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.
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