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{
"cells": [
{
"cell_type": "markdown",
"id": "a98030af-fcd1-4d63-a36e-38ba053498fa",
"metadata": {},
"source": [
"# A full business solution\n",
"\n",
"## Now we will take our project from Day 1 to the next level\n",
"\n",
"### BUSINESS CHALLENGE:\n",
"\n",
"Create a product that builds a Brochure for a company to be used for prospective clients, investors and potential recruits.\n",
"\n",
"We will be provided a company name and their primary website.\n",
"\n",
"See the end of this notebook for examples of real-world business applications.\n",
"\n",
"And remember: I'm always available if you have problems or ideas! Please do reach out."
]
},
{
"cell_type": "code",
"execution_count": 1,
"id": "d5b08506-dc8b-4443-9201-5f1848161363",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"# If these fail, please check you're running from an 'activated' environment with (llms) in the command prompt\n",
"\n",
"import os\n",
"import requests\n",
"import json\n",
"from typing import List\n",
"from dotenv import load_dotenv\n",
"from bs4 import BeautifulSoup\n",
"from IPython.display import Markdown, display, update_display\n",
"from openai import OpenAI"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "fc5d8880-f2ee-4c06-af16-ecbc0262af61",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"API key looks good so far\n"
]
}
],
"source": [
"# Initialize and constants\n",
"\n",
"load_dotenv()\n",
"api_key = os.getenv('OPENAI_API_KEY')\n",
"\n",
"if api_key and api_key.startswith('sk-proj-') and len(api_key)>10:\n",
" print(\"API key looks good so far\")\n",
"else:\n",
" print(\"There might be a problem with your API key? Please visit the troubleshooting notebook!\")\n",
" \n",
"MODEL = 'gpt-4o-mini'\n",
"openai = OpenAI()"
]
},
{
"cell_type": "code",
"execution_count": 3,
"id": "106dd65e-90af-4ca8-86b6-23a41840645b",
"metadata": {},
"outputs": [],
"source": [
"# A class to represent a Webpage\n",
"\n",
"# Some websites need you to use proper headers when fetching them:\n",
"headers = {\n",
" \"User-Agent\": \"Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/117.0.0.0 Safari/537.36\"\n",
"}\n",
"\n",
"class Website:\n",
" \"\"\"\n",
" A utility class to represent a Website that we have scraped, now with links\n",
" \"\"\"\n",
"\n",
" def __init__(self, url):\n",
" self.url = url\n",
" response = requests.get(url, headers=headers)\n",
" self.body = response.content\n",
" soup = BeautifulSoup(self.body, 'html.parser')\n",
" self.title = soup.title.string if soup.title else \"No title found\"\n",
" if soup.body:\n",
" for irrelevant in soup.body([\"script\", \"style\", \"img\", \"input\"]):\n",
" irrelevant.decompose()\n",
" self.text = soup.body.get_text(separator=\"\\n\", strip=True)\n",
" else:\n",
" self.text = \"\"\n",
" links = [link.get('href') for link in soup.find_all('a')]\n",
" self.links = [link for link in links if link]\n",
"\n",
" def get_contents(self):\n",
" return f\"Webpage Title:\\n{self.title}\\nWebpage Contents:\\n{self.text}\\n\\n\""
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "e30d8128-933b-44cc-81c8-ab4c9d86589a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['https://edwarddonner.com/',\n",
" 'https://edwarddonner.com/outsmart/',\n",
" 'https://edwarddonner.com/about-me-and-about-nebula/',\n",
" 'https://edwarddonner.com/posts/',\n",
" 'https://edwarddonner.com/',\n",
" 'https://news.ycombinator.com',\n",
" 'https://nebula.io/?utm_source=ed&utm_medium=referral',\n",
" 'https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html',\n",
" 'https://patents.google.com/patent/US20210049536A1/',\n",
" 'https://www.linkedin.com/in/eddonner/',\n",
" 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n",
" 'https://edwarddonner.com/2024/11/13/llm-engineering-resources/',\n",
" 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n",
" 'https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/',\n",
" 'https://edwarddonner.com/2024/08/06/outsmart/',\n",
" 'https://edwarddonner.com/2024/08/06/outsmart/',\n",
" 'https://edwarddonner.com/2024/06/26/choosing-the-right-llm-resources/',\n",
" 'https://edwarddonner.com/2024/06/26/choosing-the-right-llm-resources/',\n",
" 'https://edwarddonner.com/',\n",
" 'https://edwarddonner.com/outsmart/',\n",
" 'https://edwarddonner.com/about-me-and-about-nebula/',\n",
" 'https://edwarddonner.com/posts/',\n",
" 'mailto:hello@mygroovydomain.com',\n",
" 'https://www.linkedin.com/in/eddonner/',\n",
" 'https://twitter.com/edwarddonner',\n",
" 'https://www.facebook.com/edward.donner.52']"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ed = Website(\"https://edwarddonner.com\")\n",
"ed.links"
]
},
{
"cell_type": "markdown",
"id": "1771af9c-717a-4fca-bbbe-8a95893312c3",
"metadata": {},
"source": [
"## First step: Have GPT-4o-mini figure out which links are relevant\n",
"\n",
"### Use a call to gpt-4o-mini to read the links on a webpage, and respond in structured JSON. \n",
"It should decide which links are relevant, and replace relative links such as \"/about\" with \"https://company.com/about\". \n",
"We will use \"one shot prompting\" in which we provide an example of how it should respond in the prompt.\n",
"\n",
"This is an excellent use case for an LLM, because it requires nuanced understanding. Imagine trying to code this without LLMs by parsing and analyzing the webpage - it would be very hard!\n",
"\n",
"Sidenote: there is a more advanced technique called \"Structured Outputs\" in which we require the model to respond according to a spec. We cover this technique in Week 8 during our autonomous Agentic AI project."
]
},
{
"cell_type": "code",
"execution_count": 40,
"id": "6957b079-0d96-45f7-a26a-3487510e9b35",
"metadata": {},
"outputs": [],
"source": [
"link_system_prompt = \"You are provided with a list of links found on a webpage. \\\n",
"You are able to decide which of the links would be most relevant to include in a brochure about the company, \\\n",
"such as links to an About page, or a Company page, or Careers/Jobs pages.\\n\"\n",
"link_system_prompt += \"You should respond in JSON as in this example:\"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"\"\"\"\n",
"link_system_prompt += \"here is additionnal example: \"\n",
"link_system_prompt += \"\"\"\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\", was_a_relative_path: true},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\", was_a_relative_path: false}\n",
" ]\n",
"}\n",
"\"\"\""
]
},
{
"cell_type": "code",
"execution_count": 41,
"id": "b97e4068-97ed-4120-beae-c42105e4d59a",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You are provided with a list of links found on a webpage. You are able to decide which of the links would be most relevant to include in a brochure about the company, such as links to an About page, or a Company page, or Careers/Jobs pages.\n",
"You should respond in JSON as in this example:\n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\"},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\"}\n",
" ]\n",
"}\n",
"here is additionnal example: \n",
"{\n",
" \"links\": [\n",
" {\"type\": \"about page\", \"url\": \"https://full.url/goes/here/about\", was_a_relative_path: true},\n",
" {\"type\": \"careers page\": \"url\": \"https://another.full.url/careers\", was_a_relative_path: false}\n",
" ]\n",
"}\n",
"\n"
]
}
],
"source": [
"print(link_system_prompt)"
]
},
{
"cell_type": "code",
"execution_count": 49,
"id": "8e1f601b-2eaf-499d-b6b8-c99050c9d6b3",
"metadata": {},
"outputs": [],
"source": [
"def get_links_user_prompt(website):\n",
" user_prompt = f\"Here is the list of links on the website of {website.url} - \"\n",
" user_prompt += \"please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. \\\n",
"Do not include Terms of Service, Privacy, email links.\\n\"\n",
" user_prompt += \"Links (some might be relative links):\\n\"\n",
" user_prompt += \"if a link was a relative links, in the json indicate it to true otherwize don't don't add that key:\\n\"\n",
" user_prompt += \"\\n\".join(website.links)\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 44,
"id": "6bcbfa78-6395-4685-b92c-22d592050fd7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Here is the list of links on the website of https://edwarddonner.com - please decide which of these are relevant web links for a brochure about the company, respond with the full https URL in JSON format. Do not include Terms of Service, Privacy, email links.\n",
"Links (some might be relative links):\n",
"if a link was a relative links, in the json indicate it to true. and do not add it if not:\n",
"https://edwarddonner.com/\n",
"https://edwarddonner.com/outsmart/\n",
"https://edwarddonner.com/about-me-and-about-nebula/\n",
"https://edwarddonner.com/posts/\n",
"https://edwarddonner.com/\n",
"https://news.ycombinator.com\n",
"https://nebula.io/?utm_source=ed&utm_medium=referral\n",
"https://www.prnewswire.com/news-releases/wynden-stark-group-acquires-nyc-venture-backed-tech-startup-untapt-301269512.html\n",
"https://patents.google.com/patent/US20210049536A1/\n",
"https://www.linkedin.com/in/eddonner/\n",
"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n",
"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\n",
"https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n",
"https://edwarddonner.com/2024/10/16/from-software-engineer-to-ai-data-scientist-resources/\n",
"https://edwarddonner.com/2024/08/06/outsmart/\n",
"https://edwarddonner.com/2024/08/06/outsmart/\n",
"https://edwarddonner.com/2024/06/26/choosing-the-right-llm-resources/\n",
"https://edwarddonner.com/2024/06/26/choosing-the-right-llm-resources/\n",
"https://edwarddonner.com/\n",
"https://edwarddonner.com/outsmart/\n",
"https://edwarddonner.com/about-me-and-about-nebula/\n",
"https://edwarddonner.com/posts/\n",
"mailto:hello@mygroovydomain.com\n",
"https://www.linkedin.com/in/eddonner/\n",
"https://twitter.com/edwarddonner\n",
"https://www.facebook.com/edward.donner.52\n"
]
}
],
"source": [
"print(get_links_user_prompt(ed))"
]
},
{
"cell_type": "code",
"execution_count": 59,
"id": "a29aca19-ca13-471c-a4b4-5abbfa813f69",
"metadata": {},
"outputs": [],
"source": [
"def get_links(url):\n",
" website = Website(url)\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": link_system_prompt},\n",
" {\"role\": \"user\", \"content\": get_links_user_prompt(website)}\n",
" ],\n",
" response_format={\"type\": \"json_object\"}\n",
" )\n",
" result = response.choices[0].message.content\n",
" return json.loads(result)"
]
},
{
"cell_type": "code",
"execution_count": 57,
"id": "74a827a0-2782-4ae5-b210-4a242a8b4cc2",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"['/',\n",
" '/models',\n",
" '/datasets',\n",
" '/spaces',\n",
" '/posts',\n",
" '/docs',\n",
" '/enterprise',\n",
" '/pricing',\n",
" '/login',\n",
" '/join',\n",
" '/meta-llama/Llama-3.3-70B-Instruct',\n",
" '/tencent/HunyuanVideo',\n",
" '/Datou1111/shou_xin',\n",
" '/black-forest-labs/FLUX.1-dev',\n",
" '/deepseek-ai/DeepSeek-V2.5-1210',\n",
" '/models',\n",
" '/spaces/JeffreyXiang/TRELLIS',\n",
" '/spaces/multimodalart/flux-style-shaping',\n",
" '/spaces/ginipick/FLUXllama',\n",
" '/spaces/Kwai-Kolors/Kolors-Virtual-Try-On',\n",
" '/spaces/black-forest-labs/FLUX.1-dev',\n",
" '/spaces',\n",
" '/datasets/HuggingFaceFW/fineweb-2',\n",
" '/datasets/fka/awesome-chatgpt-prompts',\n",
" '/datasets/CohereForAI/Global-MMLU',\n",
" '/datasets/O1-OPEN/OpenO1-SFT',\n",
" '/datasets/amphora/QwQ-LongCoT-130K',\n",
" '/datasets',\n",
" '/join',\n",
" '/pricing#endpoints',\n",
" '/pricing#spaces',\n",
" '/pricing',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/enterprise',\n",
" '/allenai',\n",
" '/facebook',\n",
" '/amazon',\n",
" '/google',\n",
" '/Intel',\n",
" '/microsoft',\n",
" '/grammarly',\n",
" '/Writer',\n",
" '/docs/transformers',\n",
" '/docs/diffusers',\n",
" '/docs/safetensors',\n",
" '/docs/huggingface_hub',\n",
" '/docs/tokenizers',\n",
" '/docs/peft',\n",
" '/docs/transformers.js',\n",
" '/docs/timm',\n",
" '/docs/trl',\n",
" '/docs/datasets',\n",
" '/docs/text-generation-inference',\n",
" '/docs/accelerate',\n",
" '/models',\n",
" '/datasets',\n",
" '/spaces',\n",
" '/tasks',\n",
" 'https://ui.endpoints.huggingface.co',\n",
" '/chat',\n",
" '/huggingface',\n",
" '/brand',\n",
" '/terms-of-service',\n",
" '/privacy',\n",
" 'https://apply.workable.com/huggingface/',\n",
" 'mailto:press@huggingface.co',\n",
" '/learn',\n",
" '/docs',\n",
" '/blog',\n",
" 'https://discuss.huggingface.co',\n",
" 'https://status.huggingface.co/',\n",
" 'https://github.com/huggingface',\n",
" 'https://twitter.com/huggingface',\n",
" 'https://www.linkedin.com/company/huggingface/',\n",
" '/join/discord']"
]
},
"execution_count": 57,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Anthropic has made their site harder to scrape, so I'm using HuggingFace..\n",
"\n",
"huggingface = Website(\"https://huggingface.co\")\n",
"huggingface.links"
]
},
{
"cell_type": "code",
"execution_count": 60,
"id": "d3d583e2-dcc4-40cc-9b28-1e8dbf402924",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'links': [{'type': 'about page',\n",
" 'url': 'https://huggingface.co/huggingface',\n",
" 'was_a_relative_path': True},\n",
" {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'},\n",
" {'type': 'enterprise page',\n",
" 'url': 'https://huggingface.co/enterprise',\n",
" 'was_a_relative_path': True},\n",
" {'type': 'pricing page',\n",
" 'url': 'https://huggingface.co/pricing',\n",
" 'was_a_relative_path': True},\n",
" {'type': 'blog',\n",
" 'url': 'https://huggingface.co/blog',\n",
" 'was_a_relative_path': True},\n",
" {'type': 'community page', 'url': 'https://discuss.huggingface.co'},\n",
" {'type': 'GitHub page', 'url': 'https://github.com/huggingface'},\n",
" {'type': 'Twitter page', 'url': 'https://twitter.com/huggingface'},\n",
" {'type': 'LinkedIn page',\n",
" 'url': 'https://www.linkedin.com/company/huggingface/'}]}"
]
},
"execution_count": 60,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_links(\"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "0d74128e-dfb6-47ec-9549-288b621c838c",
"metadata": {},
"source": [
"## Second step: make the brochure!\n",
"\n",
"Assemble all the details into another prompt to GPT4-o"
]
},
{
"cell_type": "code",
"execution_count": 20,
"id": "85a5b6e2-e7ef-44a9-bc7f-59ede71037b5",
"metadata": {},
"outputs": [],
"source": [
"def get_all_details(url):\n",
" result = \"Landing page:\\n\"\n",
" result += Website(url).get_contents()\n",
" links = get_links(url)\n",
" print(\"Found links::\", links)\n",
" for link in links[\"links\"]:\n",
" result += f\"\\n\\n{link['type']}\\n\"\n",
" result += Website(link[\"url\"]).get_contents()\n",
" return result"
]
},
{
"cell_type": "code",
"execution_count": 21,
"id": "5099bd14-076d-4745-baf3-dac08d8e5ab2",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found links:: {'links': [{'type': 'home page', 'url': 'https://huggingface.co/'}, {'type': 'about page', 'url': 'https://huggingface.co/huggingface'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'blog', 'url': 'https://huggingface.co/blog'}, {'type': 'community page', 'url': 'https://discuss.huggingface.co'}, {'type': 'GitHub page', 'url': 'https://github.com/huggingface'}, {'type': 'Twitter page', 'url': 'https://twitter.com/huggingface'}, {'type': 'LinkedIn page', 'url': 'https://www.linkedin.com/company/huggingface/'}]}\n",
"Landing page:\n",
"Webpage Title:\n",
"Hugging Face – The AI community building the future.\n",
"Webpage Contents:\n",
"Hugging Face\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Posts\n",
"Docs\n",
"Enterprise\n",
"Pricing\n",
"Log In\n",
"Sign Up\n",
"The AI community building the future.\n",
"The platform where the machine learning community collaborates on models, datasets, and applications.\n",
"Trending on\n",
"this week\n",
"Models\n",
"meta-llama/Llama-3.3-70B-Instruct\n",
"Updated\n",
"3 days ago\n",
"•\n",
"102k\n",
"•\n",
"960\n",
"tencent/HunyuanVideo\n",
"Updated\n",
"6 days ago\n",
"•\n",
"3.73k\n",
"•\n",
"992\n",
"Datou1111/shou_xin\n",
"Updated\n",
"5 days ago\n",
"•\n",
"7.84k\n",
"•\n",
"322\n",
"black-forest-labs/FLUX.1-dev\n",
"Updated\n",
"Aug 16\n",
"•\n",
"1.38M\n",
"•\n",
"7.23k\n",
"Qwen/QwQ-32B-Preview\n",
"Updated\n",
"15 days ago\n",
"•\n",
"92.8k\n",
"•\n",
"1.27k\n",
"Browse 400k+ models\n",
"Spaces\n",
"Running\n",
"on\n",
"Zero\n",
"1.13k\n",
"🏢\n",
"TRELLIS\n",
"Scalable and Versatile 3D Generation from images\n",
"Running\n",
"on\n",
"Zero\n",
"291\n",
"🦀🏆\n",
"FLUXllama\n",
"FLUX 4-bit Quantization(just 8GB VRAM)\n",
"Running\n",
"on\n",
"L40S\n",
"244\n",
"🚀\n",
"Flux Style Shaping\n",
"Optical illusions and style transfer with FLUX\n",
"Running\n",
"on\n",
"CPU Upgrade\n",
"5.92k\n",
"👕\n",
"Kolors Virtual Try-On\n",
"Running\n",
"on\n",
"Zero\n",
"5.71k\n",
"🖥\n",
"FLUX.1 [dev]\n",
"Browse 150k+ applications\n",
"Datasets\n",
"HuggingFaceFW/fineweb-2\n",
"Updated\n",
"5 days ago\n",
"•\n",
"27.6k\n",
"•\n",
"284\n",
"fka/awesome-chatgpt-prompts\n",
"Updated\n",
"Sep 3\n",
"•\n",
"7.71k\n",
"•\n",
"6.52k\n",
"CohereForAI/Global-MMLU\n",
"Updated\n",
"1 day ago\n",
"•\n",
"4.59k\n",
"•\n",
"77\n",
"O1-OPEN/OpenO1-SFT\n",
"Updated\n",
"22 days ago\n",
"•\n",
"1.34k\n",
"•\n",
"175\n",
"amphora/QwQ-LongCoT-130K\n",
"Updated\n",
"8 days ago\n",
"•\n",
"536\n",
"•\n",
"46\n",
"Browse 100k+ datasets\n",
"The Home of Machine Learning\n",
"Create, discover and collaborate on ML better.\n",
"The collaboration platform\n",
"Host and collaborate on unlimited public models, datasets and applications.\n",
"Move faster\n",
"With the HF Open source stack.\n",
"Explore all modalities\n",
"Text, image, video, audio or even 3D.\n",
"Build your portfolio\n",
"Share your work with the world and build your ML profile.\n",
"Sign Up\n",
"Accelerate your ML\n",
"We provide paid Compute and Enterprise solutions.\n",
"Compute\n",
"Deploy on optimized\n",
"Inference Endpoints\n",
"or update your\n",
"Spaces applications\n",
"to a GPU in a few clicks.\n",
"View pricing\n",
"Starting at $0.60/hour for GPU\n",
"Enterprise\n",
"Give your team the most advanced platform to build AI with enterprise-grade security, access controls and\n",
"\t\t\tdedicated support.\n",
"Getting started\n",
"Starting at $20/user/month\n",
"Single Sign-On\n",
"Regions\n",
"Priority Support\n",
"Audit Logs\n",
"Resource Groups\n",
"Private Datasets Viewer\n",
"More than 50,000 organizations are using Hugging Face\n",
"Ai2\n",
"Enterprise\n",
"non-profit\n",
"•\n",
"361 models\n",
"•\n",
"1.71k followers\n",
"AI at Meta\n",
"Enterprise\n",
"company\n",
"•\n",
"2.05k models\n",
"•\n",
"3.75k followers\n",
"Amazon Web Services\n",
"company\n",
"•\n",
"21 models\n",
"•\n",
"2.42k followers\n",
"Google\n",
"company\n",
"•\n",
"910 models\n",
"•\n",
"5.46k followers\n",
"Intel\n",
"company\n",
"•\n",
"217 models\n",
"•\n",
"2.05k followers\n",
"Microsoft\n",
"company\n",
"•\n",
"351 models\n",
"•\n",
"6.06k followers\n",
"Grammarly\n",
"company\n",
"•\n",
"10 models\n",
"•\n",
"98 followers\n",
"Writer\n",
"Enterprise\n",
"company\n",
"•\n",
"16 models\n",
"•\n",
"178 followers\n",
"Our Open Source\n",
"We are building the foundation of ML tooling with the community.\n",
"Transformers\n",
"136,246\n",
"State-of-the-art ML for Pytorch, TensorFlow, and JAX.\n",
"Diffusers\n",
"26,624\n",
"State-of-the-art diffusion models for image and audio generation in PyTorch.\n",
"Safetensors\n",
"2,953\n",
"Simple, safe way to store and distribute neural networks weights safely and quickly.\n",
"Hub Python Library\n",
"2,165\n",
"Client library for the HF Hub: manage repositories from your Python runtime.\n",
"Tokenizers\n",
"9,150\n",
"Fast tokenizers, optimized for both research and production.\n",
"PEFT\n",
"16,699\n",
"Parameter efficient finetuning methods for large models.\n",
"Transformers.js\n",
"12,337\n",
"State-of-the-art Machine Learning for the web. Run Transformers directly in your browser, with no need for a server.\n",
"timm\n",
"32,592\n",
"State-of-the-art computer vision models, layers, optimizers, training/evaluation, and utilities.\n",
"TRL\n",
"10,308\n",
"Train transformer language models with reinforcement learning.\n",
"Datasets\n",
"19,349\n",
"Access and share datasets for computer vision, audio, and NLP tasks.\n",
"Text Generation Inference\n",
"9,433\n",
"Toolkit to serve Large Language Models.\n",
"Accelerate\n",
"8,053\n",
"Easily train and use PyTorch models with multi-GPU, TPU, mixed-precision.\n",
"Website\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Tasks\n",
"Inference Endpoints\n",
"HuggingChat\n",
"Company\n",
"About\n",
"Brand assets\n",
"Terms of service\n",
"Privacy\n",
"Jobs\n",
"Press\n",
"Resources\n",
"Learn\n",
"Documentation\n",
"Blog\n",
"Forum\n",
"Service Status\n",
"Social\n",
"GitHub\n",
"Twitter\n",
"LinkedIn\n",
"Discord\n",
"\n",
"\n",
"\n",
"home page\n",
"Webpage Title:\n",
"Hugging Face – The AI community building the future.\n",
"Webpage Contents:\n",
"Hugging Face\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Posts\n",
"Docs\n",
"Enterprise\n",
"Pricing\n",
"Log In\n",
"Sign Up\n",
"The AI community building the future.\n",
"The platform where the machine learning community collaborates on models, datasets, and applications.\n",
"Trending on\n",
"this week\n",
"Models\n",
"meta-llama/Llama-3.3-70B-Instruct\n",
"Updated\n",
"3 days ago\n",
"•\n",
"102k\n",
"•\n",
"960\n",
"tencent/HunyuanVideo\n",
"Updated\n",
"6 days ago\n",
"•\n",
"3.73k\n",
"•\n",
"992\n",
"Datou1111/shou_xin\n",
"Updated\n",
"5 days ago\n",
"•\n",
"7.84k\n",
"•\n",
"322\n",
"black-forest-labs/FLUX.1-dev\n",
"Updated\n",
"Aug 16\n",
"•\n",
"1.38M\n",
"•\n",
"7.23k\n",
"Qwen/QwQ-32B-Preview\n",
"Updated\n",
"15 days ago\n",
"•\n",
"92.8k\n",
"•\n",
"1.27k\n",
"Browse 400k+ models\n",
"Spaces\n",
"Running\n",
"on\n",
"Zero\n",
"1.13k\n",
"🏢\n",
"TRELLIS\n",
"Scalable and Versatile 3D Generation from images\n",
"Running\n",
"on\n",
"Zero\n",
"291\n",
"🦀🏆\n",
"FLUXllama\n",
"FLUX 4-bit Quantization(just 8GB VRAM)\n",
"Running\n",
"on\n",
"L40S\n",
"244\n",
"🚀\n",
"Flux Style Shaping\n",
"Optical illusions and style transfer with FLUX\n",
"Running\n",
"on\n",
"CPU Upgrade\n",
"5.92k\n",
"👕\n",
"Kolors Virtual Try-On\n",
"Running\n",
"on\n",
"Zero\n",
"5.71k\n",
"🖥\n",
"FLUX.1 [dev]\n",
"Browse 150k+ applications\n",
"Datasets\n",
"HuggingFaceFW/fineweb-2\n",
"Updated\n",
"5 days ago\n",
"•\n",
"27.6k\n",
"•\n",
"284\n",
"fka/awesome-chatgpt-prompts\n",
"Updated\n",
"Sep 3\n",
"•\n",
"7.71k\n",
"•\n",
"6.52k\n",
"CohereForAI/Global-MMLU\n",
"Updated\n",
"1 day ago\n",
"•\n",
"4.59k\n",
"•\n",
"77\n",
"O1-OPEN/OpenO1-SFT\n",
"Updated\n",
"22 days ago\n",
"•\n",
"1.34k\n",
"•\n",
"175\n",
"amphora/QwQ-LongCoT-130K\n",
"Updated\n",
"8 days ago\n",
"•\n",
"536\n",
"•\n",
"46\n",
"Browse 100k+ datasets\n",
"The Home of Machine Learning\n",
"Create, discover and collaborate on ML better.\n",
"The collaboration platform\n",
"Host and collaborate on unlimited public models, datasets and applications.\n",
"Move faster\n",
"With the HF Open source stack.\n",
"Explore all modalities\n",
"Text, image, video, audio or even 3D.\n",
"Build your portfolio\n",
"Share your work with the world and build your ML profile.\n",
"Sign Up\n",
"Accelerate your ML\n",
"We provide paid Compute and Enterprise solutions.\n",
"Compute\n",
"Deploy on optimized\n",
"Inference Endpoints\n",
"or update your\n",
"Spaces applications\n",
"to a GPU in a few clicks.\n",
"View pricing\n",
"Starting at $0.60/hour for GPU\n",
"Enterprise\n",
"Give your team the most advanced platform to build AI with enterprise-grade security, access controls and\n",
"\t\t\tdedicated support.\n",
"Getting started\n",
"Starting at $20/user/month\n",
"Single Sign-On\n",
"Regions\n",
"Priority Support\n",
"Audit Logs\n",
"Resource Groups\n",
"Private Datasets Viewer\n",
"More than 50,000 organizations are using Hugging Face\n",
"Ai2\n",
"Enterprise\n",
"non-profit\n",
"•\n",
"361 models\n",
"•\n",
"1.71k followers\n",
"AI at Meta\n",
"Enterprise\n",
"company\n",
"•\n",
"2.05k models\n",
"•\n",
"3.75k followers\n",
"Amazon Web Services\n",
"company\n",
"•\n",
"21 models\n",
"•\n",
"2.42k followers\n",
"Google\n",
"company\n",
"•\n",
"910 models\n",
"•\n",
"5.46k followers\n",
"Intel\n",
"company\n",
"•\n",
"217 models\n",
"•\n",
"2.05k followers\n",
"Microsoft\n",
"company\n",
"•\n",
"351 models\n",
"•\n",
"6.06k followers\n",
"Grammarly\n",
"company\n",
"•\n",
"10 models\n",
"•\n",
"98 followers\n",
"Writer\n",
"Enterprise\n",
"company\n",
"•\n",
"16 models\n",
"•\n",
"178 followers\n",
"Our Open Source\n",
"We are building the foundation of ML tooling with the community.\n",
"Transformers\n",
"136,246\n",
"State-of-the-art ML for Pytorch, TensorFlow, and JAX.\n",
"Diffusers\n",
"26,624\n",
"State-of-the-art diffusion models for image and audio generation in PyTorch.\n",
"Safetensors\n",
"2,953\n",
"Simple, safe way to store and distribute neural networks weights safely and quickly.\n",
"Hub Python Library\n",
"2,165\n",
"Client library for the HF Hub: manage repositories from your Python runtime.\n",
"Tokenizers\n",
"9,150\n",
"Fast tokenizers, optimized for both research and production.\n",
"PEFT\n",
"16,699\n",
"Parameter efficient finetuning methods for large models.\n",
"Transformers.js\n",
"12,337\n",
"State-of-the-art Machine Learning for the web. Run Transformers directly in your browser, with no need for a server.\n",
"timm\n",
"32,592\n",
"State-of-the-art computer vision models, layers, optimizers, training/evaluation, and utilities.\n",
"TRL\n",
"10,308\n",
"Train transformer language models with reinforcement learning.\n",
"Datasets\n",
"19,349\n",
"Access and share datasets for computer vision, audio, and NLP tasks.\n",
"Text Generation Inference\n",
"9,433\n",
"Toolkit to serve Large Language Models.\n",
"Accelerate\n",
"8,053\n",
"Easily train and use PyTorch models with multi-GPU, TPU, mixed-precision.\n",
"Website\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Tasks\n",
"Inference Endpoints\n",
"HuggingChat\n",
"Company\n",
"About\n",
"Brand assets\n",
"Terms of service\n",
"Privacy\n",
"Jobs\n",
"Press\n",
"Resources\n",
"Learn\n",
"Documentation\n",
"Blog\n",
"Forum\n",
"Service Status\n",
"Social\n",
"GitHub\n",
"Twitter\n",
"LinkedIn\n",
"Discord\n",
"\n",
"\n",
"\n",
"about page\n",
"Webpage Title:\n",
"huggingface (Hugging Face)\n",
"Webpage Contents:\n",
"Hugging Face\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Posts\n",
"Docs\n",
"Enterprise\n",
"Pricing\n",
"Log In\n",
"Sign Up\n",
"Hugging Face\n",
"Enterprise\n",
"company\n",
"Verified\n",
"https://huggingface.co\n",
"huggingface\n",
"huggingface\n",
"Follow\n",
"7,478\n",
"AI & ML interests\n",
"The AI community building the future.\n",
"Team members\n",
"224\n",
"+190\n",
"+177\n",
"+156\n",
"+146\n",
"+126\n",
"Organization Card\n",
"Community\n",
"About org cards\n",
"👋 Hi!\n",
"We are on a mission to democratize\n",
"good\n",
"machine learning, one commit at a time.\n",
"If that sounds like something you should be doing, why don't you\n",
"join us\n",
"!\n",
"For press enquiries, you can\n",
"✉ contact our team here\n",
".\n",
"Collections\n",
"1\n",
"DistilBERT release\n",
"Original DistilBERT model, checkpoints obtained from using teacher-student learning from the original BERT checkpoints.\n",
"distilbert/distilbert-base-cased\n",
"Fill-Mask\n",
"•\n",
"Updated\n",
"May 6\n",
"•\n",
"402k\n",
"•\n",
"35\n",
"distilbert/distilbert-base-uncased\n",
"Fill-Mask\n",
"•\n",
"Updated\n",
"May 6\n",
"•\n",
"15.6M\n",
"•\n",
"•\n",
"570\n",
"distilbert/distilbert-base-multilingual-cased\n",
"Fill-Mask\n",
"•\n",
"Updated\n",
"May 6\n",
"•\n",
"513k\n",
"•\n",
"147\n",
"distilbert/distilbert-base-uncased-finetuned-sst-2-english\n",
"Text Classification\n",
"•\n",
"Updated\n",
"Dec 19, 2023\n",
"•\n",
"9.7M\n",
"•\n",
"•\n",
"642\n",
"spaces\n",
"23\n",
"Sort: \n",
"\t\tRecently updated\n",
"Build error\n",
"194\n",
"⚡\n",
"paper-central\n",
"Running\n",
"296\n",
"😻\n",
"Open Source Ai Year In Review 2024\n",
"What happened in open-source AI this year, and what’s next?\n",
"Running\n",
"42\n",
"🔋\n",
"Inference Playground\n",
"Running\n",
"19\n",
"🏢\n",
"Number Tokenization Blog\n",
"Running\n",
"on\n",
"TPU v5e\n",
"5\n",
"💬\n",
"Keras Chatbot Battle\n",
"Running\n",
"101\n",
"⚡\n",
"Modelcard Creator\n",
"Expand 23\n",
"\t\t\t\t\t\t\tspaces\n",
"models\n",
"16\n",
"Sort: \n",
"\t\tRecently updated\n",
"huggingface/timesfm-tourism-monthly\n",
"Updated\n",
"4 days ago\n",
"•\n",
"23\n",
"huggingface/CodeBERTa-language-id\n",
"Text Classification\n",
"•\n",
"Updated\n",
"Mar 29\n",
"•\n",
"513\n",
"•\n",
"54\n",
"huggingface/falcon-40b-gptq\n",
"Text Generation\n",
"•\n",
"Updated\n",
"Jun 14, 2023\n",
"•\n",
"13\n",
"•\n",
"12\n",
"huggingface/autoformer-tourism-monthly\n",
"Updated\n",
"May 24, 2023\n",
"•\n",
"1.82k\n",
"•\n",
"9\n",
"huggingface/distilbert-base-uncased-finetuned-mnli\n",
"Text Classification\n",
"•\n",
"Updated\n",
"Mar 22, 2023\n",
"•\n",
"1.8k\n",
"•\n",
"2\n",
"huggingface/informer-tourism-monthly\n",
"Updated\n",
"Feb 24, 2023\n",
"•\n",
"1.25k\n",
"•\n",
"5\n",
"huggingface/time-series-transformer-tourism-monthly\n",
"Updated\n",
"Feb 23, 2023\n",
"•\n",
"2.22k\n",
"•\n",
"18\n",
"huggingface/the-no-branch-repo\n",
"Text-to-Image\n",
"•\n",
"Updated\n",
"Feb 10, 2023\n",
"•\n",
"9\n",
"•\n",
"3\n",
"huggingface/CodeBERTa-small-v1\n",
"Fill-Mask\n",
"•\n",
"Updated\n",
"Jun 27, 2022\n",
"•\n",
"39.9k\n",
"•\n",
"71\n",
"huggingface/test-model-repo\n",
"Updated\n",
"Nov 19, 2021\n",
"•\n",
"1\n",
"Expand 16\n",
"\t\t\t\t\t\t\tmodels\n",
"datasets\n",
"31\n",
"Sort: \n",
"\t\tRecently updated\n",
"huggingface/paper-central-data\n",
"Viewer\n",
"•\n",
"Updated\n",
"about 2 hours ago\n",
"•\n",
"113k\n",
"•\n",
"479\n",
"•\n",
"7\n",
"huggingface/documentation-images\n",
"Viewer\n",
"•\n",
"Updated\n",
"about 9 hours ago\n",
"•\n",
"44\n",
"•\n",
"2.6M\n",
"•\n",
"42\n",
"huggingface/transformers-metadata\n",
"Viewer\n",
"•\n",
"Updated\n",
"about 11 hours ago\n",
"•\n",
"1.51k\n",
"•\n",
"676\n",
"•\n",
"13\n",
"huggingface/policy-docs\n",
"Updated\n",
"1 day ago\n",
"•\n",
"937\n",
"•\n",
"6\n",
"huggingface/community-science-paper-v2\n",
"Viewer\n",
"•\n",
"Updated\n",
"1 day ago\n",
"•\n",
"4.9k\n",
"•\n",
"342\n",
"•\n",
"6\n",
"huggingface/diffusers-metadata\n",
"Viewer\n",
"•\n",
"Updated\n",
"3 days ago\n",
"•\n",
"56\n",
"•\n",
"452\n",
"•\n",
"4\n",
"huggingface/my-distiset-3f5a230e\n",
"Updated\n",
"22 days ago\n",
"•\n",
"15\n",
"huggingface/cookbook-images\n",
"Viewer\n",
"•\n",
"Updated\n",
"29 days ago\n",
"•\n",
"1\n",
"•\n",
"46.8k\n",
"•\n",
"6\n",
"huggingface/vllm-metadata\n",
"Updated\n",
"Oct 8\n",
"•\n",
"12\n",
"huggingface/paper-central-data-2\n",
"Viewer\n",
"•\n",
"Updated\n",
"Oct 4\n",
"•\n",
"58.3k\n",
"•\n",
"70\n",
"•\n",
"2\n",
"Expand 31\n",
"\t\t\t\t\t\t\tdatasets\n",
"Company\n",
"© Hugging Face\n",
"TOS\n",
"Privacy\n",
"About\n",
"Jobs\n",
"Website\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Pricing\n",
"Docs\n",
"\n",
"\n",
"\n",
"enterprise page\n",
"Webpage Title:\n",
"Enterprise Hub - Hugging Face\n",
"Webpage Contents:\n",
"Hugging Face\n",
"Models\n",
"Datasets\n",
"Spaces\n",
"Posts\n",
"Docs\n",
"Enterprise\n",
"Pricing\n",
"Log In\n",
"Sign Up\n",
"Enterprise Hub\n",
"Enterprise-ready version of the world’s leading AI platform\n",
"Subscribe to\n",
"Enterprise Hub\n",
"for $20/user/month with your Hub organization\n",
"Give your organization the most advanced platform to build AI with enterprise-grade security, access controls,\n",
"\t\t\tdedicated support and more.\n",
"Single Sign-On\n",
"Connect securely to your identity provider with SSO integration.\n",
"Regions\n",
"Select, manage, and audit the location of your repository data.\n",
"Audit Logs\n",
"Stay in control with comprehensive logs that report on actions taken.\n",
"Resource Groups\n",
"Accurately manage access to repositories with granular access control.\n",
"Token Management\n",
"Centralized token control and custom approval policies for organization access.\n",
"Analytics\n",
"Track and analyze repository usage data in a single dashboard.\n",
"Advanced Compute Options\n",
"Increase scalability and performance with more compute options like ZeroGPU for Spaces.\n",
"Private Datasets Viewer\n",
"Enable the Dataset Viewer on your private datasets for easier collaboration.\n",
"Advanced security\n",
"Configure organization-wide security policies and default repository visibility.\n",
"Billing\n",
"Control your budget effectively with managed billing and yearly commit options.\n",
"Priority Support\n",
"Maximize your platform usage with priority support from the Hugging Face team.\n",
"Join the most forward-thinking AI organizations\n",
"Everything you already know and love about Hugging Face in Enterprise mode.\n",
"Subscribe to\n",
"Enterprise Hub\n",
"or\n",
"Talk to sales\n",
"AI at Meta\n",
"Enterprise\n",
"company\n",
"•\n",
"2.05k models\n",
"•\n",
"3.75k followers\n",
"Nerdy Face\n",
"Enterprise\n",
"company\n",
"•\n",
"1 model\n",
"•\n",
"234 followers\n",
"ServiceNow-AI\n",
"Enterprise\n",
"company\n",
"•\n",
"108 followers\n",
"Deutsche Telekom AG\n",
"Enterprise\n",
"company\n",
"•\n",
"7 models\n",
"•\n",
"112 followers\n",
"Chegg Inc\n",
"Enterprise\n",
"company\n",
"•\n",
"77 followers\n",
"Lightricks\n",
"Enterprise\n",
"company\n",
"•\n",
"3 models\n",
"•\n",
"363 followers\n",
"Aledade Inc\n",
"Enterprise\n",
"company\n",
"•\n",
"53 followers\n",
"Virtusa Corporation\n",
"Enterprise\n",
"company\n",
"•\n",
"48 followers\n",
"HiddenLayer\n",
"Enterprise\n",
"company\n",
"•\n",
"49 followers\n",
"Ekimetrics\n",
"Enterprise\n",
"company\n",
"•\n",
"47 followers\n",
"Johnson & Johnson\n",
"Enterprise\n",
"company\n",
"•\n",
"35 followers\n",
"Vectara\n",
"Enterprise\n",
"company\n",
"•\n",
"1 model\n",
"•\n",
"54 followers\n",
"HOVER External\n",
"Enterprise\n",
"company\n",
"•\n",
"26 followers\n",
"Qualcomm\n",
"Enterprise\n",
"company\n",
"•\n",
"153 models\n",
"•\n",
"350 followers\n",
"Meta Llama\n",
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"company\n",
"•\n",
"57 models\n",
"•\n",
"13.1k followers\n",
"Orange\n",
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"company\n",
"•\n",
"4 models\n",
"•\n",
"147 followers\n",
"Writer\n",
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"company\n",
"•\n",
"16 models\n",
"•\n",
"178 followers\n",
"Toyota Research Institute\n",
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"company\n",
"•\n",
"8 models\n",
"•\n",
"91 followers\n",
"H2O.ai\n",
"Enterprise\n",
"company\n",
"•\n",
"71 models\n",
"•\n",
"359 followers\n",
"Mistral AI_\n",
"Enterprise\n",
"company\n",
"•\n",
"21 models\n",
"•\n",
"3.37k followers\n",
"IBM Granite\n",
"Enterprise\n",
"company\n",
"•\n",
"56 models\n",
"•\n",
"604 followers\n",
"Liberty Mutual\n",
"Enterprise\n",
"company\n",
"•\n",
"41 followers\n",
"Arcee AI\n",
"Enterprise\n",
"company\n",
"•\n",
"130 models\n",
"•\n",
"259 followers\n",
"Gretel.ai\n",
"Enterprise\n",
"company\n",
"•\n",
"8 models\n",
"•\n",
"70 followers\n",
"Gsk-tech\n",
"Enterprise\n",
"company\n",
"•\n",
"33 followers\n",
"BCG X\n",
"Enterprise\n",
"company\n",
"•\n",
"29 followers\n",
"StepStone Online Recruiting\n",
"Enterprise\n",
"company\n",
"•\n",
"32 followers\n",
"Prezi\n",
"Enterprise\n",
"company\n",
"•\n",
"30 followers\n",
"Shopify\n",
"Enterprise\n",
"company\n",
"•\n",
"371 followers\n",
"Together\n",
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"company\n",
"•\n",
"27 models\n",
"•\n",
"460 followers\n",
"Bloomberg\n",
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"company\n",
"•\n",
"2 models\n",
"•\n",
"132 followers\n",
"Fidelity Investments\n",
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"company\n",
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"114 followers\n",
"Jusbrasil\n",
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"company\n",
"•\n",
"77 followers\n",
"Technology Innovation Institute\n",
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"company\n",
"•\n",
"25 models\n",
"•\n",
"979 followers\n",
"Stability AI\n",
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"company\n",
"•\n",
"95 models\n",
"•\n",
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"company\n",
"•\n",
"245 models\n",
"•\n",
"38 followers\n",
"Kakao Corp.\n",
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"company\n",
"•\n",
"41 followers\n",
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"company\n",
"•\n",
"32 followers\n",
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"80 followers\n",
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"27 followers\n",
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"85 followers\n",
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"→\n",
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"Name\n",
"CPU\n",
"Memory\n",
"Accelerator\n",
"VRAM\n",
"Hourly price\n",
"CPU Basic\n",
"2 vCPU\n",
"16 GB\n",
"-\n",
"-\n",
"FREE\n",
"CPU Upgrade\n",
"8 vCPU\n",
"32 GB\n",
"-\n",
"-\n",
"$0.03\n",
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"4 vCPU\n",
"15 GB\n",
"Nvidia T4\n",
"16 GB\n",
"$0.40\n",
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"8 vCPU\n",
"30 GB\n",
"Nvidia T4\n",
"16 GB\n",
"$0.60\n",
"1x Nvidia L4\n",
"8 vCPU\n",
"30 GB\n",
"Nvidia L4\n",
"24 GB\n",
"$0.80\n",
"4x Nvidia L4\n",
"48 vCPU\n",
"186 GB\n",
"Nvidia L4\n",
"96 GB\n",
"$3.80\n",
"1x Nvidia L40S\n",
"8 vCPU\n",
"62 GB\n",
"Nvidia L4\n",
"48 GB\n",
"$1.80\n",
"4x Nvidia L40S\n",
"48 vCPU\n",
"382 GB\n",
"Nvidia L4\n",
"192 GB\n",
"$8.30\n",
"8x Nvidia L40S\n",
"192 vCPU\n",
"1534 GB\n",
"Nvidia L4\n",
"384 GB\n",
"$23.50\n",
"Nvidia A10G - small\n",
"4 vCPU\n",
"15 GB\n",
"Nvidia A10G\n",
"24 GB\n",
"$1.00\n",
"Nvidia A10G - large\n",
"12 vCPU\n",
"46 GB\n",
"Nvidia A10G\n",
"24 GB\n",
"$1.50\n",
"2x Nvidia A10G - large\n",
"24 vCPU\n",
"92 GB\n",
"Nvidia A10G\n",
"48 GB\n",
"$3.00\n",
"4x Nvidia A10G - large\n",
"48 vCPU\n",
"184 GB\n",
"Nvidia A10G\n",
"96 GB\n",
"$5.00\n",
"Nvidia A100 - large\n",
"12 vCPU\n",
"142 GB\n",
"Nvidia A100\n",
"80 GB\n",
"$4.00\n",
"TPU v5e 1x1\n",
"22 vCPU\n",
"44 GB\n",
"Google TPU v5e\n",
"16 GB\n",
"$1.20\n",
"TPU v5e 2x2\n",
"110 vCPU\n",
"186 GB\n",
"Google TPU v5e\n",
"64 GB\n",
"$4.75\n",
"TPU v5e 2x4\n",
"220 vCPU\n",
"380 GB\n",
"Google TPU v5e\n",
"128 GB\n",
"$9.50\n",
"Custom\n",
"on demand\n",
"on demand\n",
"on demand\n",
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"Name\n",
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"Small\n",
"20 GB\n",
"$5\n",
"Medium\n",
"150 GB\n",
"$25\n",
"Large\n",
"1 TB\n",
"$100\n",
"Building something cool as a side project? We also offer community GPU grants.\n",
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"Starting at $0.033/hour\n",
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"\t\t\t\t\tand autoscaling infrastructure, right from the HF Hub.\n",
"→\n",
"Learn more\n",
"CPU\n",
"instances\n",
"Provider\n",
"Architecture\n",
"vCPUs\n",
"Memory\n",
"Hourly rate\n",
"aws\n",
"Intel Sapphire Rapids\n",
"1\n",
"2GB\n",
"$0.03\n",
"2\n",
"4GB\n",
"$0.07\n",
"4\n",
"8GB\n",
"$0.13\n",
"8\n",
"16GB\n",
"$0.27\n",
"azure\n",
"Intel Xeon\n",
"1\n",
"2GB\n",
"$0.06\n",
"2\n",
"4GB\n",
"$0.12\n",
"4\n",
"8GB\n",
"$0.24\n",
"8\n",
"16GB\n",
"$0.48\n",
"gcp\n",
"Intel Sapphire Rapids\n",
"1\n",
"2GB\n",
"$0.05\n",
"2\n",
"4GB\n",
"$0.10\n",
"4\n",
"8GB\n",
"$0.20\n",
"8\n",
"16GB\n",
"$0.40\n",
"Accelerator\n",
"instances\n",
"Provider\n",
"Architecture\n",
"Topology\n",
"Accelerator Memory\n",
"Hourly rate\n",
"aws\n",
"Inf2\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tNeuron\n",
"x1\n",
"14.5GB\n",
"$0.75\n",
"x12\n",
"760GB\n",
"$12.00\n",
"gcp\n",
"TPU\n",
"\t\t\t\t\t\t\t\t\t\t\t\t\t\t\tv5e\n",
"1x1\n",
"16GB\n",
"$1.20\n",
"2x2\n",
"64GB\n",
"$4.75\n",
"2x4\n",
"128GB\n",
"$9.50\n",
"GPU\n",
"instances\n",
"Provider\n",
"Architecture\n",
"GPUs\n",
"GPU Memory\n",
"Hourly rate\n",
"aws\n",
"NVIDIA T4\n",
"1\n",
"14GB\n",
"$0.50\n",
"4\n",
"56GB\n",
"$3.00\n",
"aws\n",
"NVIDIA L4\n",
"1\n",
"24GB\n",
"$0.80\n",
"4\n",
"96GB\n",
"$3.80\n",
"aws\n",
"NVIDIA L40S\n",
"1\n",
"48GB\n",
"$1.80\n",
"4\n",
"192GB\n",
"$8.30\n",
"8\n",
"384GB\n",
"$23.50\n",
"aws\n",
"NVIDIA A10G\n",
"1\n",
"24GB\n",
"$1.00\n",
"4\n",
"96GB\n",
"$5.00\n",
"aws\n",
"NVIDIA A100\n",
"1\n",
"80GB\n",
"$4.00\n",
"2\n",
"160GB\n",
"$8.00\n",
"4\n",
"320GB\n",
"$16.00\n",
"8\n",
"640GB\n",
"$32.00\n",
"gcp\n",
"NVIDIA T4\n",
"1\n",
"16GB\n",
"$0.50\n",
"gcp\n",
"NVIDIA L4\n",
"1\n",
"24GB\n",
"$0.70\n",
"4\n",
"96GB\n",
"$3.80\n",
"gcp\n",
"NVIDIA A100\n",
"1\n",
"80GB\n",
"$3.60\n",
"2\n",
"160GB\n",
"$7.20\n",
"4\n",
"320GB\n",
"$14.40\n",
"8\n",
"640GB\n",
"$28.80\n",
"gcp\n",
"NVIDIA H100\n",
"1\n",
"80GB\n",
"$10.00\n",
"2\n",
"160GB\n",
"$20.00\n",
"4\n",
"320GB\n",
"$40.00\n",
"8\n",
"640GB\n",
"$80.00\n",
"Pro Account\n",
"PRO\n",
"A monthly subscription to access powerful features.\n",
"→\n",
"Get Pro\n",
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"\t\t\t\t\t\t\t\t\t\tfeatures\n",
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"\t\t\t\t\t\t\t\t\t\tShow your support on your profile\n",
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"\n",
"\n",
"\n",
"careers page\n",
"Webpage Title:\n",
"Hugging Face - Current Openings\n",
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"\n",
"\n",
"\n",
"\n",
"blog\n",
"Webpage Title:\n",
"Hugging Face – Blog\n",
"Webpage Contents:\n",
"Hugging Face\n",
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"RLHF\n",
"Leaderboard\n",
"Case Studies\n",
"LeMaterial: an open source initiative to accelerate materials discovery and research\n",
"By\n",
"AlexDuvalinho\n",
"December 10, 2024\n",
"guest\n",
"•\n",
"18\n",
"Community Articles\n",
"view all\n",
"How to Expand Your AI Music Generations of 30 Seconds to Several Minutes\n",
"By\n",
"theeseus-ai\n",
"•\n",
"about 5 hours ago\n",
"🇪🇺✍ EU AI Act: Systemic Risks in the First CoP Draft Comments ✍🇪🇺\n",
"By\n",
"yjernite\n",
"•\n",
"1 day ago\n",
"•\n",
"6\n",
"The Intersection of CTMU and QCI: Implementing Emergent Intelligence\n",
"By\n",
"dimentox\n",
"•\n",
"1 day ago\n",
"Building an AI-powered search engine from scratch\n",
"By\n",
"as-cle-bert\n",
"•\n",
"2 days ago\n",
"•\n",
"5\n",
"**Build Your Own AI Server at Home: A Cost-Effective Guide Using Pre-Owned Components**\n",
"By\n",
"theeseus-ai\n",
"•\n",
"2 days ago\n",
"•\n",
"1\n",
"MotionLCM-V2: Improved Compression Rate for Multi-Latent-Token Diffusion\n",
"By\n",
"wxDai\n",
"•\n",
"2 days ago\n",
"•\n",
"10\n",
"RLHF 101: A Technical Dive into RLHF\n",
"By\n",
"GitBag\n",
"•\n",
"3 days ago\n",
"•\n",
"1\n",
"[Talk Arena](https://talkarena.org)\n",
"By\n",
"WillHeld\n",
"•\n",
"3 days ago\n",
"Multimodal RAG with Colpali, Milvus and VLMs\n",
"By\n",
"saumitras\n",
"•\n",
"3 days ago\n",
"In Honour of This Year's NeurIPs Test of Time Paper Awardees\n",
"By\n",
"Jaward\n",
"•\n",
"4 days ago\n",
"•\n",
"1\n",
"Power steering: Squeeze massive power from small LLMs\n",
"By\n",
"ucheog\n",
"•\n",
"4 days ago\n",
"•\n",
"4\n",
"Exploring the Power of KaibanJS v0.11.0 🚀\n",
"By\n",
"darielnoel\n",
"•\n",
"4 days ago\n",
"•\n",
"1\n",
"**Building a Custom Retrieval System with Motoko and Node.js**\n",
"By\n",
"theeseus-ai\n",
"•\n",
"4 days ago\n",
"Finding Moroccan Arabic (Darija) in Fineweb 2\n",
"By\n",
"omarkamali\n",
"•\n",
"5 days ago\n",
"•\n",
"17\n",
"Running Your Custom LoRA Fine-Tuned MusicGen Large Locally\n",
"By\n",
"theeseus-ai\n",
"•\n",
"7 days ago\n",
"•\n",
"1\n",
"Building a Local Vector Database Index with Annoy and Sentence Transformers\n",
"By\n",
"theeseus-ai\n",
"•\n",
"8 days ago\n",
"•\n",
"2\n",
"Practical Consciousness Theory for AI System Design\n",
"By\n",
"KnutJaegersberg\n",
"•\n",
"8 days ago\n",
"•\n",
"3\n",
"Releasing QwQ-LongCoT-130K\n",
"By\n",
"amphora\n",
"•\n",
"8 days ago\n",
"•\n",
"6\n",
"They Said It Couldn’t Be Done\n",
"By\n",
"Pclanglais\n",
"•\n",
"8 days ago\n",
"•\n",
"68\n",
"🐺🐦⬛ LLM Comparison/Test: 25 SOTA LLMs (including QwQ) through 59 MMLU-Pro CS benchmark runs\n",
"By\n",
"wolfram\n",
"•\n",
"9 days ago\n",
"•\n",
"68\n",
"Hugging Face models in Amazon Bedrock\n",
"By\n",
"pagezyhf\n",
"December 9, 2024\n",
"•\n",
"5\n",
"Hugging Face Community Releases an Open Preference Dataset for Text-to-Image Generation\n",
"By\n",
"davidberenstein1957\n",
"December 9, 2024\n",
"•\n",
"43\n",
"Welcome PaliGemma 2 – New vision language models by Google\n",
"By\n",
"merve\n",
"December 5, 2024\n",
"•\n",
"103\n",
"“How good are LLMs at fixing their mistakes? A chatbot arena experiment with Keras and TPUs\n",
"By\n",
"martin-gorner\n",
"December 5, 2024\n",
"•\n",
"12\n",
"Rethinking LLM Evaluation with 3C3H: AraGen Benchmark and Leaderboard\n",
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"Hugging Face\n",
"812,455 followers\n",
"1d\n",
"Since the release of OpenAI's o1 model, test-time compute scaling has become one of the hottest topics in LLM research. Rather than relying on ever-larger pre-training budgets, test-time methods use dynamic inference strategies that allow models to “think longer” on harder problems. \n",
"\n",
"Although it is not known how o1 was trained, recent research from DeepMind shows that test-time compute can be scaled optimally through strategies like iterative self-refinement or using a reward model to perform search over the space of solutions.\n",
"\n",
"In this webinar, Lewis. Ed, and Philipp explore the benefits of applying the DeepMind recipe to scale up test-time compute for open models. Join us to find out how we enabled Llama 1B to outperform Llama 8B on the challenging MATH benchmark!\n",
"Scaling test-time compute for open models to solve math problems like o1\n",
"www.linkedin.com\n",
"6\n",
"1 Comment\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Gradio\n",
"46,081 followers\n",
"6h\n",
"Report this post\n",
"🔥🔥 FlowEdit\n",
"Gradio\n",
"app is now live on\n",
"Hugging Face\n",
":\n",
"https://lnkd.in/gG7eXuBQ\n",
"For reference, please check out our earlier post introducing the FlowEdit model:\n",
"https://lnkd.in/gHDUHTct\n",
"…more\n",
"42\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Gradio\n",
"46,081 followers\n",
"6h\n",
"Edited\n",
"Report this post\n",
"Leffa from Meta is a 🆕 unified framework for Virtual try-on and Pose transfer!🔥 👕 \n",
"\n",
"Provides controllable person image generation that enables precise manipulation of both appearance (i.e., Virtual try-on) and pose (i.e., Pose transfer).\n",
"\n",
"Learn more about 📚 Code,🔥 Demo, and 🤗 Model ⬇ \n",
"\n",
"> Leffa Model on\n",
"Hugging Face\n",
"Hub with MIT license:\n",
"https://lnkd.in/gfFWcsCF\n",
"> Build Leffa for VirtualTryOn and Pose Transfer locally with\n",
"Gradio\n",
":\n",
"https://lnkd.in/gcnezZrW\n",
"> Leffa Gradio app is up on\n",
"Hugging Face\n",
"Spaces:\n",
"https://lnkd.in/gdnuNTHv\n",
"…more\n",
"204\n",
"1 Comment\n",
"Like\n",
"Comment\n",
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"Hugging Face\n",
"reposted this\n",
"Sayak Paul\n",
"ML @ Hugging Face 🤗\n",
"8h\n",
"Report this post\n",
"Sun never sets for the Diffusers team 🤗🌞\n",
"\n",
"Presenting our latest integration of LTX by\n",
"Lightricks\n",
"- a superior video generation model at its scale. \n",
"\n",
"It produces 24 FPS videos at a 768x512 resolution faster than they can be watched 🤪\n",
"\n",
"Model is pretty consumer-friendly, but in case not, you should be able to leverage all the diffusers goodies to make it run on your machine!\n",
"\n",
"Kudos to\n",
"Aryan V S\n",
", who led this integration. Next up is -- a script for ___ LTX 🚀 Wrong answers only 🪄\n",
"\n",
"Check it out here:\n",
"https://lnkd.in/gwMKniDN\n",
"…more\n",
"102\n",
"2 Comments\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Aryan V S\n",
"Engineer @Hugging Face 🤗\n",
"8h\n",
"Report this post\n",
"It is raining open-source video models these last few weeks! LTX Video is now in Diffusers with finetuning and structure-control support coming shortly. This model comes from the amazing brains at\n",
"Lightricks\n",
"💫 🧑🍳 \n",
"\n",
"LTXV is very smol compared to some other open source models, but packs twice their punch! Fast generation, low memory requirements, high quality - what more do we need? Just two years ago, image generation models struggled at 2B param scale but now we have entirely different beasts 💪 \n",
"\n",
"Links in comment\n",
"…more\n",
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"1 Comment\n",
"Like\n",
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"Share\n",
"Hugging Face\n",
"reposted this\n",
"Ben Burtenshaw\n",
"Building Argilla @ 🤗 Hugging face\n",
"10h\n",
"Report this post\n",
"If you're improving your LLM skills on real world use cases, this free course just got real! Smol course now has 4 chapters, and the most important was just released.\n",
"\n",
"🌎 Chapter 4 shows you how to evaluate models on custom use cases. So you can test out the models you trained in the earlier chapters to see if they do the job.\n",
"\n",
"📚 The course walks through using simple libraries to create an evaluation dataset for a custom domain. Libraries like lighteval, distilabel, and argilla. \n",
"\n",
"🤯 If you've ever wondered how to evaluate an LLM for you problem, this is the material for you.\n",
"\n",
"We'll return to this chapter in a capstone project!\n",
"127\n",
"12 Comments\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Freddy Boulton\n",
"Software Engineer @ 🤗\n",
"1d\n",
"Report this post\n",
"Hello Llama 3.2! 🗣🦙\n",
"\n",
"Build a Siri-like coding assistant that responds to \"Hello Llama\" in 100 lines of python!\n",
"\n",
"This is all possible with gradio-webrtc, the easiest way to add audio/video streaming to your AI models! Links to repo and\n",
"Hugging Face\n",
"space below:\n",
"…more\n",
"175\n",
"5 Comments\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Amélie Viallet\n",
"Building Argilla @ 🤗 Hugging Face\n",
"1d\n",
"Report this post\n",
"[FineWeb2 - Annotation Sprint] It's amazing to see “language teams” growing every day! 🤗 \n",
"\n",
"Annotation can be tedious and requires a lot of focus, so it’s always easier—and way more fun—when done together.\n",
"\n",
"Some datasets already have over 15 contributors—👏—while others rely on just one amazing person (double 👏👏 for them!). \n",
"Finding contributors for less widespread languages can be tricky!\n",
"\n",
"Here are a few tips to connect with your language community:\n",
"\n",
" 🦜 Call your community in your language: We’ve prepared templates you can translate before publishing on Social Media or email.\n",
"\n",
"🪡 Make the task simpler for all: Translate the guidelines! They can be dense, and the examples are super helpful for getting it right.\n",
"\n",
"🫚 Share the initiative widely with anyone passionate about supporting their language. The only requirement is fluency!\n",
"\n",
"And you, what are your tips?\n",
"#fineWeb2\n",
"#argilla\n",
"53\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"812,455 followers\n",
"1d\n",
"Report this post\n",
"Since the release of OpenAI's o1 model, test-time compute scaling has become one of the hottest topics in LLM research. Rather than relying on ever-larger pre-training budgets, test-time methods use dynamic inference strategies that allow models to “think longer” on harder problems. \n",
"\n",
"Although it is not known how o1 was trained, recent research from DeepMind shows that test-time compute can be scaled optimally through strategies like iterative self-refinement or using a reward model to perform search over the space of solutions.\n",
"\n",
"In this webinar, Lewis. Ed, and Philipp explore the benefits of applying the DeepMind recipe to scale up test-time compute for open models. Join us to find out how we enabled Llama 1B to outperform Llama 8B on the challenging MATH benchmark!\n",
"Scaling test-time compute for open models to solve math problems like o1\n",
"www.linkedin.com\n",
"15\n",
"Like\n",
"Comment\n",
"Share\n",
"Hugging Face\n",
"reposted this\n",
"Franck Abgrall\n",
"Software Engineer @ Hugging Face 🤗\n",
"1d\n",
"Report this post\n",
"✨ We just released an overview of the permissions for fine-grained tokens by hovering over the badge on\n",
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"\n",
"It will show the highest permission you've set for each entity\n",
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}
],
"source": [
"print(get_all_details(\"https://huggingface.co\"))"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "9b863a55-f86c-4e3f-8a79-94e24c1a8cf2",
"metadata": {},
"outputs": [],
"source": [
"system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"and creates a short brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"Include details of company culture, customers and careers/jobs if you have the information.\"\n",
"\n",
"# Or uncomment the lines below for a more humorous brochure - this demonstrates how easy it is to incorporate 'tone':\n",
"\n",
"# system_prompt = \"You are an assistant that analyzes the contents of several relevant pages from a company website \\\n",
"# and creates a short humorous, entertaining, jokey brochure about the company for prospective customers, investors and recruits. Respond in markdown.\\\n",
"# Include details of company culture, customers and careers/jobs if you have the information.\"\n"
]
},
{
"cell_type": "code",
"execution_count": 65,
"id": "6ab83d92-d36b-4ce0-8bcc-5bb4c2f8ff23",
"metadata": {},
"outputs": [],
"source": [
"def get_brochure_user_prompt(company_name, url):\n",
" user_prompt = f\"You are looking at a company called: {company_name}\\n\"\n",
" user_prompt += f\"Here are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\n\"\n",
" user_prompt += f\"The Title must be bold and subtitle in italic\\n\"\n",
" user_prompt += get_all_details(url)\n",
" user_prompt = user_prompt[:5_000] # Truncate if more than 5,000 characters\n",
" return user_prompt"
]
},
{
"cell_type": "code",
"execution_count": 32,
"id": "cd909e0b-1312-4ce2-a553-821e795d7572",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found links:: {'links': [{'type': 'about page', 'url': 'https://huggingface.co'}, {'type': 'models page', 'url': 'https://huggingface.co/models'}, {'type': 'datasets page', 'url': 'https://huggingface.co/datasets'}, {'type': 'spaces page', 'url': 'https://huggingface.co/spaces'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'community page', 'url': 'https://discuss.huggingface.co'}, {'type': 'GitHub page', 'url': 'https://github.com/huggingface'}, {'type': 'Twitter page', 'url': 'https://twitter.com/huggingface'}, {'type': 'LinkedIn page', 'url': 'https://www.linkedin.com/company/huggingface/'}]}\n"
]
},
{
"data": {
"text/plain": [
"'You are looking at a company called: HuggingFace\\nHere are the contents of its landing page and other relevant pages; use this information to build a short brochure of the company in markdown.\\nLanding page:\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nTrending on\\nthis week\\nModels\\nmeta-llama/Llama-3.3-70B-Instruct\\nUpdated\\n3 days ago\\n•\\n102k\\n•\\n960\\ntencent/HunyuanVideo\\nUpdated\\n6 days ago\\n•\\n3.73k\\n•\\n992\\nDatou1111/shou_xin\\nUpdated\\n5 days ago\\n•\\n7.84k\\n•\\n322\\nblack-forest-labs/FLUX.1-dev\\nUpdated\\nAug 16\\n•\\n1.38M\\n•\\n7.23k\\nQwen/QwQ-32B-Preview\\nUpdated\\n15 days ago\\n•\\n92.8k\\n•\\n1.27k\\nBrowse 400k+ models\\nSpaces\\nRunning\\non\\nZero\\n1.13k\\n🏢\\nTRELLIS\\nScalable and Versatile 3D Generation from images\\nRunning\\non\\nZero\\n291\\n🦀🏆\\nFLUXllama\\nFLUX 4-bit Quantization(just 8GB VRAM)\\nRunning\\non\\nL40S\\n244\\n🚀\\nFlux Style Shaping\\nOptical illusions and style transfer with FLUX\\nRunning\\non\\nCPU Upgrade\\n5.92k\\n👕\\nKolors Virtual Try-On\\nRunning\\non\\nZero\\n5.71k\\n🖥\\nFLUX.1 [dev]\\nBrowse 150k+ applications\\nDatasets\\nHuggingFaceFW/fineweb-2\\nUpdated\\n5 days ago\\n•\\n27.6k\\n•\\n284\\nfka/awesome-chatgpt-prompts\\nUpdated\\nSep 3\\n•\\n7.71k\\n•\\n6.52k\\nCohereForAI/Global-MMLU\\nUpdated\\n1 day ago\\n•\\n4.59k\\n•\\n77\\nO1-OPEN/OpenO1-SFT\\nUpdated\\n22 days ago\\n•\\n1.34k\\n•\\n175\\namphora/QwQ-LongCoT-130K\\nUpdated\\n8 days ago\\n•\\n536\\n•\\n46\\nBrowse 100k+ datasets\\nThe Home of Machine Learning\\nCreate, discover and collaborate on ML better.\\nThe collaboration platform\\nHost and collaborate on unlimited public models, datasets and applications.\\nMove faster\\nWith the HF Open source stack.\\nExplore all modalities\\nText, image, video, audio or even 3D.\\nBuild your portfolio\\nShare your work with the world and build your ML profile.\\nSign Up\\nAccelerate your ML\\nWe provide paid Compute and Enterprise solutions.\\nCompute\\nDeploy on optimized\\nInference Endpoints\\nor update your\\nSpaces applications\\nto a GPU in a few clicks.\\nView pricing\\nStarting at $0.60/hour for GPU\\nEnterprise\\nGive your team the most advanced platform to build AI with enterprise-grade security, access controls and\\n\\t\\t\\tdedicated support.\\nGetting started\\nStarting at $20/user/month\\nSingle Sign-On\\nRegions\\nPriority Support\\nAudit Logs\\nResource Groups\\nPrivate Datasets Viewer\\nMore than 50,000 organizations are using Hugging Face\\nAi2\\nEnterprise\\nnon-profit\\n•\\n361 models\\n•\\n1.71k followers\\nAI at Meta\\nEnterprise\\ncompany\\n•\\n2.05k models\\n•\\n3.75k followers\\nAmazon Web Services\\ncompany\\n•\\n21 models\\n•\\n2.42k followers\\nGoogle\\ncompany\\n•\\n910 models\\n•\\n5.46k followers\\nIntel\\ncompany\\n•\\n217 models\\n•\\n2.05k followers\\nMicrosoft\\ncompany\\n•\\n351 models\\n•\\n6.06k followers\\nGrammarly\\ncompany\\n•\\n10 models\\n•\\n98 followers\\nWriter\\nEnterprise\\ncompany\\n•\\n16 models\\n•\\n178 followers\\nOur Open Source\\nWe are building the foundation of ML tooling with the community.\\nTransformers\\n136,246\\nState-of-the-art ML for Pytorch, TensorFlow, and JAX.\\nDiffusers\\n26,624\\nState-of-the-art diffusion models for image and audio generation in PyTorch.\\nSafetensors\\n2,953\\nSimple, safe way to store and distribute neural networks weights safely and quickly.\\nHub Python Library\\n2,165\\nClient library for the HF Hub: manage repositories from your Python runtime.\\nTokenizers\\n9,150\\nFast tokenizers, optimized for both research and production.\\nPEFT\\n16,699\\nParameter efficient finetuning methods for large models.\\nTransformers.js\\n12,337\\nState-of-the-art Machine Learning for the web. Run Transformers directly in your browser, with no need for a server.\\ntimm\\n32,592\\nState-of-the-art computer vision models, layers, optimizers, training/evaluation, and utilities.\\nTRL\\n10,308\\nTrain transformer language models with reinforcement learning.\\nDatasets\\n19,349\\nAccess and share datasets for computer vision, audio, and NLP tasks.\\nText Generation Inference\\n9,433\\nToolkit to serve Large Language Models.\\nAccelerate\\n8,053\\nEasily train and use PyTorch models with multi-GPU, TPU, mixed-precision.\\nWebsite\\nModels\\nDatasets\\nSpaces\\nTasks\\nInference Endpoints\\nHuggingChat\\nCompany\\nAbout\\nBrand assets\\nTerms of service\\nPrivacy\\nJobs\\nPress\\nResources\\nLearn\\nDocumentation\\nBlog\\nForum\\nService Status\\nSocial\\nGitHub\\nTwitter\\nLinkedIn\\nDiscord\\nZhihu\\nWeChat\\n\\n\\n\\nabout page\\nWebpage Title:\\nHugging Face – The AI community building the future.\\nWebpage Contents:\\nHugging Face\\nModels\\nDatasets\\nSpaces\\nPosts\\nDocs\\nEnterprise\\nPricing\\nLog In\\nSign Up\\nThe AI community building the future.\\nThe platform where the machine learning community collaborates on models, datasets, and applications.\\nTrending on\\nthis week\\nModels\\nmeta-llama/Llama-3.3-70B-Instruct\\nUpdated\\n3 days ago\\n•\\n102k\\n•\\n960\\ntencent/HunyuanVideo\\nUpdated\\n6 days ago\\n•\\n3.73k\\n•\\n992\\nDatou1111/shou_xin\\nUpdated\\n5 days ago\\n•\\n7.84k\\n•\\n322\\nblack-forest-labs/FLUX.1-dev\\nUpdated\\nAug 16\\n•\\n1.38M\\n•\\n7.23k\\nQwen/QwQ-32B-Preview\\nUpdated\\n15 days ago\\n•\\n92.8k\\n•\\n1.27k\\nBrowse 400k+ models\\nSpaces\\nRunning\\non\\nZero\\n1.13k\\n🏢\\nTRELLIS\\nScalable and Versatile 3D Ge'"
]
},
"execution_count": 32,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"get_brochure_user_prompt(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": 67,
"id": "e44de579-4a1a-4e6a-a510-20ea3e4b8d46",
"metadata": {},
"outputs": [],
"source": [
"def create_brochure(company_name, url):\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" )\n",
" return response.choices[0].message.content\n",
" "
]
},
{
"cell_type": "code",
"execution_count": 71,
"id": "e093444a-9407-42ae-924a-145730591a39",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found links:: {'links': [{'type': 'about page', 'url': 'https://huggingface.com'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'blog page', 'url': 'https://huggingface.com/blog'}, {'type': 'company page', 'url': 'https://discuss.huggingface.co'}, {'type': 'github page', 'url': 'https://github.com/huggingface'}, {'type': 'twitter page', 'url': 'https://twitter.com/huggingface'}, {'type': 'linkedin page', 'url': 'https://www.linkedin.com/company/huggingface/'}]}\n"
]
},
{
"data": {
"text/markdown": [
"```markdown\n",
"# **Hugging Face**\n",
"### _The AI Community Building the Future_\n",
"\n",
"---\n",
"\n",
"## 💡 About Us\n",
"Hugging Face is not just a company; it's a vibrant community at the forefront of artificial intelligence and machine learning. Our platform serves as a collaborative space where enthusiasts, researchers, and developers come together to create, discover, and share cutting-edge models, datasets, and applications. With a commitment to open source and innovation, we're shaping the future of AI.\n",
"\n",
"## 🌍 Our Offerings\n",
"- **Models**: Explore over 400,000 machine learning models, including state-of-the-art architectures for various tasks across text, image, video, and audio domains.\n",
"- **Datasets**: Access a rich repository of 100,000+ datasets to enhance your ML projects.\n",
"- **Spaces**: Deploy and share your applications seamlessly on our platform, fostering collaboration and discovery.\n",
"- **Enterprise Solutions**: We provide tailored solutions with enterprise-grade security, support, and access controls for over 50,000 organizations, including notable giants like Google, Microsoft, and Amazon Web Services.\n",
"\n",
"## 🤝 Our Customers\n",
"We cater to a diverse range of clients, from emerging startups to established enterprises looking to leverage AI capabilities. Hugging Face is trusted by organizations like Meta, Grammarly, and Intel, making it a go-to platform for machine learning advancement.\n",
"\n",
"## 🌱 Company Culture\n",
"At Hugging Face, we foster an inclusive and dynamic culture that encourages creativity and collaboration. Our team is composed of enthusiastic individuals from various backgrounds who share a passion for artificial intelligence. We believe in the power of community contributions to make machine learning accessible to everyone. A commitment to continuous learning and support for individual growth encourages employees to innovate and challenge the status quo.\n",
"\n",
"## 🚀 Careers at Hugging Face\n",
"Join us in our mission to democratize AI! We are on the lookout for talented individuals across various domains including engineering, data science, and community engagement. If you are passionate about building tools that impact the future of AI, consider applying for a position. Explore our current job openings on our [Jobs page](https://huggingface.co/jobs).\n",
"\n",
"## 🌟 Why Choose Hugging Face?\n",
"- **Open Source Commitment**: We believe in the power of open collaboration and sharing knowledge.\n",
"- **Innovative Tools**: Our state-of-the-art tools and libraries streamline the ML development process.\n",
"- **Community Focus**: We thrive on contributions from our community, ensuring the best resources are at everyone's fingertips.\n",
"\n",
"---\n",
"\n",
"For more information, visit our website at [HuggingFace.co](https://huggingface.co) and join us in building the future of AI!\n",
"```"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"brochure= create_brochure(\"HuggingFace\", \"https://huggingface.com\")\n",
"display(Markdown(brochure))"
]
},
{
"cell_type": "code",
"execution_count": 82,
"id": "49977b5d-1378-4786-b61e-a02bc969cd40",
"metadata": {},
"outputs": [],
"source": [
"def translate(content, lang):\n",
" print(\"start_the_translation\")\n",
" trans_system_prompt = f\"Has an assistant, you have the task to translate in {lang} the content and keep the result in markdown\"\n",
" trans_user_prompt = \"You received the content of the brochure. you must translate it for international client\\n.\"\n",
" trans_user_prompt += f\"The content is:{content}\"\n",
" response = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\":trans_system_prompt },\n",
" {\"role\": \"user\", \"content\": trans_user_prompt}\n",
" ],\n",
" )\n",
" return response.choices[0].message.content"
]
},
{
"cell_type": "code",
"execution_count": 85,
"id": "afe303ca-16c8-4437-9596-b56e9af931fa",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"start_the_translation\n"
]
},
{
"data": {
"text/markdown": [
"```markdown\n",
"# **Hugging Face**\n",
"### _Communauté ya AI oyo ezalaka na etape ya koleka_\n",
"\n",
"---\n",
"\n",
"## 💡 Ekomeli\n",
"Hugging Face ezali te pekeli; ezali communauté ya makasi na ntango ya kitoko ya intelijans ya makasi mpe kokanga boye. Plateforme na biso ezali ndako ya bokokani epayi ya ba-enthousiasts, ba-researchers, mpe ba-developers oyo bakotaki kosepela, kobongisa, mpe koyamba ba-modèles, ba-datasets, mpe ba-applications ya malamu. Na ekateli ya kosala na makambo ya sika mpe bokeseni, tozali kolongola avenir ya AI.\n",
"\n",
"## 🌍 Makambo na biso\n",
"- **Ba-Modèles**: Talá bisika ya ba-modèles ya machine learning koleka 400,000, okata na ba-architectures ya kitoko mpo na makambo ebele na domaine ya texte, image, vidéo, mpe audio.\n",
"- **Ba-Datasets**: Pesa nzela na ba-datasets ya 100,000+ mpo na kobundisa misala na yo ya ML.\n",
"- **Ba-Spaces**: Lula mpe koyamba ba-applications na yo na nzela ya kitoko na plateforme na biso, kosembola bokokani mpe kokanisa.\n",
"- **Soluté ya Entreprise**: Tozali kopesa makambo oyo etali entreprise na bokamba ya sécurité, soutien, mpe contrôles ya accès mpo na ba-organisation koleka 50,000, na ba-grands comme Google, Microsoft, mpe Amazon Web Services.\n",
"\n",
"## 🤝 Ba-Clients na biso\n",
"Tosala na ba-clients nyonso, na banzela ya ba-startups ya sika ti na ba-entreprises ya kala oyo balingaka kokanisa makambo ya AI. Hugging Face ezali site ya boko ya ba-organisation lokola Meta, Grammarly, mpe Intel, oyo esalelaka yango mpo na ntango ya kokola ya machine learning.\n",
"\n",
"## 🌱 Culture ya Entreprise\n",
"Na Hugging Face, tosalaka culture ya kokamwa mpe kolanda molende oyo ebotaka bokokani mpe bokeseni. Équipe na biso ezali na ba-personnes ya gete oyo bazali na makambo ebele ya bokinaka, kasi bokeseni ya intelijans ya makasi. Tozali na likanisi ya makasi mpo na basani ya communauté mpo na komibanzela machine learning mpo na nyonso. État ya kotalela mpo na koyekola mpo na bokeseni ya moto moko na moko ekangani ba-employés na biso mpo na kolongola mabe mpe kosembola mpasi.\n",
"\n",
"## 🚀 Ba-Carrières na Hugging Face\n",
"Landa biso na mosala na biso ya kolongola AI na komipesa! Tozali na ngai ya kotela ba-personnes ya makasi na ba-domaines ebele lokola engineering, data science, mpe engagement ya communauté. Soki ozali na likambo ya kolona ba-tools oyo ekoyeba avenir ya AI, zwela mosala. Talá ba-postes oyo ezali na [page ya Ba-Emplois](https://huggingface.co/jobs).\n",
"\n",
"## 🌟 Mpo na nini Olingi Hugging Face?\n",
"- **État ya Ozala Makasi na Makambo ya Sika**: Tozali na likanisi ya makasi mpo na bokokani ya baninga mpe kondima koyamba makambo.\n",
"- **Ba-Tools ya Kitoko**: Ba-tools na biso mpe ba-archives ya kitoko basalaka makambo ya ml moko.\n",
"- **Koleka na Communauté**: Tokoki na sokisoko ya ba-contributions ya ba-communauté na biso, mpo na kokoba na nzela ya kitoko.\n",
"\n",
"---\n",
"\n",
"Mpo na makambo ebele, kende na website na biso na [HuggingFace.co](https://huggingface.co) mpe landa biso na kolongola avenir ya AI!\n",
"```"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(Markdown(translate(brochure, \"lingala\")))"
]
},
{
"cell_type": "markdown",
"id": "61eaaab7-0b47-4b29-82d4-75d474ad8d18",
"metadata": {},
"source": [
"## Finally - a minor improvement\n",
"\n",
"With a small adjustment, we can change this so that the results stream back from OpenAI,\n",
"with the familiar typewriter animation"
]
},
{
"cell_type": "code",
"execution_count": 35,
"id": "51db0e49-f261-4137-aabe-92dd601f7725",
"metadata": {},
"outputs": [],
"source": [
"def stream_brochure(company_name, url):\n",
" stream = openai.chat.completions.create(\n",
" model=MODEL,\n",
" messages=[\n",
" {\"role\": \"system\", \"content\": system_prompt},\n",
" {\"role\": \"user\", \"content\": get_brochure_user_prompt(company_name, url)}\n",
" ],\n",
" stream=True\n",
" )\n",
" \n",
" response = \"\"\n",
" display_handle = display(Markdown(\"\"), display_id=True)\n",
" for chunk in stream:\n",
" response += chunk.choices[0].delta.content or ''\n",
" response = response.replace(\"```\",\"\").replace(\"markdown\", \"\")\n",
" update_display(Markdown(response), display_id=display_handle.display_id)"
]
},
{
"cell_type": "code",
"execution_count": 55,
"id": "56bf0ae3-ee9d-4a72-9cd6-edcac67ceb6d",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Found links:: {'links': [{'type': 'about page', 'url': 'https://huggingface.co/about'}, {'type': 'careers page', 'url': 'https://apply.workable.com/huggingface/'}, {'type': 'enterprise page', 'url': 'https://huggingface.co/enterprise'}, {'type': 'pricing page', 'url': 'https://huggingface.co/pricing'}, {'type': 'blog page', 'url': 'https://huggingface.co/blog'}, {'type': 'discussion forums', 'url': 'https://discuss.huggingface.co'}, {'type': 'github page', 'url': 'https://github.com/huggingface'}, {'type': 'twitter page', 'url': 'https://twitter.com/huggingface'}, {'type': 'linkedin page', 'url': 'https://www.linkedin.com/company/huggingface/'}]}\n"
]
},
{
"data": {
"text/markdown": [
"# <span style=\"color:red\">Welcome to Hugging Face!</span>\n",
"\n",
"---\n",
"\n",
"### 🤖 **The AI Community Helping You Hug It Out** 🤗\n",
"\n",
"At Hugging Face, we don't just build AI; we build a whole community that dreams big and collaborates even bigger! Forget about flying solo; our platform offers a cozy corner where machine learning enthusiasts can come together, share models, datasets, and maybe even a virtual cup of coffee. ☕\n",
"\n",
"### 🚀 What’s Trending This Week?\n",
"\n",
"- **meta-llama/Llama-3.3-70B-Instruct** - Updated 3 days ago and already has more followers than your favorite influencer: **102k**! \n",
"- **tencent/HunyuanVideo** - Still waiting on a Netflix adaptation. Just **3.73k** users trying to make sense of it!\n",
"- **black-forest-labs/FLUX.1-dev** - Not just a coding project; it’s a whole experience—update rate: once a month and counting. **1.38M** supporters!\n",
"\n",
"### 🏢 More Than Just Algorithms\n",
"\n",
"We’re like a cozy library mixed with an explosion of creativity (and a sprinkle of caffeine)! With over **400,000 models** and **100,000 datasets**, you’ll find all the toys you need to build the ultimate machine learning castle! 🏰\n",
"\n",
"### 🎉 **Join Our Hug-tastic Culture!**\n",
"\n",
"- **Collaboration**: Work hand-in-hand with over **50,000 organizations** including big names like Google, Microsoft, and Grammarly! (Don’t worry, we don’t ask for autographs)\n",
" \n",
"- **Open Source Fun**: Participate in creating cool new tools—like our Transformers™ library! Who knew a “transformer” could be cooler than a truck that turns into a robot? 🤔\n",
"\n",
"### 🕵 Careers at Hugging Face\n",
"\n",
"- **Looking for a Job?** We’re hiring! Take your skills from pretender to defender in the AI world. \n",
"- **Perks**: Flexible workspace, competitive pay, and the option to take breaks every now and then to hug a co-worker! Just kidding, keep it professional…mostly! 😉\n",
"\n",
"### 💌 Join Us Today!\n",
"\n",
"Are you an AI aficionado or just really fond of hugging? 😅 Come for the algorithms, stay for the community! We provide paid Compute and Enterprise solutions, so whether you're a startup or a seasoned pro, we offer the right resources to transform your capabilities!\n",
"\n",
"### 📞 Get In Touch!\n",
"\n",
"Still have questions? Willing to engage in a deep dive discussion about whether machines can cry? Or just want to say hi? Check out our [website](https://huggingface.co) or follow us on social media.\n",
"\n",
"---\n",
"### **Let’s Hug it Out With AI!**"
],
"text/plain": [
"<IPython.core.display.Markdown object>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "fdb3f8d8-a3eb-41c8-b1aa-9f60686a653b",
"metadata": {},
"outputs": [],
"source": [
"# Try changing the system prompt to the humorous version when you make the Brochure for Hugging Face:\n",
"\n",
"stream_brochure(\"HuggingFace\", \"https://huggingface.co\")"
]
},
{
"cell_type": "markdown",
"id": "a27bf9e0-665f-4645-b66b-9725e2a959b5",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../business.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#181;\">Business applications</h2>\n",
" <span style=\"color:#181;\">In this exercise we extended the Day 1 code to make multiple LLM calls, and generate a document.\n",
"\n",
"This is perhaps the first example of Agentic AI design patterns, as we combined multiple calls to LLMs. This will feature more in Week 2, and then we will return to Agentic AI in a big way in Week 8 when we build a fully autonomous Agent solution.\n",
"\n",
"Generating content in this way is one of the very most common Use Cases. As with summarization, this can be applied to any business vertical. Write marketing content, generate a product tutorial from a spec, create personalized email content, and so much more. Explore how you can apply content generation to your business, and try making yourself a proof-of-concept prototype.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "14b2454b-8ef8-4b5c-b928-053a15e0d553",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../important.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#900;\">Before you move to Week 2 (which is tons of fun)</h2>\n",
" <span style=\"color:#900;\">Please see the week1 EXERCISE notebook for your challenge for the end of week 1. This will give you some essential practice working with Frontier APIs, and prepare you well for Week 2.</span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "markdown",
"id": "17b64f0f-7d33-4493-985a-033d06e8db08",
"metadata": {},
"source": [
"<table style=\"margin: 0; text-align: left;\">\n",
" <tr>\n",
" <td style=\"width: 150px; height: 150px; vertical-align: middle;\">\n",
" <img src=\"../resources.jpg\" width=\"150\" height=\"150\" style=\"display: block;\" />\n",
" </td>\n",
" <td>\n",
" <h2 style=\"color:#f71;\">A reminder on 2 useful resources</h2>\n",
" <span style=\"color:#f71;\">1. The resources for the course are available <a href=\"https://edwarddonner.com/2024/11/13/llm-engineering-resources/\">here.</a><br/>\n",
" 2. I'm on LinkedIn <a href=\"https://www.linkedin.com/in/eddonner/\">here</a> and I love connecting with people taking the course!\n",
" </span>\n",
" </td>\n",
" </tr>\n",
"</table>"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3de35771-455f-40b5-ba44-7c0a6b7c427a",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
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