Sparsh Bhasin

image classification models

Best Image Classification Models in 2024

You can find pre-trained models that can accomplish any task you want. These models, trained on a large corpus of data can capture patterns and features. There are models made specifically for image classifications. Using pre-trained models, developers can save on time and cost.  Instead of doing hours of manual

llama 3.1 finetuning

Building a Sarcastic Chatbot: A Case Study in Fine-Tuning and Deployment with MonsterAPI

We ran a fun experiment and fine-tuned the LLaMa 3.1 8B model to create a sarcastic chatbot. Here's a complete breakdown of how we did it, and the fun results we got.

SDXL fine-tuning

How to Fine-tune SDXL for Avatar Generation on MonsterAPI

Convert your images into classy avatars by fine-tuning SDXL In just 3 steps, fine-tune SDXL and make your personal avatar generation agent.

Llama 3.2

Comprehensive Guide for Instruction Fine-tuning of LLaMa 3.2 using MonsterAPI

In this blog, we'll teach you how to fine-tune a llama-3.2 model to generate code using the alpaca Python coding dataset. We'll use LORA, which preserves the pre-trained model knowledge while facilitating its seamless learning of new things.

Whisper Finetuning

How to Finetune Whisper for Speech-to-Text Transcription

Whisper Fine-tuning for speech-to-text transcription can be complicated if you don't know what to do. Use MonsterAPI's fine-tuning & deployment pipeline to streamline the process.

finetuning sdxl

How to run a fine-tuned image generation SDXL model?

Fine-tuning the SDXL model for your use case is complicated. Deploying the finetuned SDXL model is even more complex. So, we created this step by step guide on how to run SDXL model.

Data augmentation

How to Build a Dataset for LLM Fine-tuning

Building the right dataset for LLM fine-tuning makes all the performance for LLM fine-tuning. Here's how to build a dataset for LLM fine-tuning.

LoRA Finetuning

5 Reasons Why LoRA Adapters are the Future of Fine-Tuning

LoRA (Low-Rank Adaption) is a game-changing solution for optimizing the fine-tuning of large language models. Here's how LoRA adapters are future of fine-tuning.

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.