Gaurav Vij - MonsterAPI Blog (Page 3)

Gaurav Vij

Chief experimentation officer @ MonsterAPI

What is vLLM and How to Implement It?

What is vLLM and How to Implement It?

Serving large language models (LLMs) in production environments poses significant challenges, including high memory consumption, latency issues, and the need for efficient resource management. These challenges often result in suboptimal performance and scalability problems, hindering the deployment of LLMs in real-world applications. vLLM addresses these challenges by optimizing memory management

How to Fine-tune a Large Language Model

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How to Fine-tune a Large Language Model

A comprehensive guide with fundamental concepts on how to fine-tune a large language model in 2024.

Unsloth & SDPA Integrated in MonsterAPI for 2x LLM Finetuning Performance Boost

Unsloth integration

Unsloth & SDPA Integrated in MonsterAPI for 2x LLM Finetuning Performance Boost

Finetuning LLMs to fit your business use case is crucial for building powerful Generative AI applications. Whether you’re developing a ChatGPT alternative or Chatbot for customer support or an email copywriting AI model, finetuning plays a critical role in improving the performance and quality for your specific application needs.

Finetuning LLMs with MonsterTuner

How to Fine-tune Open Source AI Models like LlaMa, Mistral, SDXL

Fine-tuning is a technique in machine learning used to adapt a pre-trained model to a new and more specific task. It's like taking a highly trained athlete and giving them specialized coaching to excel in a particular sport. In this blog, we will explore the various methods to

Stable Diffusion 3 - Latest Image Generation Model that beats MidJourney, DALL-E, & Google ImageFX

Stable Diffusion 3

Stable Diffusion 3 - Latest Image Generation Model that beats MidJourney, DALL-E, & Google ImageFX

Stable Diffusion 3 (SD3) is the latest text-to-image generation model from Stability AI, designed to create highly realistic and detailed images from textual descriptions. This model ranges from 800 million to 8 billion parameters, surpassing its predecessors in terms of realism, detail, and color accuracy. Stable Diffusion 3 Performance Stable

What is LORA and Q-LORA Finetuning?

What is LORA and Q-LORA Finetuning?

Low-Rank Adaptation (LoRA) and its variant, Quantized Low-Rank Adaptation (Q-LoRA)—significantly improve how LLMs are fine-tuned and deployed.

Finetuning Llama-3 with MonsterGPT

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Finetuning Llama-3 with MonsterGPT

Llama-3 currently holds the top position among open-source large language models (LLMs). On the Chatbot Arena Leaderboard, it leads the open-source category by a significant margin, with no comparable rivals. The performance gap between Llama-3 and GPT-4 is surprisingly narrow, and with its next release Llama-3 400B it is expected

MonsterAPI is Now Integrated With Portkey - Here’s Everything You Need to Know

MonsterAPI is Now Integrated With Portkey - Here’s Everything You Need to Know

MonsterAPI is now integrated in Portkey, making it easier for developers to route LLM text generation requests directly to our cost-effective, scalable LLM APIs while using Portkey SDK.

Txt2Img API Model Upgrade

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Txt2Img API Model Upgrade

We’ve transitioned from Stable Diffusion v1.5 to the Pix-Art-Sigma model. PixArt-Σ is a cutting-edge Diffusion Transformer model (DiT) that directly generates images in stunning 4K resolution. This upgrade brings a remarkable leap in quality, offering images with higher fidelity and better alignment with text prompts compared to its

Deploying Llama 3 with MonsterGPT

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Deploying Llama 3 with MonsterGPT

Llama-3 currently holds the top position among open-source large language models (LLMs). On the Chatbot Arena Leaderboard, it leads the open-source category by a significant margin, with no comparable rivals. The performance gap between Llama-3 and GPT-4 is surprisingly narrow, and with its next release, Llama-3 400B it is expected

Photomaker Optimization: Faster API and Lower Costs

Photomaker Optimization: Faster API and Lower Costs

We've significantly improved our Photomaker API, achieving a remarkable 45% increase in speed compared to the previous version and cutting serving costs by nearly 50%. These enhancements greatly enhance the efficiency and affordability of our services, providing users with faster responses and lower expenses. To achieve this, we&

What is LLaMa 3 and How It Differs from LLaMa 2

What is LLaMa 3 and How It Differs from LLaMa 2

What is LLaMa 3 and How It Differs from LLaMa 2 The world of large language models (LLMs) is constantly evolving, with new advancements pushing the boundaries of what's possible. Meta's recent release of LLaMa 3 has sparked significant interest, particularly compared to its predecessor, LLaMa

SDXL Optimization: Faster API and Lower Costs

SDXL Optimization: Faster API and Lower Costs

We have successfully optimized our SDXL API, achieving a remarkable 45% increase in speed compared to our previous API and a nearly 50% reduction in serving costs. These improvements significantly elevate the efficiency and affordability of our services, benefiting users with super-faster responses and lower expenses. We've introduced

How to Choose the Right GPU for Finetuning LLMs?

How to Choose the Right GPU for Finetuning LLMs?

When fine-tuning Large Language Models (LLMs), selecting the right Graphics Processing Unit (GPU) is crucial. The ideal GPU can make or break the performance of your LLM fine-tuning tasks. Not only does it impact the performance you’ll get, but it will also decide how much you’ll be spending

Step-by-Step Guide to Finetuning SDXL

sdxl finetuning

Step-by-Step Guide to Finetuning SDXL

Today, we're super excited to announce the addition of fine-tuning support for SDXL 1.0, SD 2, and SD 2-1. Fine-tuning helps you to train SDXL on specific objects or styles, enabling the creation of a new model capable of generating images tailored to those objects or styles.

Gemma-2B LLM fine tuned on MonsterAPI outperforms LLaMA 13B on Maths reasoning

Gemma-2B LLM fine tuned on MonsterAPI outperforms LLaMA 13B on Maths reasoning

In this case study, we explore how a smaller, fine-tuned model like Gemma-2B can perform better than larger models while being far more efficient and fast. Using MonsterAPI's No-Code LLM fine-tuner - MonsterTuner, we finetuned Gemma-2B specifically for mathematical reasoning tasks, and we achieved a 68% performance boost

Introduction to LLMs - What Are LLMs, How Do LLMs Work?

Introduction to LLMs - What Are LLMs, How Do LLMs Work?

Overview of Large Language Models Large language models (LLMs) have revolutionized how AI models work. Large Language Models are similar to human intelligence. They are used for several things, including academia, tech, entertainment, and the research community. LLMs are trained on extensive data sets, they utilize intricate architectures like stack

Build a Retrieval-Augmented Generation ChatBot in 10 Minutes using MonsterAPI

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Build a Retrieval-Augmented Generation ChatBot in 10 Minutes using MonsterAPI

Retrieval Augmented Generation (RAG) is a technique that generates answers to pre-existing queries by combining pre-established rules or parameters (non-parametric memory) with external data from the internet (parametric memory). By responding in natural language conversations with contextually relevant responses, RAG bots are revolutionizing user interactions. We'll dive into

How to fine-tune a Large Language Model (LLM) and deploy it on MonsterAPI

How to fine-tune a Large Language Model (LLM) and deploy it on MonsterAPI

Learn how to fine-tune an LLM without code using MonsterAPI’s no-code LLM finetuning and deploying it with just 1 click using Monster Deploy service. Using this service anyone can deploy an LLM with or without a LoRA Adapter with just a few clicks or commands. 💡Recently we did a

Outperforming SOTA LLMs for Less than the Cost of a Coffee with Monster Tuner

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Outperforming SOTA LLMs for Less than the Cost of a Coffee with Monster Tuner

Finetuning Mistral 7B LLM using Monster Tuner to outperform SOTA LLMs like Falcon and Zephyr.

Reshaping Education using AI powered Speech Processing for Underprivileged Youth

Unlocking Infinite Possibilities in Language Learning: Nudge & Monster API's Symphony of Innovation India is a diverse nation, home to numerous languages, dialects, and cultures. Within this diversity, English holds a vital position, acting as a tool for socioeconomic advancement and global communication. The/Nudge is an institute

Finetuning LLaMA 70B with No-Code: Results, Methods, and Implications

Finetuning LLaMA 70B with No-Code: Results, Methods, and Implications

In this blog post, we will demonstrate how you can effortlessly fine-tune the LLaMA 2 - 70B model at a fraction of the cost of $57.75 with just a few clicks, utilising the No-Code LLM-Finetuner from Monster API. Download the Fine-Tuned Model weights from Hugging Face Overview of LLaMa

MonsterAPI is now Integrated with Llama Index!

MonsterAPI is now Integrated with Llama Index!

In the ever-evolving realm of artificial intelligence, simplicity in accessing Language Models (LLMs) is essential. Developers and businesses continuously seek ways to harness the immense potential of these models to enhance their AI-driven applications. The combination of MonsterAPI with llama_index provides an invaluable solution to businesses for questioning meaningful

Text to Image SDXL API Guide

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Text to Image SDXL API Guide

Let's get started with our guide on using the Text-to-Image SDXL API from Monster API, powered by Stable Diffusion SDXL 1.0. This guide is designed to help developers smoothly integrate this technology into their applications. MonsterAPI gives developers access to powerful AI models, making it easier to

How to Fine-Tune GPT-J on Alpaca GPT-4

How to Fine-Tune GPT-J on Alpaca GPT-4

We fine-tuned the GPT-J model with our MonsterAPI's MonsterTuner and the results were impressive. The GPT-J model comes with an impressive 6 billion parameters, making it an incredible option for fine-tuning for multiple use cases. We used the Alpaca GPT-4 dataset, which contains a collection of instructions paired