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Very helpful, thanks. Have you tried giving aiXplain a try? It's very similar to OpenRouter.

I subbed and liked

Thanks so much, mate. Amazing video.

This model is returning the number of classes it was trained on and not the image embeddings.

Unexpected Response: 403 (Forbidden) - while creating the recreate_collection qclient.recreate_collection .. looks this method may get deprecated in future .

Great video! I appreciate how clearly you explained everything and connected the concepts with practical examples. The way you highlighted specific documentation points for use in the code was beneficial. You put a lot of thought into making everything accessible. I also noticed how well you balanced technical details without overwhelming the viewer, making this a solid resource for anyone exploring LLM inference. I've subscribed and look forward to more from you!

This was very helpful.

Wouldn't storing Base64 will have impact on performance?

Nitro now requires middleware to folder to be placed inside the server folder and not the root directory now... just as a heads up to everyone.

source code?

so what ?

Great content, thank you for sharing. Can you make a simpler step-by-step ChatGPT API utilization video for less advanced users? Thank you in advance :)

Hi. At 16:03, after running I am getting this error: Cell In[13], line 4 1 from transformers import AutoImageProcessor, ResNetForImageClassification 3 processor = AutoImageProcessor.from_pretrained("microsoft/resnet-50") ----> 4 model = ResNetForImageClassification.from_pretrained("microsoft/resnet-50") 6 inputs = processor( 7 images, 8 return_tensors="pt", 9 ) 10 outputs = model(**inputs) File ~\AppData\Roaming\Python\Python312\site-packages\transformers\utils\import_utils.py:1637, in DummyObject.__getattribute__(cls, key) 1635 if key.startswith("_") and key != "_from_config": 1636 return super().__getattribute__(key) -> 1637 requires_backends(cls, cls._backends) File ~\AppData\Roaming\Python\Python312\site-packages\transformers\utils\import_utils.py:1616, in requires_backends(obj, backends) 1614 # Raise an error for users who might not realize that classes without "TF" are torch-only 1615 if "torch" in backends and "tf" not in backends and not is_torch_available() and is_tf_available(): -> 1616 raise ImportError(PYTORCH_IMPORT_ERROR_WITH_TF.format(name)) 1618 # Raise the inverse error for PyTorch users trying to load TF classes 1619 if "tf" in backends and "torch" not in backends and is_torch_available() and not is_tf_available(): ImportError: ... If you really do want to use PyTorch please go to pytorch.org/get-started/locally/ and follow the instructions that match your environment. Please help

Can you provide a link to the code / notebook for the flight search API function? I see you posted the lookup album but not the flight search.

You won my heart of subscriber Sir ❤❤

Sir, How can custom collect datasets in our own local language and import in our GPT model ?

Very nice video

Really helpful in getting me started with the API, thanks!

Your explanation was really helpful. One small request, Could you please create a tutorial video on integrating the Assistants API with Voiceflow for a chatbot? Also, please include instructions on how to store user details in a Google Sheet.

Very informative. Do you have a githup account?

This was very helpful, thank you!

Excellent. Working for me even post Assistant upgrades. How did you integrate your Streamlit app? Do you have this on Git too?

Is there any better flight API than the one you are using?

hello could you help me understand some things

Interesting, but why use base64? isn't that going to take up 4X the space than the original image's bytes ?

really nice video, explaining things very well. The thing i like the best is that Sam talks at normal speed, and explains everything very simple with clear examples. Thnx

Thanks a lot for the clear and very informative explanation

then why we use agent ? use openai function calling + prompt eng. get more control

Wonderful, finally someone who doesn’t use a chatbot mechanism and explains how this works!!!

Great! could you post a link for the entire project in streamlit?

Great video men. Appreciate your sharing

Running through the exact same steps you did (Wikipedia movie plots from Kaggle, limiting dataset to 1000 entries), it looks like the training is going to take about 1 hour and 45 minutes. I'm actually not that well versed in the hardware side of computers, but I can see my CPU is maxed out using an AMD Ryzen 7 3700x 8-core processor. Would this be the limiting factor causing the 7x time increase? Great tutorial, btw!

Hi, thank you so much for the wonderful content. Could you please share the code? Thank you

Where is the full code?

Bro imma be honest with you… i have seen a lot of videos trying to to understand how to integrate custom functions in my assistant, but none of them worked for me until i saw your video🙏🙏 thanks a lot, you have great teaching skills 🤙 u r the best

I watched about 8 other videos trying to figure out this process. This one cracked it for me. Nice one.

really need the code my mannn

really really need the code my mannnnnnn

really really need the code my mannn

all you need in 12 minutes - thanks for this !!

Please Do more Videos With OpenAI, API, Tools and Assistant API. Worth it.

Amazing Video, Got my Respect! Thank you for that!

It’s sad how there is barely any likes and comments let’s change that

Hello. Congratulations!! Great explanation and content!! I have one question.. Do you know what we should do so the AI Assistant doesn't show the source? In your example it shows as the [7+source], and in the assistant that I am working on it also shows that way.

tutorial not for beginners

Thank you very much❤

Your link to the github repo is pointing to another project

how can i call function just like you? In common, we add message in thread, but this video send instruction to run. help me. and your notebook link is not collect (other file)