End To End LLM Project Using LLAMA 2- Open Source LLM Model From Meta
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- Опубликовано: 19 июн 2024
- Blog Generation Platform Code: github.com/krishnaik06/Comple...
The Llama 2 release introduces a family of pretrained and fine-tuned LLMs, ranging in scale from 7B to 70B parameters (7B, 13B, 70B). The pretrained models come with significant improvements over the Llama 1 models, including being trained on 40% more tokens, having a much longer context length (4k tokens 🤯), and using grouped-query attention for fast inference of the 70B model🔥!
Time Stamp
00:00:00 Introduction And Agenda
00:02:36 Introducing LLama 2 Fro Meta
00:05:08 LLama 2 Research paper
00:10:20 How To Download LLama 2 model
00:13:18 End To End Blog Generation LLM Platform
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AI field is really becoming dynamic.Lot of changes are coming from Traditional Machine Learning to Generative AI.This field is changing dynamically and we need to update ourself as we go ahead.
Join my telegram group where I post and discuss these types of content.Happy Learning!!
Make sure you have telegram installed in it.
t.me/+V0UeLG8ji-F8ThNb
hi, do we have an open source model which can help us in selecting something out of the available list based on requirements...... or any other way
Sir we don't want to use pre-trained model, we want to fine tuning these models with our custome data.
Sir, I have a basic question related to Prompting. Does any learning (with model weight update) happen during Prompting? If not, then how does the model learn from Few Shot Prompting?
Your videos are awesome, If you kindly make a video on RLHF with the code, it will be greatly helpful.
hi sir could you please help me with the erro i'm facing when run the model
File "C:\Users\tarun\anaconda3\envs\venv\lib\site-packages\streamlit
untime\scriptrunner\script_runner.py", line 534, in _run_script
exec(code, module.__dict__)
File "C:\Users\tarun\llm\app.py", line 57, in
st.write(getllamaresponse(input_text,no_words,blog_style))
File "C:\Users\tarun\llm\app.py", line 28, in getllamaresponse
response=llm(prompt.format(style=blog_style,text=input_text,n_words=no_words))
File "C:\Users\tarun\anaconda3\envs\venv\lib\site-packages\langchain_core\prompts\prompt.py", line 132, in format
return DEFAULT_FORMATTER_MAPPING[self.template_format](self.template, **kwargs)
File "C:\Users\tarun\anaconda3\envs\venv\lib\string.py", line 161, in format
return self.vformat(format_string, args, kwargs)
File "C:\Users\tarun\anaconda3\envs\venv\lib\site-packages\langchain_core\utils\formatting.py", line 18, in vformat
return super().vformat(format_string, args, kwargs)
File "C:\Users\tarun\anaconda3\envs\venv\lib\string.py", line 165, in vformat
result, _ = self._vformat(format_string, args, kwargs, used_args, 2)
File "C:\Users\tarun\anaconda3\envs\venv\lib\string.py", line 205, in _vformat
obj, arg_used = self.get_field(field_name, args, kwargs)
File "C:\Users\tarun\anaconda3\envs\venv\lib\string.py", line 270, in get_field
obj = self.get_value(first, args, kwargs)
File "C:\Users\tarun\anaconda3\envs\venv\lib\string.py", line 227, in get_value
return kwargs[key]
Thank you Krish, please continue to create contents like this!
I am happy that i found you in RUclips. You are doing a great job. Keep going.
please keep doing such videos!! kudos to your work.. and patience.
Thank you very much, You have made my Project way easier by the way you explained everything!
Thank you Krish, I am from QA background but your videos helped me a lot to learn AI. you are my AI Guru :)
wow @krishnaik06!!! Thank you for this. Just in time!! I had been looking for Llama tutorials and here you are :)
Hi Krish,
I like this approach of the training. It gives you a good background
I really Love the way you explain Krish , I am a big Fan of your teaching style and content tooo..
Hi Krish, Thank you so much for uploading all best resources to keep us upto date. I am ur follower from many years. because of your resources I got into Datascience job 2 year back and upgrading myself by ur videos and encouragement. Thank you
that's great 😊
thank you sir ,your video have every details we required for using a llm model.
Thanks for making this video, which is very much useful in implementing real world LLM use cases, OfCourse we are using currently.
Thanks a lot for your amazing videos! I learn a lot from you. Love from Togo!
Thank you for making this amazing video on open source llama2 , really helpful for free gpt programmers
This is a great way to learn Krish , keep doing your great work
Sir your knowledge, confidence and content are phenomenal. Salute to great efforts. :)
Mazza aa gaya, Sir. Really well structured! Thanks a lot!
This is my excellent learning till today. Thank you so much sir.
Hi Krish,
Thanks for your educational videos.
ALWAYS BIG THINGS AND EPLAINATIONS , THANK YOU
It already met your target. Keep teaching!
Thankyou for teaching us... Very helpfull for other domains too..
Really cool tutorial. You made it easy to understand>>>Hats off
please keep going with this format for other models as well. Thanks
Great content! thank you Krish
Hello sir, PW student here. First of all, I would like to thank you for the amazing ML classes you took for PW. They were really amazing and the way you taught were very easy and very simple. Looking forward to learning the rest from you here.
What is pw
@@riyayadav8468😮 Physics wallah
@@riyayadav8468, Physics Wallah
i learn and know about data science and ai ...thank you very much sir your video such a very help me for work in project and also understand about next workplace in ai and data science ...
Hey Krish, this is a treasure please do videos on fine-tuning, and deployment also.
Sir its a very good video to learn about LLM and to know how to generate end to end projects from these models
Thank you for sharing this valuable knowledge
Thank you so much. I love all your Videos.
Great explanation sir, much love from sri lanka❤
Great Sir, I am a Full Stack Developer, I am looking forword to involve in Data Science field, and I am going to follow your The-Grand-Complete-Data-Science-Materials and Roadmap-To-Learn-Generative-AI-In-2024.
My 2024 target is only follow you .😊
Thank you GURU JI🙏
Thank you sir for this wonderful and fruitful video
Eagerly waiting for fine-tuning such model on our own data.
Amazing video, Thanks Krish
Great lecture, and great effort than you very much.
Thank you for uploading end to end project, can you make video on other LLMs like falcon, Jurassic, Lama index
Quite Good Sir And we Want More Like This
haha your predicted stats have been smashed. cheers to seeing more videos around working with LLM's
Hello Sir, Thank you for such a quick and Concise tutorial on Llama.
I watched till the end and did a side-by-side code my myself till the end BUT the output in the StreamLit app is, taking forever to generate the required text
Thank you so much for amazing contents
It's really initiative. You are doing very well
Good project. Thanks Krish
Hi Sir, fantastic video. Keep it up
Hi Krish! I learned data science through your videos and am now working as a data scientist. BIg Thanks! Can we use QA answer models without OpenAI or anything free of cost?
Thanks a lot sir nice initiative please keep it up
Incredible Stuff Keep Going ✌
Thanks for making this video
Thanks for the wonderful vedio, Can you also create a vedio on deployment of such large models
Thank you for the great video
Can you please make a tutorial on using LLM's to augment textual data on private data. Where no data will be re-used to train LLM models. That would be a great help!
awsome ..thank you so much
you videos deserve more than 100000000 comments and likes
It is really a nice initiative by you
Thanks krish for this series, can you also make video on huggingface RagModel and RagRetriever for generative AI
Thanks Krish
Thanks for the video sir .....❤
one of the best video
That was really helpful.
Thanks Krish this way ahead ChatGPT LLM
Thanks for the knowledge1
Thank you
Superb video. Thanks, Krish. Just a question: How do you use a finetuned model adapter to build the application? A video on this would be greatly helpful.
best video on LLama
Awesome
I hope to see your future video tutorials of ai-based chatbot using python. Thanks
Thank you❤ Sir
sir please videos on using pretrained models also like yolo ,coco ssd so that we can dive deep into how to build projects using that
NICE WORK SIR
Thank You Sir
good sir very helping this vedios nice nice......
awesome
Amazing.......... it is really good
Really great tutorial
Hi Sir, Please create a video on Optimization and quantization techniques for LLM
Thanks!
amazing content
I hope the next video would be as open source as possible . If you are going to use cloud please make sure its free so that we can code along with you. Again thank you so much
This is nice. However can you create some end to end video project like network log analysis or db log analysis for enterprise customers so that it is more meaningful
Good project🎉
It's fantastic session Krish. Please help to clarify below
Now we are crafting the input to extract what we need in a specific way. It's kind of a prompt engineering technique correct ?
For your effort🎉
Really enjoyed while learning from you. Could you please tell us how we can use this LLM'S API?
It was a great project! Could you please create a video on integrating the same project with an API?🙂
Did you find any other resources about it? Because I don't want to download such big model into my local.
Great video, love from Pakistan sir...
great video!
Finally meta also created their llm models
This is a Great video.
but please make a video about on a how to connect NVidia gpu with conda oro python
Nice initiative ❤
thanks 😍
helpfull video
No need of stopping in terminal, if you refresh the webpage it will fetch the latest changes which you have made in the visual studio
You made it simple
Great
Sir, it's a very important video. Nice job, Sir. Can I add to my website if I used WordPress?
I want to understand what is the future of LLM and GenAI for developers what kind of work we will get ? Using other AI models to crate chat bot, images, videos ??
Best video ❤❤
Hi Krish, nice tutorial/content as always. I thought lowering the temperature would make the response more deterministic and less random, isn't it so?
yes but even as low as 0.01 can make it give different outputs on each run
bro lets goo