#finetuning

33rdSon3rdson.hashnode.dev

Training AI to Predict Clinical Trial Outcomes: A 30% Improvement in 3 Hours

Predicting whether a clinical trial will succeed or fail is notoriously difficult. Even experienced pharmaceutical analysts struggle with accuracy rates much better than a coin flip. But what if we co

Feb 199 min read

ASAnanth Sfirst-tech-blog.hashnode.dev

Frontier LLM Post-Training : SFT vs DPO/IPO/KTO + RLAIF

If you trained a frontier LLM today the way we trained them in 2021—pretrain, do a little instruction tuning, ship—you’d get crushed in production. Not because the base model can’t write or reason, but because users don’t experience “capability”; the...

Feb 128 min read

IMItay Mlilbullet.hashnode.dev

Tiny Guards: Defending Agents with small 1-0.6B Models

Prompt injections and their new rival Prompt injection turns “trusted inputs” (emails, web pages, retrieved docs) into an execution surface. If your agent can browse, read email, or call tools, a buried instruction can hijack actions. The dirty secre...

Feb 1111 min read

RARana Adeel Tahirthelatentlament.hashnode.dev

Fine-Tuning BLIP-2 with LoRA

In my journey to dive deeper into multimodal AI systems, I decided to fine-tune BLIP-2, a powerful vision-language model trained on the Flickr8k dataset to generate image captions. What made this more exciting was integrating LoRA (Low-Rank Adaptatio...

Feb 103 min read

RARana Adeel Tahirthelatentlament.hashnode.dev

Fine-Tuning DeepSeek Math to Solve Emoji Equations: A Step-by-Step Guide

This blog post guides you through the process of fine-tuning the DeepSeek Math model to solve math problems expressed using emojis. This is a fun and engaging way to explore the capabilities of large language models (LLMs) and their ability to unders...

Feb 29 min read

GSGeet Sharmageetsharma.hashnode.dev

Why Fine-Tuning Often Fails (and Why RAG + Agents Usually Win)

Fine-tuning promises customization. Reality often delivers disappointment. Why Fine-Tuning Breaks Down Training data is shallow Domain knowledge changes Models forget previous capabilities Drift requires retraining Each iteration increases cos...

Jan 261 min read

BBBrian Baldockblog.brianbaldock.net

Learning Fine-Tuning on Nvidia DGX Spark - Part 1

If you are working with NVIDIA DGX Spark (or your own rig) and trying to do anything beyond toy experimentation, you will very quickly run into challenges. 👉 Setting up your own rig? See my previous article to get started Deploying Local AI Inferen...

Jan 245 min read

RGRyan Giggsryan-giggs.hashnode.dev

OCI Generative AI: Dedicated Cluster Sizing, Pricing, and Fine-Tuning Deep Dive

Understanding how to properly size, price, and configure dedicated AI clusters is crucial for cost-effective deployment of custom LLMs on Oracle Cloud Infrastructure. This comprehensive guide walks you through cluster types, pricing calculations, fin...

Jan 1511 min read

RGRyan Giggsryan-giggs.hashnode.dev

Fine-Tuning and Inference in LLMs: From Custom Models to Production Deployment

As organizations move beyond using pre-trained models, two critical concepts become essential: fine-tuning and inference. Fine-tuning allows you to customize models for specific domains, while inference enables you to deploy these models to serve pre...

Jan 1310 min read

JBJun Baesjun.hashnode.dev

Why LoRA? Understanding the representative PEFT.

Why LoRA? Low-Rank Adaptation (LoRA) has revolutionized the way we approach Large Language Models (LLMs). As the most prominent Parameter-Efficient Fine-Tuning (PEFT) method, LoRA allows developers to adapt massive models like Llama 3 or GPT-4 to spe...

Jan 116 min read

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#finetuning

Training AI to Predict Clinical Trial Outcomes: A 30% Improvement in 3 Hours

Frontier LLM Post-Training : SFT vs DPO/IPO/KTO + RLAIF

Tiny Guards: Defending Agents with small 1-0.6B Models

Fine-Tuning BLIP-2 with LoRA

Fine-Tuning DeepSeek Math to Solve Emoji Equations: A Step-by-Step Guide

Why Fine-Tuning Often Fails (and Why RAG + Agents Usually Win)

Learning Fine-Tuning on Nvidia DGX Spark - Part 1

OCI Generative AI: Dedicated Cluster Sizing, Pricing, and Fine-Tuning Deep Dive

Fine-Tuning and Inference in LLMs: From Custom Models to Production Deployment

Why LoRA? Understanding the representative PEFT.

#finetuning

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#finetuning

Training AI to Predict Clinical Trial Outcomes: A 30% Improvement in 3 Hours

Frontier LLM Post-Training : SFT vs DPO/IPO/KTO + RLAIF

Tiny Guards: Defending Agents with small 1-0.6B Models

Fine-Tuning BLIP-2 with LoRA

Fine-Tuning DeepSeek Math to Solve Emoji Equations: A Step-by-Step Guide

Why Fine-Tuning Often Fails (and Why RAG + Agents Usually Win)

Learning Fine-Tuning on Nvidia DGX Spark - Part 1

OCI Generative AI: Dedicated Cluster Sizing, Pricing, and Fine-Tuning Deep Dive

Fine-Tuning and Inference in LLMs: From Custom Models to Production Deployment

Why LoRA? Understanding the representative PEFT.