agents-course/unit_1_quiz_student_responses
Updated
โข
116
โข
2
Hugging Face Agents Course
university
AI & ML interests
None defined yet.
Recent Activity
View all activity
agents-course's activity
burtenshawย
updated
a
dataset
5 minutes ago
burtenshawย
authored
a
paper
about 1 hour ago
thomwolfย
authored
a
paper
about 1 hour ago
ThomasSimoniniย
updated
a
dataset
about 3 hours ago
Post
5530
Introducing ๐ผ๐ฝ๐ฒ๐ป ๐๐ฒ๐ฒ๐ฝ-๐ฅ๐ฒ๐๐ฒ๐ฎ๐ฟ๐ฐ๐ต by Hugging Face! ๐ฅ
OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.
โฑ๏ธ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! โฑ๏ธ
โก๏ธ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...
We aimed for the best performance: are the agent's answers really rigorous?
On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
โก๏ธ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution ๐ช๐ช
And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !
Read the blog post ๐ https://huggingface.co/blog/open-deep-research
OpenAI's latest agentic app Deep Research seems really good... But it's closed, as usual.
โฑ๏ธ So with a team of cracked colleagues, we set ourselves a 24hours deadline to replicate and open-source Deep Research! โฑ๏ธ
โก๏ธ We built open-Deep-Research, an entirely open agent that can: navigate the web autonomously, scroll and search through pages, download and manipulate files, run calculation on data...
We aimed for the best performance: are the agent's answers really rigorous?
On GAIA benchmark, Deep Research had 67% accuracy on the validation set.
โก๏ธ open Deep Research is at 55% (powered by o1), it is:
- the best pass@1 solution submitted
- the best open solution ๐ช๐ช
And it's only getting started ! Please jump in, drop PRs, and let's bring it to the top !
Read the blog post ๐ https://huggingface.co/blog/open-deep-research
Post
2433
Now you can launch a code agent directly from your terminal!
โจ ๐๐๐๐๐๐๐๐๐ "๐๐๐๐ ๐๐๐๐" directly launches a CodeAgent
โถ๏ธ This also works with web agents (replace ๐๐๐๐๐๐๐๐๐ with ๐ ๐๐๐๐๐๐๐) thanks to @merve !
๐พ Another treat from smolagents release 1.7.0:
Now agents have a memory mechanism, enabling many possibilities like replaying the last run with ๐๐๐๐๐.๐๐๐๐๐๐ข(), thank you @clefourrier !
Check the release notes here ๐ https://github.com/huggingface/smolagents/releases/tag/v1.7.0
โจ ๐๐๐๐๐๐๐๐๐ "๐๐๐๐ ๐๐๐๐" directly launches a CodeAgent
โถ๏ธ This also works with web agents (replace ๐๐๐๐๐๐๐๐๐ with ๐ ๐๐๐๐๐๐๐) thanks to @merve !
๐พ Another treat from smolagents release 1.7.0:
Now agents have a memory mechanism, enabling many possibilities like replaying the last run with ๐๐๐๐๐.๐๐๐๐๐๐ข(), thank you @clefourrier !
Check the release notes here ๐ https://github.com/huggingface/smolagents/releases/tag/v1.7.0
Post
2875
I have just released a new blogpost about kv caching and its role in inference speedup ๐
๐ https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
๐ https://huggingface.co/blog/not-lain/kv-caching/
some takeaways :
- 4 replies
Post
3549
๐ง๐ต๐ฒ ๐๐๐ฏ ๐๐ฒ๐น๐ฐ๐ผ๐บ๐ฒ๐ ๐ฒ๐
๐๐ฒ๐ฟ๐ป๐ฎ๐น ๐ถ๐ป๐ณ๐ฒ๐ฟ๐ฒ๐ป๐ฐ๐ฒ ๐ฝ๐ฟ๐ผ๐๐ถ๐ฑ๐ฒ๐ฟ๐!
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
โ Hosting our own inference was not enough: now the Hub 4 new inference providers: fal, Replicate, SambaNova Systems, & Together AI.
Check model cards on the Hub: you can now, in 1 click, use inference from various providers (cf video demo)
Their inference can also be used through our Inference API client. There, you can use either your custom provider key, or your HF token, then billing will be handled directly on your HF account, as a way to centralize all expenses.
๐ธ Also, PRO users get 2$ inference credits per month!
Read more in the announcement ๐ https://huggingface.co/blog/inference-providers
- 1 reply
burtenshawย
posted
an
update
10 days ago
Post
2796
Manic few days in open source AI, with game changing development all over the place. Here's a round up of the resources:
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co/blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
- The science team at @huggingface reproduced and open source the seek r1. https://github.com/huggingface/open-r1
- @qwen released a series of models with 1 million token context! https://qwenlm.github.io/blog/qwen2.5-1m/
- SmolVLM got even smaller with completely new variants at 256m and 500m https://huggingface.co/blog/smolervlm
There's so much you could do with these developments. Especially combining them together into agentic applications or fine-tuning them on your use case.
- 1 reply
Post
2837
Today we make the biggest release in smolagents so far: ๐๐ฒ ๐ฒ๐ป๐ฎ๐ฏ๐น๐ฒ ๐๐ถ๐๐ถ๐ผ๐ป ๐บ๐ผ๐ฑ๐ฒ๐น๐, ๐๐ต๐ถ๐ฐ๐ต ๐ฎ๐น๐น๐ผ๐๐ ๐๐ผ ๐ฏ๐๐ถ๐น๐ฑ ๐ฝ๐ผ๐๐ฒ๐ฟ๐ณ๐๐น ๐๐ฒ๐ฏ ๐ฏ๐ฟ๐ผ๐๐๐ถ๐ป๐ด ๐ฎ๐ด๐ฒ๐ป๐๐! ๐ฅณ
Our agents can now casually open up a web browser, and navigate on it by scrolling, clicking elements on the webpage, going back, just like a user would.
The demo below shows Claude-3.5-Sonnet browsing GitHub for task: "Find how many commits the author of the current top trending repo did over last year."
Hi @mlabonne !
Go try it out, it's the most cracked agentic stuff I've seen in a while ๐คฏ (well, along with OpenAI's Operator who beat us by one day)
For more detail, read our announcement blog ๐ https://huggingface.co/blog/smolagents-can-see
The code for the web browser example is here ๐ https://github.com/huggingface/smolagents/blob/main/examples/vlm_web_browser.py
Our agents can now casually open up a web browser, and navigate on it by scrolling, clicking elements on the webpage, going back, just like a user would.
The demo below shows Claude-3.5-Sonnet browsing GitHub for task: "Find how many commits the author of the current top trending repo did over last year."
Hi @mlabonne !
Go try it out, it's the most cracked agentic stuff I've seen in a while ๐คฏ (well, along with OpenAI's Operator who beat us by one day)
For more detail, read our announcement blog ๐ https://huggingface.co/blog/smolagents-can-see
The code for the web browser example is here ๐ https://github.com/huggingface/smolagents/blob/main/examples/vlm_web_browser.py
- 3 replies
burtenshawย
posted
an
update
13 days ago
Post
1034
Hey ๐
I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.
OSAIResearchCommunity/README#2
I'm helping out on some community research to learn about the AI community. If you want to join in the conversation, head over here where I started a community discussion on the most influential model since BERT.
OSAIResearchCommunity/README#2
burtenshawย
posted
an
update
13 days ago
Post
1703
๐ฃ Teachers and Students! Here's a handy quiz app if you're preparing your own study material.
TLDR, It's a quiz that uses a dataset to make questions and save answers
Here's how it works:
- make a dataset of multiple choice questions
- duplicate the space add set the dataset repo
- log in and do the quiz
- submit the questions to create a new dataset
I made this to get ready for the agents course, but I hope it's useful for you projects too!
quiz app burtenshaw/dataset_quiz
dataset with questions burtenshaw/exam_questions
agents course we're working on https://huggingface.co/agents-course
TLDR, It's a quiz that uses a dataset to make questions and save answers
Here's how it works:
- make a dataset of multiple choice questions
- duplicate the space add set the dataset repo
- log in and do the quiz
- submit the questions to create a new dataset
I made this to get ready for the agents course, but I hope it's useful for you projects too!
quiz app burtenshaw/dataset_quiz
dataset with questions burtenshaw/exam_questions
agents course we're working on https://huggingface.co/agents-course
burtenshawย
posted
an
update
14 days ago
Post
2320
AI was built on side projects!
burtenshawย
posted
an
update
15 days ago
Post
3699
๐ง Work in Progress! ๐ง
๐ทโโ๏ธ We're working hard on getting the official agents course ready for the 50,000 students that have signed up.
If you want to contribute to the discussion, I started these community posts. Looking forward to hearing from you:
- smolagents unit in the agents course - agents-course/README#7
- LlamaIndex Unit in the agents course - agents-course/README#6
- LangChain and LangGraph unit in the agents course - agents-course/README#5
- Real world use cases in the agents course - agents-course/README#8
๐ทโโ๏ธ We're working hard on getting the official agents course ready for the 50,000 students that have signed up.
If you want to contribute to the discussion, I started these community posts. Looking forward to hearing from you:
- smolagents unit in the agents course - agents-course/README#7
- LlamaIndex Unit in the agents course - agents-course/README#6
- LangChain and LangGraph unit in the agents course - agents-course/README#5
- Real world use cases in the agents course - agents-course/README#8
Post
1395
we now have more than 2000 public AI models using ModelHubMixin๐ค
burtenshawย
posted
an
update
20 days ago
Post
1384
Playing with agents, and I reckon Gradio spaces make the perfect agent tools! So I wrote this guide using Gradio and smolagents:
https://huggingface.co/blog/burtenshaw/gradio-spaces-agent-tools
https://huggingface.co/blog/burtenshaw/gradio-spaces-agent-tools
Post
1226
๐ ๐ถ๐ป๐ถ๐ ๐ฎ๐
'๐ ๐ป๐ฒ๐ ๐ ๐ผ๐ ๐๐๐ ๐ฟ๐ฒ๐ฎ๐ฐ๐ต๐ฒ๐ ๐๐น๐ฎ๐๐ฑ๐ฒ-๐ฆ๐ผ๐ป๐ป๐ฒ๐ ๐น๐ฒ๐๐ฒ๐น ๐๐ถ๐๐ต ๐ฐ๐ ๐๐ผ๐ธ๐ฒ๐ป๐ ๐ฐ๐ผ๐ป๐๐ฒ๐
๐ ๐น๐ฒ๐ป๐ด๐๐ต ๐ฅ
This work from Chinese startup @MiniMax-AI introduces a novel architecture that achieves state-of-the-art performance while handling context windows up to 4 million tokens - roughly 20x longer than current models. The key was combining lightning attention, mixture of experts (MoE), and a careful hybrid approach.
๐๐ฒ๐ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
๐๏ธ MoE with novel hybrid attention:
โฃ Mixture of Experts with 456B total parameters (45.9B activated per token)
โฃ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers
๐ Outperforms leading models across benchmarks while offering vastly longer context:
โฃ Competitive with GPT-4/Claude-3.5-Sonnet on most tasks
โฃ Can efficiently handle 4M token contexts (vs 256K for most other LLMs)
๐ฌ Technical innovations enable efficient scaling:
โฃ Novel expert parallel and tensor parallel strategies cut communication overhead in half
โฃ Improved linear attention sequence parallelism, multi-level padding and other optimizations achieve 75% GPU utilization (that's really high, generally utilization is around 50%)
๐ฏ Thorough training strategy:
โฃ Careful data curation and quality control by using a smaller preliminary version of their LLM as a judge!
Overall, not only is the model impressive, but the technical paper is also really interesting! ๐
It has lots of insights including a great comparison showing how a 2B MoE (24B total) far outperforms a 7B model for the same amount of FLOPs.
Read it in full here ๐ MiniMax-01: Scaling Foundation Models with Lightning Attention (2501.08313)
Model here, allows commercial use <100M monthly users ๐ MiniMaxAI/MiniMax-Text-01
This work from Chinese startup @MiniMax-AI introduces a novel architecture that achieves state-of-the-art performance while handling context windows up to 4 million tokens - roughly 20x longer than current models. The key was combining lightning attention, mixture of experts (MoE), and a careful hybrid approach.
๐๐ฒ๐ ๐ถ๐ป๐๐ถ๐ด๐ต๐๐:
๐๏ธ MoE with novel hybrid attention:
โฃ Mixture of Experts with 456B total parameters (45.9B activated per token)
โฃ Combines Lightning attention (linear complexity) for most layers and traditional softmax attention every 8 layers
๐ Outperforms leading models across benchmarks while offering vastly longer context:
โฃ Competitive with GPT-4/Claude-3.5-Sonnet on most tasks
โฃ Can efficiently handle 4M token contexts (vs 256K for most other LLMs)
๐ฌ Technical innovations enable efficient scaling:
โฃ Novel expert parallel and tensor parallel strategies cut communication overhead in half
โฃ Improved linear attention sequence parallelism, multi-level padding and other optimizations achieve 75% GPU utilization (that's really high, generally utilization is around 50%)
๐ฏ Thorough training strategy:
โฃ Careful data curation and quality control by using a smaller preliminary version of their LLM as a judge!
Overall, not only is the model impressive, but the technical paper is also really interesting! ๐
It has lots of insights including a great comparison showing how a 2B MoE (24B total) far outperforms a 7B model for the same amount of FLOPs.
Read it in full here ๐ MiniMax-01: Scaling Foundation Models with Lightning Attention (2501.08313)
Model here, allows commercial use <100M monthly users ๐ MiniMaxAI/MiniMax-Text-01
thomwolfย
authored
a
paper
21 days ago
Post
2467
๐ช๐ฒ'๐๐ฒ ๐ท๐๐๐ ๐ฟ๐ฒ๐น๐ฒ๐ฎ๐๐ฒ๐ฑ ๐๐บ๐ผ๐น๐ฎ๐ด๐ฒ๐ป๐๐ ๐๐ญ.๐ฏ.๐ฌ ๐, and it comes with a major feature: you can now log agent runs using OpenTelemetry to inspect them afterwards! ๐
This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.
The setup is very easy, in a few lines of code.
Find a tutorial here ๐ https://huggingface.co/docs/smolagents/tutorials/inspect_runs
This interactive format is IMO much easier to inspect big multi-step runs than endless console logs.
The setup is very easy, in a few lines of code.
Find a tutorial here ๐ https://huggingface.co/docs/smolagents/tutorials/inspect_runs
- 5 replies
burtenshawย
posted
an
update
22 days ago
Post
41252
Weโre launching a FREE and CERTIFIED course on Agents!
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
https://bit.ly/hf-learn-agents
We're thrilled to announce the launch of the Hugging Face Agents course on Learn! This interactive, certified course will guide you through building and deploying your own AI agents.
Here's what you'll learn:
- Understanding Agents: We'll break down the fundamentals of AI agents, showing you how they use LLMs to perceive their environment (observations), reason about it (thoughts), and take actions. Think of a smart assistant that can book appointments, answer emails, or even write code based on your instructions.
- Building with Frameworks: You'll dive into popular agent frameworks like LangChain, LlamaIndex and smolagents. These tools provide the building blocks for creating complex agent behaviors.
- Real-World Applications: See how agents are used in practice, from automating SQL queries to generating code and summarizing complex documents.
- Certification: Earn a certification by completing the course modules, implementing a use case, and passing a benchmark assessment. This proves your skills in building and deploying AI agents.
Audience
This course is designed for anyone interested in the future of AI. Whether you're a developer, data scientist, or simply curious about AI, this course will equip you with the knowledge and skills to build your own intelligent agents.
Enroll today and start building the next generation of AI agent applications!
https://bit.ly/hf-learn-agents
- 23 replies