Gaurav Vij

Chief experimentation officer @ MonsterAPI

Apple intelligence

Apple Intelligence Unveiled: The Next-Gen AI Driving Personalized Experiences

Apple intelligence has taken the tech world by storm. Apple has showcased Apple Intelligence's incredible capabilities. Here's everything you need to know about Apple Intelligence.

llm deployment

Deploying Large Language Models: Navigating the Unknown

Deploying a large language model to fit a use case can be extremely challenging. Here are all the best practices to consider during LLM deployment.

gemma 2 2b

Fine-tuning Gemma-2–2B-it for Translation

In this guide, we're showing you how to fine-tune a Gemma 2 2B model for English to Hindi translation.

perplexity score

Using Perplexity to eliminate known data points

In this guide, we're covering the most reliable metric to determine how important are data points in the cluster for training an LLM.

dataset thinning

Dataset Thinning for faster fine-tuning of LLMs

The quantity of the dataset is often confused with the quantity. Datasets with large corpus of data aren't the best when it comes to fine-tuning. Here's how to speed up fine-tuning and improve the performance of your model with dataset thinning.

instruction pretraining

Instruction Pre-Training of Language models using Monster-API

Pre-training is a crucial step in the development of large-scale language models, forming the bedrock upon which their language understanding and generation capabilities are built.

llm leaderboard

Top 12 LLM Leaderboards to Help You Choose the Right Model

Here's our pick of the best-rated open LLM leaderboards. With these models, you can choose the right model for your AI model.

supervised finetuning

Supervised vs Unsupervised LLM Finetuning: A Comprehensive Guide

Choosing between supervised LLM finetuning and unsupervised LLM finetuning can be a challenge. In this guide, we're helping you make an informed decision.

open source LLMs

9 Top Open-Source LLMs for 2024 & Their Use-Cases

In this post we've covered all the top-rated open source LLMs. We'll keep updating this list as new models drop.

gemma 2b

Fine-tuning Google Gemma 2B: A Case Study in Model Finetuning and Optimization

In this guide, we're exploring the performance boost and optimization of Google's Gemma 2B base model by fine-tuning it using MonsterTuner.

common LLM finetuning mistakes

Common Large Language Model Fine-tuning Mistakes to Avoid

Fine-tuning an LLM can be tricky if you don't have the right knowledge. Here are some common mistakes you should avoid while fine-tuning a large language model

Flux InPainting

Step-by-Step Guide to Deploying Flux Docker Image

In this step-by-step guide, we'll teach you how to deploy and use a Flux Inpaint docker image with just a few clicks on MonsterAPI.

text guided inpainting

Text guided fashion clothes image inpainting on MonsterAPI

Using MonsterAPI's one click deployment, you can host a text-guided image inpainting service and edit your fashion images with simple text-based instructions.

Grokkfast

Accelerating Learning with Grokkfast: Now Available in MonsterAPI

Grokkfast is designed to speed up the generalization process in neural networks, particularly in scenarios where traditional optimizers might struggle or take longer to converge.

deploying a fine-tuned LLM

How to Host a Fine-Tuned LLM?

Hosting a fine-tuned LLM can be a major challenge because of a range of GPU-infra hosting options and technical problems. In this blog we'll cover how to deploy your fine-tuned LLM with a single click.

llm finetuning

Choosing the Right LLMs & Fine-Tuning for Text Summarization & Code Generation?

In this blog, we’ll be walking you through the process of choosing the right LLM for text summarization and code generation and how to fine-tune them on MonsterAPI.

LLaMa 3.1 8B

Fine-tuning LLama 3.1 8B and Outperforming the Competition

Using MonsterAPI's no-code LLM fine-tuner, MonsterTuner, we fine-tuned the Llama 3.1 base model and outperformed larger models.

Retrieval-Augmented Generation

RAG vs Fine-Tuning: Choosing the Right Approach for Your LLM

RAG involves combining information retrieval with generative language models. Fine-tuning includes training a pre-trained LLM on a specific dataset to suit a particular task. Here's when to use RAG vs Fine-tuning.

RoPE Scaling

Enhancing LLM Context Length with RoPE Scaling

Scaling is a fundamental necessity in the development and application of Large Language Models (LLMs) for several compelling reasons.

Apple OpenELM

Everything you need to know before fine-tuning Apple’s Open ELM

In this blog post, we will explore the key features of OpenELM, its potential implications for the field of natural language processing, and how to fine-tune an open-ELM model on your data using MosterAPI.

Haystack RAG

Haystack x MonsterAPI: Powerful SLMs at your fingertips

By integrating MonsterAPI with Haystack, users can tap into large language models to build state of the art RAG pipelines for their chatbots and Agents.

llama 3.1

LLaMa 3.1 405B vs GPT4o - Head-to-Head Comparison

Comparison of LLaMa 3.1 405B and gpt-4o on parameters like mathematics, economics, linguistic understanding, and more.

Data augmentation

Enhancing Language Model Fine-tuning with LLM Data Augmentation

Fine-tuning large language models (LLMs) for specific applications can sometimes be constrained by the limited availability of targeted data. This is where data augmentation steps in , allowing developers to expand their existing limited datasets and improve model performance without the need for manual data collection and wrangling efforts. Here we

Deploying ComfyUI Docker Image on MonsterAPI

Deploying the ComfyUI Docker image to MonsterAPI simplifies the process of hosting a GPU-powered ComfyUI service.

negative prompts in stable diffusion

A Guide to Negative Prompts in Stable Diffusion

Stable Diffusion, probably the most powerful text-to-image generation model, can generate anything you can dream up, as long as your prompt is clear. You can improve the quality of your images by using negative prompting. You can break down prompting into two parts: * Positive prompts * Negative prompts  The positive prompts