Sparsh Bhasin - MonsterAPI Blog (Page 2)

Sparsh Bhasin

best open source LLMs

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.

Fine-tuning Gemma 2B & achieving 60% performance boost

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 Large Language Model Fine-tuning Mistakes to Avoid

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

Step-by-Step Guide to Deploying Flux Docker Image

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 fashion clothes image inpainting 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.

Accelerating Learning with Grokkfast: Now Available in MonsterAPI

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.

Guide to deploying a fine tuned LLM

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.

Building text summarization & code generation bots with MonsterAPI

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.

Fine-tuning LLama 3.1 8B and Outperforming the Competition

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 vs LLM Fine-Tuning

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.

Enhancing LLM Context Length with 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.

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

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 x MonsterAPI: Powerful SLMs at your fingertips

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 405B vs GPT4o - Head-to-Head Comparison

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.

Enhancing Language Model Fine-tuning with LLM Data Augmentation

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 ComfyUI Docker Image on MonsterAPI

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

A Guide to Negative Prompts in Stable Diffusion

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

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

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 build our

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.

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.