LLM API Rate Limits & Robust Applications Development

 🚀 LLM API Rate Limits & Robust Applications Development 🚀

When building robust applications with Large Language Models (LLMs), one of the key challenges is managing API rate limits. These limits, like requests per minute (RPM) and tokens per minute (TPM), are crucial for ensuring fair use but can become a bottleneck if not handled properly.

💡 For instance, the Gemini API has specific rate limits depending on the model you choose. For the gemini-1.5-pro, the free tier allows only 2 RPM and 32,000 TPM, while the pay-as-you-go option significantly increases these limits to 360 RPM and 4 million TPM. You can see the full breakdown here [1].

The LLM providers, like OpenAI and Google, impose these limits to prevent abuse and ensure efficient use of their resources. For example, OpenAI's guidance on handling rate limits includes tips on waiting until your limit resets, sending fewer tokens, or implementing exponential backoff [2]. However, this doesn’t mean you’re left in the lurch. For instance, Google’s Gemini API offers a form to request a rate limit increase if your project requires it [3].

🔍 Handling Rate Limits Effectively:

• 💡 Automatic Retries: When your requests fail due to transient errors, implementing automatic retries can help keep your application running smoothly.
• 💡 Manual Backoff and Retry: For more control, consider a manual approach to managing retries and backoff times. Check out how this can be done with Gemini API [4].

At Murat Karakaya Akademi (https://lnkd.in/dEHBv_S3), I often receive questions about these challenges. Developers are curious about how to effectively manage rate limits and ensure their applications are resilient. In one of my recent tutorials, I discussed these very issues and provided strategies to overcome them.

💡 Interested in learning more? Visit my YouTube channel, subscribe, and join the conversation! 📺

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#APIRateLimits #LLM #GeminiAPI #OpenAI #MuratKarakayaAkademi

[1] Full API rate limit details for Gemini-1.5-pro: https://lnkd.in/dQgXGQcm
[2] OpenAI's RateLimitError and handling tips: https://lnkd.in/dx56CE9z
[3] Request a rate limit increase for Gemini API: https://lnkd.in/dn3A389g
[4] Error handling strategies in LLM APIs: https://lnkd.in/dt7mxW46

🚀 What is an LLM Inference Engine?

I've recently received questions about LLM inference engines on my YouTube channel, "Murat Karakaya Akademi." This topic is becoming increasingly important as more organizations integrate Large Language Models (LLMs) into their operations. If you're curious to learn more or see a demonstration, feel free to visit my channel (https://www.youtube.com/@MuratKarakayaAkademi).

🚀 What is an LLM Inference Engine?

An LLM inference engine is a powerful tool designed to make serving LLMs faster and more efficient. These engines are optimized to handle high throughput and low latency, ensuring that LLMs can respond quickly to a large number of requests. They come with advanced features like response streaming, dynamic request batching, and support for multi-node/multi-GPU serving, making them essential for production environments.

Why Use Them?

• 🎯 Simple Launching: Easily serve popular LLMs with a straightforward setup [1].
• 🛡️ Production Ready: Equipped with distributed tracing, Prometheus metrics, and Open Telemetry [2].
• ⚡ Performance Boost: Leverage Tensor Parallelism, optimized transformers code, and quantization techniques to accelerate inference on multiple GPUs [3].
• 🌐 Broad Support: Compatible with NVIDIA GPUs, AMD and Intel CPUs, TPUs, and more [1].

Examples include:

• vLLM: Known for its state-of-the-art serving throughput and efficient memory management [1].
• Ray Serve: Excellent for model composition and low-cost serving of multiple ML models [2].
• Hugging Face TGI: A toolkit for deploying and serving popular open-source LLMs [3].

#LLM #MachineLearning #AI #InferenceEngine #MuratKarakayaAkademi

References: [1] What is vLLM? https://github.com/vllm-project/vllm
[2] Ray Serve Overview https://docs.ray.io/en/latest/serve/index.html?_gl=1*14i4ooq*_gcl_au*MTE0Mjg5OTE0Ni4xNzI0NjY5MTkx

[3] Hugging Face Text Generation Inference https://huggingface.co/docs/text-generation-inference/en/index 

Text Generation in Deep Learning with Tensorflow & Keras: Fundamentals

Text Generation in Deep Learning with Tensorflow & Keras: Fundamentals

This tutorial is the first part of the “Text Generation in Deep Learning” series. We will cover all the topics related to Text Generation with sample implementations in Python Tensorflow Keras. You can access the codes, videos, and posts from the below links. In this part, we will learn the Fundamentals of Text Generation in Deep Learning.

You can access to all parts of the Deep Learning with Tensorflow & Keras Series at my blog muratlkarakaya.net. You can watch all these parts on the Murat Karakaya Akademi YouTube channel in ENGLISH or TURKISH. You can access the complete Python Keras codes in the video description of each part.

If you would like to learn more about Deep Learning with practical coding examples, please subscribe to my YouTube Channel or follow my blog on muratkarakaya.net. Please, turn on notifications to notify you when new parts are uploaded.

Photo by Peter Herrmann on Unsplash

 

Character Level Text Generation with an LSTM Model

This tutorial is the fifth part of the “Text Generation in Deep Learning with Tensorflow & Keras” series. In this series, we have been covering all the topics related to Text Generation with sample implementations in Python, Tensorflow & Keras. 

In this tutorial, we will focus on how to build a Language Model using Keras LSTM layer for Character Level Text Generation. First, we will download a sample corpus (text file). After opening the file, we will apply the TensorFlow input pipeline that we have developed in Part B to prepare the training dataset by preprocessing and splitting the text into input character sequence (X) and output character (y). Then, we will design an LSTM-based Language Model and trai n it using the train set. Later on, we will apply several sampling methods that we have implemented in Part D to generate text and observe the effect of these sampling methods on the generated text. Thus, in the end, we will have a trained LSTM-based Language Model for character-level text generation with three sampling methods.

You can access all the parts of the Text Generation in Deep Learning with Tensorflow & Keras tutorial series on my blog at muratkarakaya.net. You can watch all these parts on the Murat Karakaya Akademi channel on YouTube in ENGLISH or TURKISH. You can access this Colab Notebook using the link.

If you would like to learn more about Deep Learning with practical coding examples, please subscribe to Murat Karakaya Akademi YouTube Channel or follow my blog at muratkarakaya.net. Do not forget to turn on notifications so that you will be notified when new parts are uploaded.

If you are ready, let’s get started!

Photo by Jan Huber on Unsplash

Character Level Text Generation with an Encoder-Decoder Model

Character Level Text Generation with an Encoder-Decoder Model

This tutorial is the sixth part of the “Text Generation in Deep Learning with Tensorflow & Keras” series. In this series, we have been covering all the topics related to Text Generation with sample implementations in Python, Tensorflow & Keras. 

After opening the file, we will apply the TensorFlow input pipeline that we have developed in Part B to prepare the training dataset by preprocessing and splitting the text into input character sequence (X) and output character (y). Then, we will design an Encoder-Decoder approach with Bahdanau Attentionas the Language Model. We will train this model using the train set. Later on, we will apply several sampling methods that we have implemented in Part D to generate text and observe the effect of these sampling methods on the generated text. Thus, in the end, we will have a trained Encoder Decoder-based Language Model for character-level text generation with three sampling methods.

You can access to all parts of the Deep Learning with Tensorflow & Keras Series at my blog muratlkarakaya.net. You can watch all these parts on the Murat Karakaya Akademi YouTube channel in ENGLISH or TURKISH. You can access the complete Python Keras code here.  

If you would like to learn more about Deep Learning with practical coding examples, please subscribe to Murat Karakaya Akademi YouTube Channel or follow my blog on muratkarakaya.net.  Do not forget to turn on notifications so that you will be notified when new parts are uploaded.

If you are ready, let’s get started!

Last updated on 25th March 2022.

Photo by Emile Perron on Unsplash

 

Fundamentals of Text Generation

Author: Murat Karakaya
Date created: 21 April 2021
Last modified: 19 May 2021
Description: This is an introductory tutorial on Text Generation in Deep Learning which is the first part of the “Controllable Text Generation with Transformers” series

Accessible on:

• YouTube in English
• YouTube in Turkish
• Blogger
• Github pages
• Github Repo
• Google Colab

Photo by Markus Spiske on Unsplash

 

Fundamentals of Controllable Text Generation

Author: Murat Karakaya
Date created: 21 April 2021
Last modified: 24 May 2021
Description: This is an introductory tutorial on Controllable Text Generation in Deep Learning which is the second part of the “Controllable Text Generation with Transformers” series. This series will focus on developing TensorFlow (TF) / Keras implementation of Controllable Text Generation from scratch. You can access all these parts from my blog at muratkarakaya.net.

Before getting started, I assume that you have already reviewed:

• the tutorial series “Text Generation methods in Deep Learning with Tensorflow (TF) & Keras”
• the tutorial series “Sequence-to-Sequence Learning”
• the previous parts in this series on my blog at muratkarakaya.net

Please ensure that you have completed the above tutorial series to easily follow the below discussions.

Accessible on:

• YouTube in English
• YouTube in Turkish
• muratkarakaya.net
• Github pages
• Github Repo
• Google Colab

Photo by Chris Leipelt on Unsplash