Thanks, Jason. These types of introductory videos are super useful. Hope you make more intro videos of other platforms and off AI in general to developers who are new to it.
Hey Jason, this introductory video on Hugging Face and its ecosystem is amazing. It helped immensely in understanding what we can do with this platform.
Ok, you deserve a subscribe! You know what... usually I don't get fascinated by people teaching this stuff because I feel that they are redundant and just doing it for views. However, you were very smooth and went out from explaining it into doing a project in nearly 3 mins with explaining, so I have to thank you BRO! KEEP IT UP PLEASE, WE NEED PEOPLE LIKE YOU!!
really great work, just discovered your channel a couple days ago. Thanks for breaking down the things to the core and putting out so much value to work with in such time periods. Please continueth👍
their product are not used by google, ms etc - they reimplement and host models made by google, ms etc so they create and managed the organisations for them - I worked there
Thank you so much, incredibly useful video! I was walking by hugging face for a long time without knowing that this is such an amazing thing. Now I have everything to start working with it right now, thanks Jason!
🎯 Key Takeaways for quick navigation: 00:00 *🤖 Hugging Face Overview* - Hugging Face is a platform for discovering and sharing AI models. - Three main parts: models, datasets, and spaces. - Models: Various AI models for tasks like image to text, text to speech, etc., hosted for immediate testing. 01:52 *📊 Datasets on Hugging Face* - Hugging Face provides datasets for training your own models. - Allows filtering and previewing datasets, useful for training custom models. 02:20 *🚀 Hugging Face Spaces* - Spaces allow users to deploy and share their AI apps. - Users can explore and interact with apps built by others, offering learning opportunities. 03:15 *🔍 Building an AI App: Step by Step* - Components: Image to text model, language model for story generation, text to speech model. - Process: Select relevant models from Hugging Face, implement in code. 04:23 *🖼️ Image to Text Model Implementation* - Utilize Hugging Face's Transformers library to access predefined tasks. - Code implementation example for converting an image to text using Hugging Face models. 05:32 *📝 Language Model for Story Generation* - Using a language model (GPT) for generating a story based on the text description. - Integration of GPT model from Hugging Face into the app. 06:42 *🔊 Text to Speech Model Integration* - Using Hugging Face's text to speech model for generating audio from the generated story text. - Implementation of the text to speech model and handling audio output. 07:53 *🎛️ App UI Development* - Building the user interface using Streamlit library. - Integrating image upload, model processing, story generation, and audio output into the UI. 08:44 *💡 Recap and Recommendation* - Recap of using Hugging Face models for various AI tasks. - Recommendation to explore Hugging Face's tasks and models for further learning. - Mention of another platform, Relevance AI, for quick AI app development. Made with HARPA AI
Thank you Jason, your video rocks! I think it would be even better if you would share the VS Code project with us (without the API keys and other personal info) ;).
Excellent breakdown of using Hugging Face for AI apps! Your step-by-step guide is incredibly helpful for both beginners and experienced developers. Thanks for sharing your expertise!
I mostly download videos but after watching this video i had to come back to give it a comment, like , subscription and just give you a watch time. not time wasted .Thank you
Very good example, thanks for sharing. FYI: I did run into a error 403 with Streamlit, possibly Windows related. I was able to resolve by creating a ./.streamlit/ folder with config.toml file, with the following details: [server] enableXsrfProtection=false enableCORS = false
Thank you! This video has been very useful for me. I've been trying to get into the AI Industry lately as I wanna get more in-depth within it (instead of using wrappers like OpenAI's APIs), and HuggingFace seems like a good place to start. Do you know where I can find more resourses?
I am getting the error "ValueError: Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length." when using the image to text model
Great vids! I’m learning a lot! What combo would you recommend using to parse and clean structured and unstructured data? For example there are 1000 real estate listings in a csv, and many do not contain a piece of data explicitly, but they may contain it in the long unstructured description…
You obviously need to know some basic coding language to input into the right areas, what resources do you suggest for a beginner to learn the basic inputs?
Though your explanation was to the point, I found it little difficult. I guess, I have to watch the video couple of times. Thanks a ton for the tutorial.🙏
Thanks for the feedback Ravi! Really appreciate, I’m improving the pace, so this is super helpful. Is there particular parts that you wish I can explain a bit better?
A Colab notebook file would help wonders to learn step by step of each concept as 5 min is too little for so much, and please don’t get me wrong you did an excellent job of inviting to your channel with the 5-min headline and delivered, I am proposing this as an solution to explain a bit better " in a format that each one can dive in their own pace without being a 30min video and it doesn’t necessarily must be the same code as the video, but a Jason AI foundations.
Great vidéos ! When you said we can use huggingface models with our own token key, does that mean we download the whole model on our personal machine? If yes how to be sure our machine is good enough ??
instead of using the openAI in the llm parameter which is present in the story_llm can we use any other... i mean i,m trying to use hugging face_hub and huggingface_pipeline but it is saying that module is not callable
Thanks, Jason. These types of introductory videos are super useful. Hope you make more intro videos of other platforms and off AI in general to developers who are new to it.
Thanks!
Hey Jason, this introductory video on Hugging Face and its ecosystem is amazing. It helped immensely in understanding what we can do with this platform.
Thank you for sharing Jason,now I have the superpower to make a model every day,well actually every 5 mins :)
Pretty dope.
Ok, you deserve a subscribe!
You know what... usually I don't get fascinated by people teaching this stuff because I feel that they are redundant and just doing it for views. However, you were very smooth and went out from explaining it into doing a project in nearly 3 mins with explaining, so I have to thank you BRO!
KEEP IT UP PLEASE, WE NEED PEOPLE LIKE YOU!!
really great work, just discovered your channel a couple days ago. Thanks for breaking down the things to the core and putting out so much value to work with in such time periods. Please continueth👍
their product are not used by google, ms etc - they reimplement and host models made by google, ms etc so they create and managed the organisations for them - I worked there
Hi
After all tutorial, found this tutorial, and I am starting understand how to use hugging face
Thanks!
Great stuff! I have been following along a few of your AI vids. All quality work! Thanks Jason. Subscribed.
thanks, jason. This is a good introductory video about huggingface
Thank you for a clear video summary and a great intro to HF! You have got me across the line.
Most of the questions I had about the huggingface ecosystem were answered in the video. Thank you for making this video.
Thank you so much, incredibly useful video! I was walking by hugging face for a long time without knowing that this is such an amazing thing. Now I have everything to start working with it right now, thanks Jason!
🎯 Key Takeaways for quick navigation:
00:00 *🤖 Hugging Face Overview*
- Hugging Face is a platform for discovering and sharing AI models.
- Three main parts: models, datasets, and spaces.
- Models: Various AI models for tasks like image to text, text to speech, etc., hosted for immediate testing.
01:52 *📊 Datasets on Hugging Face*
- Hugging Face provides datasets for training your own models.
- Allows filtering and previewing datasets, useful for training custom models.
02:20 *🚀 Hugging Face Spaces*
- Spaces allow users to deploy and share their AI apps.
- Users can explore and interact with apps built by others, offering learning opportunities.
03:15 *🔍 Building an AI App: Step by Step*
- Components: Image to text model, language model for story generation, text to speech model.
- Process: Select relevant models from Hugging Face, implement in code.
04:23 *🖼️ Image to Text Model Implementation*
- Utilize Hugging Face's Transformers library to access predefined tasks.
- Code implementation example for converting an image to text using Hugging Face models.
05:32 *📝 Language Model for Story Generation*
- Using a language model (GPT) for generating a story based on the text description.
- Integration of GPT model from Hugging Face into the app.
06:42 *🔊 Text to Speech Model Integration*
- Using Hugging Face's text to speech model for generating audio from the generated story text.
- Implementation of the text to speech model and handling audio output.
07:53 *🎛️ App UI Development*
- Building the user interface using Streamlit library.
- Integrating image upload, model processing, story generation, and audio output into the UI.
08:44 *💡 Recap and Recommendation*
- Recap of using Hugging Face models for various AI tasks.
- Recommendation to explore Hugging Face's tasks and models for further learning.
- Mention of another platform, Relevance AI, for quick AI app development.
Made with HARPA AI
Thank you Jason, your video rocks! I think it would be even better if you would share the VS Code project with us (without the API keys and other personal info) ;).
You have earned a subscriber my friend. Thanks for such an awesome tutorial.
Excellent breakdown of using Hugging Face for AI apps! Your step-by-step guide is incredibly helpful for both beginners and experienced developers. Thanks for sharing your expertise!
Jason, you are a walking GOAT! Keep going, please!
Fantastic video for someone who is starting on AI today :)
I mostly download videos but after watching this video i had to come back to give it a comment, like , subscription and just give you a watch time.
not time wasted .Thank you
You made a really superb video. Straight to topic. I felt like learning something new today. You got a new subscriber 🤗
your channel is my favorite ai source
This is so well done. Thank you Jason
Thank you! The explanation is straight to the point and easy to understand.
Very helpful tutorial for beginners, thanks and subscribed!
Excellent delivery! Great video!
Best I’ve seen so far! Thank you 🙏🏼
Thank you Nordin!
Dude, you nailed with clear cut and concise explanation. Super...!!!!
Instant sub, good work on the video
This is dope Jason! Super useful!
the way you describe is really outstanding
Best tutorial I’ve seen in a long time
Straight forward, I love it
As always, great content 😄
Dude, your content is REALLY good
Amazing Video bro. In a small time, you explained alot of things
Great video, short and to the point. Thank you!
Love your work Jason. You are an inspiration.
Thanks Jason! Looking more like this!
bro u literally created a major project in 9min
exactly haha
Really straight forward video. Thank u jason
hey jason , thanks for this video it's a really good introduction for hugging face platform !
Great content Jason - thanks so much! Subscribed!
Legendary video.
Can't thank you enough for these videos man, keep up the great work!
You're a legend mate.
Yes! 🔥🔥🔥 🚀
Thanks man. It's clear and super helpful.
Thanks, Jason. this was very informative, I hope you can make more in depth videos on HF own deploy Inference API
Very clever content idea, concentrated info. Helpful for newcommers and professionals both. Thanks
Nice! Thanks!
thanks Jason , content is really good but text is very small invisible to view in laptop screen , please zoom out while you show the code.
Great. I especially liked the summary of the code at the end.
Thank you. 新年快乐。 我钦佩中国。
So what is langchain. You put it in the title, but dont mention it
Lang chain is a framework allow us to make web apps having AI usage easily without much syntax
An application that allows us to work over llm
Great practical hands-on example! subscribed
Awesome video straight forward and informative really appreciated
great explainantion thanks a lot
@Great video! I really enjoyed learning about Hugging Face + Langchain. Thanks for sharing your knowledge!!!!!
Great tutorial! Thank you!
wow, that was really helpful. Thank you
This was so very helpful. Thank you.
Very good example, thanks for sharing.
FYI: I did run into a error 403 with Streamlit, possibly Windows related. I was able to resolve by creating a ./.streamlit/ folder with config.toml file, with the following details:
[server]
enableXsrfProtection=false
enableCORS = false
Thanks man, also showing how the transformers get it a bit wrong, like the lady is laughing hard, not smiling in love: 2:54.
Great stuff⭐
Yes, very good, to the point, top notch and cheers.
Thank you for this precious piece of content
Subscribed. great content
Thank you! This video has been very useful for me. I've been trying to get into the AI Industry lately as I wanna get more in-depth within it (instead of using wrappers like OpenAI's APIs), and HuggingFace seems like a good place to start. Do you know where I can find more resourses?
That's a very well done video, thank you! :)
Love It!!
Great tutorial!
Great tutorial, keep them coming, thank you for sharing 🎉
clear and concise video, Thanks.
Great tutorial, github link for your code would be a nice addition.
Great video !! thanks !
could you make your text editor font sizes bigger for next time was straining to see
Thanks mate, will do!
Thanks for this walkthrough tutorial
Amazing! Thanks.
I am getting the error
"ValueError: Unable to create tensor, you should probably activate padding with 'padding=True' to have batched tensors with the same length."
when using the image to text model
Pretty good thanks!
Where did you use Langchain?
Thank you sooo much❤
Great vids! I’m learning a lot!
What combo would you recommend using to parse and clean structured and unstructured data? For example there are 1000 real estate listings in a csv, and many do not contain a piece of data explicitly, but they may contain it in the long unstructured description…
How do I link the form to Blogger? If you have a video explaining the method, please give it to me
Do you mean embed the form for uploading image on your blog?
@@AIJasonZ No, I mean using a bot from huggingface in a blogger blog
helped a lot! thanks
You obviously need to know some basic coding language to input into the right areas, what resources do you suggest for a beginner to learn the basic inputs?
Just read about python, pip and installing libraries
Though your explanation was to the point, I found it little difficult. I guess, I have to watch the video couple of times. Thanks a ton for the tutorial.🙏
Thanks for the feedback Ravi! Really appreciate, I’m improving the pace, so this is super helpful.
Is there particular parts that you wish I can explain a bit better?
A Colab notebook file would help wonders to learn step by step of each concept as 5 min is too little for so much, and please don’t get me wrong you did an excellent job of inviting to your channel with the 5-min headline and delivered, I am proposing this as an solution to explain a bit better " in a format that each one can dive in their own pace without being a 30min video and it doesn’t necessarily must be the same code as the video, but a Jason AI foundations.
Great vidéos !
When you said we can use huggingface models with our own token key, does that mean we download the whole model on our personal machine? If yes how to be sure our machine is good enough ??
Great content!!!
btw which Mac model you have?
I bet it was you on the image :)
Thank you. Appreciate your content.
Perfect!! Thank you!!
instead of using the openAI in the llm parameter which is present in the story_llm can we use any other... i mean i,m trying to use hugging face_hub and huggingface_pipeline but it is saying that module is not callable
Damm good tutorial! Absolutely on point.
Wait, where did the .flac information come from?
Amazing videos !!
Awesome!
Great tutorial. But some libraries depreciated, so need to make changes accordingly. Also gpt model used in this tutorial is not working now.
So it is now obsolete...thanks
Tea time vedio ❤