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https://github.com/hwchase17/langchain
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b93638ef1e
# Your PR Title (What it does) <!-- Thank you for contributing to LangChain! Your PR will appear in our release under the title you set. Please make sure it highlights your valuable contribution. Replace this with a description of the change, the issue it fixes (if applicable), and relevant context. List any dependencies required for this change. After you're done, someone will review your PR. They may suggest improvements. If no one reviews your PR within a few days, feel free to @-mention the same people again, as notifications can get lost. Finally, we'd love to show appreciation for your contribution - if you'd like us to shout you out on Twitter, please also include your handle! --> <!-- Remove if not applicable --> Fixes # (issue) ## Before submitting <!-- If you're adding a new integration, please include: 1. a test for the integration - favor unit tests that does not rely on network access. 2. an example notebook showing its use See contribution guidelines for more information on how to write tests, lint etc: https://github.com/hwchase17/langchain/blob/master/.github/CONTRIBUTING.md --> ## Who can review? Community members can review the PR once tests pass. Tag maintainers/contributors who might be interested: <!-- For a quicker response, figure out the right person to tag with @ @hwchase17 - project lead Tracing / Callbacks - @agola11 Async - @agola11 DataLoaders - @eyurtsev Models - @hwchase17 - @agola11 Agents / Tools / Toolkits - @vowelparrot VectorStores / Retrievers / Memory - @dev2049 -->
282 lines
7.1 KiB
Plaintext
282 lines
7.1 KiB
Plaintext
{
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"cells": [
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "959300d4",
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"metadata": {},
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"source": [
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"# Hugging Face Hub\n",
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"\n",
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"The [Hugging Face Hub](https://huggingface.co/docs/hub/index) is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together.\n",
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"\n",
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"This example showcases how to connect to the Hugging Face Hub."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "4c1b8450-5eaf-4d34-8341-2d785448a1ff",
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"metadata": {
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"tags": []
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},
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"source": [
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"To use, you should have the ``huggingface_hub`` python [package installed](https://huggingface.co/docs/huggingface_hub/installation)."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d772b637-de00-4663-bd77-9bc96d798db2",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"!pip install huggingface_hub > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "d597a792-354c-4ca5-b483-5965eec5d63d",
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"metadata": {},
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"outputs": [],
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"source": [
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"# get a token: https://huggingface.co/docs/api-inference/quicktour#get-your-api-token\n",
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"\n",
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"from getpass import getpass\n",
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"\n",
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"HUGGINGFACEHUB_API_TOKEN = getpass()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b8c5b88c-e4b8-4d0d-9a35-6e8f106452c2",
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"os.environ[\"HUGGINGFACEHUB_API_TOKEN\"] = HUGGINGFACEHUB_API_TOKEN"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "84dd44c1-c428-41f3-a911-520281386c94",
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"metadata": {},
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"source": [
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"**Select a Model**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "39c7eeac-01c4-486b-9480-e828a9e73e78",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"google/flan-t5-xl\" # See https://huggingface.co/models?pipeline_tag=text-generation&sort=downloads for some other options\n",
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"\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "3acf0069",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import PromptTemplate, LLMChain\n",
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"\n",
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"\n",
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"question = \"Who won the FIFA World Cup in the year 1994? \"\n",
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"\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "ddaa06cf-95ec-48ce-b0ab-d892a7909693",
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"metadata": {},
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"source": [
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"## Examples\n",
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"\n",
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"Below are some examples of models you can access through the Hugging Face Hub integration."
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "4fa9337e-ccb5-4c52-9b7c-1653148bc256",
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"metadata": {},
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"source": [
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"### StableLM, by Stability AI\n",
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"\n",
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"See [Stability AI's](https://huggingface.co/stabilityai) organization page for a list of available models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "36a1ce01-bd46-451f-8ee6-61c8f4bd665a",
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"metadata": {},
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"outputs": [],
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"source": [
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"repo_id = \"stabilityai/stablelm-tuned-alpha-3b\"\n",
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"# Others include stabilityai/stablelm-base-alpha-3b\n",
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"# as well as 7B parameter versions"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b5654cea-60b0-4f40-ab34-06ba1eca810d",
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"metadata": {},
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"outputs": [],
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"source": [
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2f19d0dc-c987-433f-a8d6-b1214e8ee067",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "1a5c97af-89bc-4e59-95c1-223742a9160b",
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"metadata": {},
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"source": [
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"### Dolly, by Databricks\n",
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"\n",
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"See [Databricks](https://huggingface.co/databricks) organization page for a list of available models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "521fcd2b-8e38-4920-b407-5c7d330411c9",
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"databricks/dolly-v2-3b\"\n",
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"\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "9907ec3a-fe0c-4543-81c4-d42f9453f16c",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "03f6ae52-b5f9-4de6-832c-551cb3fa11ae",
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"metadata": {},
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"source": [
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"### Camel, by Writer\n",
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"\n",
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"See [Writer's](https://huggingface.co/Writer) organization page for a list of available models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "257a091d-750b-4910-ac08-fe1c7b3fd98b",
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import HuggingFaceHub\n",
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"\n",
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"repo_id = \"Writer/camel-5b-hf\" # See https://huggingface.co/Writer for other options\n",
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"llm = HuggingFaceHub(repo_id=repo_id, model_kwargs={\"temperature\":0, \"max_length\":64})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b06f6838-a11a-4d6a-88e3-91fa1747a2b3",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Reuse the prompt and question from above.\n",
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"llm_chain = LLMChain(prompt=prompt, llm=llm)\n",
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"print(llm_chain.run(question))"
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]
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},
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{
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"attachments": {},
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"cell_type": "markdown",
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"id": "2bf838eb-1083-402f-b099-b07c452418c8",
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"metadata": {},
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"source": [
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"**And many more!**"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "18c78880-65d7-41d0-9722-18090efb60e9",
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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