For API Calls\n", "!pip install huggingface_hub\n", "!pip install transformers\n", "!pip install accelerate\n", "!pip install bitsandbytes\n", "!pip install langchain\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "XLWDCEdHt_zh", "outputId": "0c9e5412-e612-460d-f1c0-69d184317ece" }, "execution_count": 1, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "Collecting langchain-huggingface\n", " Downloading langchain_huggingface-0.0.1-py3-none-any.whl (17 kB)\n", "Requirement already satisfied: huggingface-hub>=0.23.0 in 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"21db8f7a-4c1a-42c7-db19-b95e0b92ad17" }, "execution_count": 2, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "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\n" ] } ] }, { "cell_type": "markdown", "source": [ "## HuggingFaceEndpoint\n", "## How to Access HuggingFace Models with API\n", "There are also two ways to use this class. You can specify the model with the repo_id parameter. Those endpoints use the serverless API, which is particularly beneficial to people using pro accounts or enterprise hub. Still, regular users can already have access to a fair amount of request by connecting with their HF token in the environment where they are executing the code.\n" ], "metadata": { "id": "JzOjqBkjD3l2" } }, { "cell_type": "code", "source": [ "from langchain_huggingface import HuggingFaceEndpoint" ], "metadata": { "id": "CEigbCwUEG92" }, "execution_count": 3, "outputs": [] }, { "cell_type": "code", "source": [ "from google.colab import userdata\n", "sec_key=userdata.get(\"HUGGINGFACEHUB_API_TOKEN\")\n", "print(sec_key)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "KDzGTtF53o8C", "outputId": "53ae5479-0b31-4fc8-800d-49c33c79523e" }, "execution_count": 4, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "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\n" ] } ] }, { "cell_type": "code", "source": [ "import os\n", "os.environ[\"HUGGINGFACEHUB_API_TOKEN\"]=sec_key" ], "metadata": { "id": "cOyD_m8R3iYB" }, "execution_count": 5, "outputs": [] }, { "cell_type": "code", "source": [ "repo_id=\"mistralai/Mistral-7B-Instruct-v0.2\"\n", "llm=HuggingFaceEndpoint(repo_id=repo_id,max_length=128,temperature=0.7,token=sec_key)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "u3K3lb8037l-", "outputId": "3b3cd2da-1bb2-426a-a15c-09b3d1d583ad" }, "execution_count": 6, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:langchain_huggingface.llms.huggingface_endpoint:WARNING! max_length is not default parameter.\n", " max_length was transferred to model_kwargs.\n", " Please make sure that max_length is what you intended.\n", "WARNING:langchain_huggingface.llms.huggingface_endpoint:WARNING! token is not default parameter.\n", " token was transferred to model_kwargs.\n", " Please make sure that token is what you intended.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", "Token is valid (permission: read).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "llm.invoke(\"What is machine learning\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 157 }, "id": "c1GzJopg4Xni", "outputId": "a1eacc2c-fd96-40b4-ad83-6412e817a4a5" }, "execution_count": 8, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'? Machine learning is a type of artificial intelligence that enables computer systems to learn and improve from experience without being explicitly programmed. It is based on the idea that a system can learn to recognize patterns and make decisions with data, rather than through explicit instructions given to it. Machine learning algorithms use statistical techniques to analyze and learn from data, and then use that knowledge to make predictions or take actions.\\n\\nMachine learning is used in a wide range of applications, from image and speech recognition to fraud detection and recommendation systems. It is also used in self-driving cars, virtual assistants, and many other advanced technologies. Machine learning algorithms can be divided into three main categories: supervised learning, unsupervised learning, and reinforcement learning.\\n\\nSupervised learning involves training a machine learning model on labeled data, which means that the data comes with known outputs. The model learns to map inputs to outputs based on the labeled examples it is given. Once the model is trained, it can make predictions on new, unlabeled data.\\n\\nUnsupervised learning involves training a machine learning model on unlabeled data, which means that the data does not come with known outputs. The model learns to find patterns and relationships in the data on its own, without any explicit guidance. Unsupervised learning is often used for clustering data or identifying anomalies.\\n\\nReinforcement learning involves training a machine learning model to take actions in an environment to maximize a reward. The model learns by interacting with the environment and receiving feedback in the form of rewards or penalties. Reinforcement learning is often used in gaming and robotics applications.\\n\\nMachine learning is a powerful technology that has the potential to revolutionize many industries and improve our lives in countless ways. However, it also raises important ethical and social issues, such as privacy, bias, and job displacement. It is important for developers, policymakers, and the public to engage in thoughtful and informed discussions about the role and impact of machine learning in our society.'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 8 } ] }, { "cell_type": "code", "source": [ "repo_id=\"mistralai/Mistral-7B-Instruct-v0.3\"\n", "llm=HuggingFaceEndpoint(repo_id=repo_id,max_length=128,temperature=0.7,token=sec_key)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "0luWPLuA4zSJ", "outputId": "30e705fb-0316-4f62-aac8-248042356d80" }, "execution_count": 9, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "WARNING:langchain_huggingface.llms.huggingface_endpoint:WARNING! max_length is not default parameter.\n", " max_length was transferred to model_kwargs.\n", " Please make sure that max_length is what you intended.\n", "WARNING:langchain_huggingface.llms.huggingface_endpoint:WARNING! token is not default parameter.\n", " token was transferred to model_kwargs.\n", " Please make sure that token is what you intended.\n" ] }, { "output_type": "stream", "name": "stdout", "text": [ "The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n", "Token is valid (permission: read).\n", "Your token has been saved to /root/.cache/huggingface/token\n", "Login successful\n" ] } ] }, { "cell_type": "code", "source": [ "llm.invoke(\"What is Genertaive AI\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 157 }, "id": "fV0XxaQT43h7", "outputId": "ea08e931-80bc-4804-dead-bbeeae459419" }, "execution_count": 10, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "' and how it works?\\n\\nGenerative AI is a type of artificial intelligence (AI) that creates new content by learning patterns from existing data. It can generate text, images, music, and even video content that is similar to, but not identical to, the data it was trained on.\\n\\nGenerative AI works by using a process called generative modeling. This involves training a machine learning model on a large dataset, such as a collection of images, texts, or other data. The model learns the underlying patterns and structures in the data, and can then use these patterns to generate new content.\\n\\nThere are several types of generative AI models, including:\\n\\n1. Generative adversarial networks (GANs): These are a type of neural network that consist of two parts: a generator and a discriminator. The generator creates new content, while the discriminator tries to determine whether the content was created by the generator or was taken from the training data. The two parts are trained together, with the generator trying to improve its ability to create realistic content, and the discriminator trying to become better at identifying fake content.\\n2. Variational autoencoders (VAEs): These are a type of neural network that learns a probability distribution over the data it was trained on. The network can then sample from this distribution to generate new content.\\n3. Recurrent neural networks (RNNs): These are a type of neural network that are particularly good at processing sequential data, such as text. They can be used to generate new text by predicting the next word in a sequence based on the words that came before it.\\n4. Transformer models: These are a type of neural network that are particularly good at processing long sequences of data, such as sentences or paragraphs of text. They can be used to generate new text by predicting the next word in a sequence based on the words that came before it.\\n\\nGenerative AI has many potential applications, including creating realistic synthetic data for training other AI models, generating personalized content for users, and creating art and music. However, it also raises important ethical and societal questions, such as the potential for AI to create deepfakes and other forms of misinformation, and the potential for AI to replace human creators in certain industries.'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 10 } ] }, { "cell_type": "code", "source": [ "from langchain import PromptTemplate, LLMChain\n", "\n", "question=\"Who won the Cricket World Cup in the year 2011?\"\n", "template = \"\"\"Question: {question}\n", "Answer: Let's think step by step.\"\"\"\n", "prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n", "print(prompt)" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "nA2j9nSG5Alz", "outputId": "bb8e4eae-090c-4450-a888-ba048922bad3" }, "execution_count": 11, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "input_variables=['question'] template=\"Question: {question}\\nAnswer: Let's think step by step.\"\n" ] } ] }, { "cell_type": "code", "source": [ "llm_chain=LLMChain(llm=llm,prompt=prompt)\n", "print(llm_chain.invoke(question))" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/" }, "id": "M4qAglDx5aFc", "outputId": "11a7a856-6c8d-4709-bd96-a29247365754" }, "execution_count": 13, "outputs": [ { "output_type": "stream", "name": "stdout", "text": [ "{'question': 'Who won the Cricket World Cup in the year 2011?', 'text': ' The Cricket World Cup is a global tournament held every four years. The year 2011 falls between the 1999 and 2015 World Cups. However, the World Cup held in 2011 was won by India. So, the answer is India.'}\n" ] } ] }, { "cell_type": "markdown", "source": [ "## HuggingFacePipeline\n", "Among transformers, the Pipeline is the most versatile tool in the Hugging Face toolbox. LangChain being designed primarily to address RAG and Agent use cases, the scope of the pipeline here is reduced to the following text-centric tasks: “text-generation\", “text2text-generation\", “summarization”, “translation”.\n", "Models can be loaded directly with the from_model_id method\n" ], "metadata": { "id": "mqS7TKWQEVge" } }, { "cell_type": "code", "source": [ "from langchain_huggingface import HuggingFacePipeline\n", "from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline" ], "metadata": { "id": "HEbmzjYKEZN0" }, "execution_count": 15, "outputs": [] }, { "cell_type": "code", "source": [ "model_id=\"gpt2\"\n", "model=AutoModelForCausalLM.from_pretrained(model_id)\n", "tokenizer=AutoTokenizer.from_pretrained(model_id)\n" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 241, "referenced_widgets": [ "c76ac71b127a48b7a1017fb35a7a502f", "6c258d8bc0ba4754a7ef37b1a5905303", "dd717a8865ce4c6e8be78585d35faf48", "3f5fd3e73e6a4c309c41eb83f8d8eb10", "dd55f82f8cdd404e8b9806326423d804", "d96d8520b94b4f6c9d5c16c5260523e5", "3d2834e2ca0245958ff6188b803a214a", "2521d8e2e26141b1a1b4f49b377dd0fa", "921d1715204d476eb420411052f745fe", "90c46448e67e493ea0fa04295086169e", "78849ab4cb614d94b6b1acd9b6de811b", "16ae56292a9247788a89c06654e3257f", "398564b7e000416993213ea0a4cb1b09", "b112fd130cda424e8d5084456bf9e804", "a88a4bb301db44d6939fc85fecfc1a6e", "8d2cebd7931a4db9b9dd1798116ee9cd", "fc0df028135d48c6bb1f24eaefb5917f", "0ce5c972dfc442248e542ec3ddd23ccb", "01334ff672ed4a42beb97fc3eb11c491", "6e9dc24c69c94341a0ec9d9cbc5d755c", "645339b43bb34560b1e63167538281bf", "a4783d15cbc7498f98dea69c28d6c140", "b4d98851b01940f7b3626f132dadbc52", "22dabf46def040009933cb74d2c40fbc", "a8a981c060e146d1b188818350e11d5f", "6f03945d76764bb786c8bfb1d83ecc90", "7214318f2c364ff884ebeb0bd917657e", "d12a46563ac14a018982c064ed32fd71", "f75c812da4954fd8850cab621f724dfd", "7dec52491ede4a50a35d6846ace97d08", "e4c5709456c0458a9ce7ef4ebb261fcc", "031781dfe6c4414f850e78cf1a64d791", "8a78cf32b3f044c2b7010cbefe8c0519", "c893f0cd18994b21911ce7164d4424ec", "5112abc2a39f44bba1e4aeedab9fd820", "43fade46d18d44028db3374026bd1b20", "43d0d27d08b94ed099f51ab8546b1d72", "84de8a6d6679458a871b37222e15b791", "98eadf1d8d7646fc8d3cea296556b350", "ff0ccdf2b1554bc39d7004491646f7cd", "b87d0f1a11d24cc4ab2c5d4aa33131e5", "5555a48dc3b64d8aad1f5968ef58ee37", "8df55b2bfd5442e588749f1f81a2eea0", "0a2ac15c6d3440b0a851619827541cf2", "41961baa10404665a8906087e54d8cbd", "6ea4939e5f2e458aa92b4bf8d298372a", "9c34e5f0b3df44eebc5903bd12ba191a", "74b8868cb8e64bc1bc640fcb13266c7b", "1f134ba4ef3740298fe3246ddf5c01bc", "958142653a194c6d97e08b10a83932c6", "e679f6a27f654e7ea620616caee02435", "2fd08621c1c74765bcd3d74c8f749dfe", "704b064e826d406ea679aeb51cd97016", "2d23caeaa6764d3f9fa4f513d662d7f1", "1247b17275304ab5904660aaced4dd30", "72a86841f4e940a3a1dc952b02370c32", "5b34d2277ef743cbbe8fd3ecad038e51", "a04ecad294384473ac87445b30df0499", "7d61e7114cca40dc87b2e4e303040720", "568afe16d9834fb0bd39bd9232f3c5c7", "faf4caf1ecb54997bc8000c68f962e30", "f7d1f35352ce45cf87f62539bf5bbb1a", "aad7683bf489472e88dcaa68d24bfe5a", "f48bea6bbe4e422693fd1074b61c86ee", "17a880f336d94dc4b71b1754d7ee90d4", "af902dcb69784dc3a4d3c624a78de130", "ef17696125d7405e81ff670d26d27252", "2d8f3d046eee40a7aff08149b9d9dd58", "5e514a7e465f408084efd318f56f2d48", "2fc27f94d4784c5eb50708ce904108ba", "d9da5297d2584a7f91f944aea655d3ee", "2c4c37c362a24cf0b0105a0249c041e0", "bfaedab81d5e4dfda60f20c5e33a745f", "1b45b211aea34d95aae7c62b7dbe72cb", "d05be79f0835403a9eb74d59b02bca89", "f26858dc74534b009c815ba6eea41e02", "23c137c762064d4eb8b8fbc006fe4208" ] }, "id": "TauCkxoT6hb9", "outputId": "b0fbb130-02b1-4206-f2b1-2e0eca36aa1e" }, "execution_count": 16, "outputs": [ { "output_type": "display_data", "data": { "text/plain": [ "config.json: 0%| | 0.00/665 [00:00)" ] }, "metadata": {}, "execution_count": 19 } ] }, { "cell_type": "code", "source": [ "hf.invoke(\"What is machine learning\")" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 53 }, "id": "t2OOJVj47aEU", "outputId": "ab71ea62-b6d7-4be0-b345-5e9b03c3ec32" }, "execution_count": 23, "outputs": [ { "output_type": "stream", "name": "stderr", "text": [ "Setting `pad_token_id` to `eos_token_id`:50256 for open-end generation.\n" ] }, { "output_type": "execute_result", "data": { "text/plain": [ "'What is machine learning, and what can we do about it?\"\\n\\nAnswering that question, COO Tim Miller says:'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 23 } ] }, { "cell_type": "code", "source": [ "## Use HuggingfacePipelines With Gpu\n", "gpu_llm = HuggingFacePipeline.from_model_id(\n", " model_id=\"gpt2\",\n", " task=\"text-generation\",\n", " device=0, # replace with device_map=\"auto\" to use the accelerate library.\n", " pipeline_kwargs={\"max_new_tokens\": 100},\n", ")" ], "metadata": { "id": "laZJsurT7hSb" }, "execution_count": 24, "outputs": [] }, { "cell_type": "code", "source": [ "from langchain_core.prompts import PromptTemplate\n", "\n", "template = \"\"\"Question: {question}\n", "\n", "Answer: Let's think step by step.\"\"\"\n", "prompt = PromptTemplate.from_template(template)" ], "metadata": { "id": "1-chtUFJ8KEI" }, "execution_count": 25, "outputs": [] }, { "cell_type": "code", "source": [ "chain=prompt|gpu_llm" ], "metadata": { "id": "DJO4KT9e8OcN" }, "execution_count": 26, "outputs": [] }, { "cell_type": "code", "source": [ "question=\"What is artificial intelligence?\"\n", "chain.invoke({\"question\":question})" ], "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "id": "Ogy0yag68Uho", "outputId": "be24ca0c-1488-4e4b-9522-8dc86d52d978" }, "execution_count": 28, "outputs": [ { "output_type": "execute_result", "data": { "text/plain": [ "'Question: What is artificial intelligence?\\n\\nAnswer: Let\\'s think step by step. AI is actually an interesting term we\\'re not used to.\\n\\nThe term is an example of \"deep learning\" that we use to describe a method we\\'ll be discussing, or a approach to use of artificial intelligence, in order to understand and refine human abilities and behaviors that we know we wish to achieve. For instance, human intelligence might be used to understand many complex problems and the processes of life that arise in a given situation.\\n\\nBecause of the complexity of human cognition, we must'" ], "application/vnd.google.colaboratory.intrinsic+json": { "type": "string" } }, "metadata": {}, "execution_count": 28 } ] }, { "cell_type": "code", "source": [], "metadata": { "id": "32inALS78hmK" }, "execution_count": null, "outputs": [] } ] }