18 results for “fine-tuning”
Related topicsfine-tuning
Aug 07
Soup Project Claims to Fine-Tune 8B Models on 4 GB Laptop GPUs
A recently shared open-source project named "Soup" claims to enable the fine-tuning of 8-billion-parameter large language models (LLMs) on laptops equipped with just a 4 GB GPU. The project, posted to Hacker News by creator MakazhanAlpamys, utilizes a technique called "layer streaming" to achieve this feat, according to its GitHub page.
Jan 27
Train a 70B Model on One GPU: A LoRA Walkthrough
Learn how Low-Rank Adaptation (LoRA) allows you to efficiently fine-tune massive language models on consumer hardware by training only a tiny fraction of the model's parameters.
May 22
Your LLM Is a Reward Model: How DPO Cut Alignment Costs
Direct Preference Optimization (DPO) simplifies the complex RLHF pipeline by eliminating the need for a separate reward model. This article explains how DPO works, why it made alignment more accessible, and the trade-offs involved in this more direct approach.
Mar 12
More Data, Smaller Models: The 2022 Paper That Resized Every LLM
A 2022 paper from DeepMind, known as Chinchilla, proved that the AI industry was building models that were too large and training them on too little data. We explain the concept of compute-optimal scaling and why a smaller model trained for longer is often superior.
Dec 16
The 'R' in RAG Was Meant To Be Trained, Not Just Searched
The original "Retrieval-Augmented Generation" (RAG) paper proposed a deeply integrated system where the retriever and generator were trained together. This is a stark contrast to today's common "vector search + prompt" pattern, and understanding the original vision reveals the trade-offs we've made.
Oct 23
From 18% to 58% Accuracy: The Simple Prompt That Taught AI to Reason
A deep dive into Chain-of-Thought (CoT) prompting, the technique that unlocked complex reasoning in LLMs. Learn how it works, why it's an emergent ability of scale, and where the field is heading in 2025.
Jun 11
Stop Fine-Tuning Your LLM: Why Retrieval Is Cheaper, Faster & More Reliable
Developers often try to add knowledge to a large language model by fine-tuning it, a costly and often ineffective approach. A better strategy follows a clear hierarchy: start with the prompt, then use Retrieval-Augmented Generation (RAG) for facts, and only use fine-tuning to change the model's core behavior.
Aug 10
The $8,000 Misunderstanding: How to Cut Your LLM Bill by 80%
Facing a surprise LLM bill? Learn how enterprise teams are cutting AI inference costs by 50-80% using a practical framework of model routing, prompt caching, and efficient architecture, without sacrificing quality.
Jul 28
Kimi K3: a 2.8-trillion-parameter open model that bets on linear attention
Moonshot's new open-weight flagship isn't chasing a leaderboard — it's a bet that a cheap million-token context changes what we build. What actually changed under the hood, how to run it, and where it sits against the open-weight frontier.
Jul 06
HITL That Works: Make Review Faster Than the Original Task
Most human-in-the-loop (HITL) review queues fail because they are slower and more tedious than the manual work they replace. This article details four principles for designing HITL systems that humans will actually use, focusing on making review faster than creation, surfacing model uncertainty, and turning the review process into a continuous evaluation set.
Apr 30
The Trillion-Parameter Tipping Point: Open-Weight AI Is Now a C-Suite Decision
As of April 2026, open-weight models with over a trillion parameters rival proprietary APIs in capability, forcing a new strategic decision for enterprises. This article breaks down the choice across four critical axes: data control, capability, cost, and the upgrade cycle.
May 14
Your LLM Is a Bug: A 5-Point Test for When Not to Use AI
Large Language Models are powerful but often the wrong tool for the job. This article provides a five-point decision test to identify when a simpler, more reliable solution like a database query, a regular expression, or a classical classifier is the better choice.
Apr 02
The 76% Mistake: Why AI Build vs. Buy Is a Layer-by-Layer Choice
The "build vs. buy" decision for LLM applications is no longer a single choice. This article presents a framework for analyzing the AI stack layer by layer—from the model to evaluation—to avoid common pitfalls and focus engineering effort where it creates the most value.
Mar 26
Role Confusion: The 61% Attack Success Rate Exposing LLM's Core Flaw
Prompt injection isn't just "tricking an AI"; it's a classic authorization vulnerability. New research shows a 61% attack success rate by exploiting 'role confusion,' where models grant authority based on writing style, not source, demanding a shift to architectural security patterns.
Mar 06
Failover Amnesia: Why Your LLM Chatbot Forgets During an Outage
Your application successfully fails over to a backup LLM provider during an outage, but your user's conversation is lost. This article explains why stateless failover breaks the user experience and details the stateful abstraction layer required to preserve conversational context, making your AI resilient in a way that users actually notice.
Jan 06
The $12 Agent Task: Why Production AI Needs a Harness, Not a Loop
Most AI agent demos are built on a simple loop that is too expensive and unreliable for production. Real-world systems use a "harness" architecture with structured workflows, checkpoints, and budgets to create agents that can survive contact with reality.
Nov 07
The 1M Token Budget: Why 1 in 10 Queries Fail at Scale
Large context windows are not a free upgrade; they are a budget with steep costs in money, latency, and accuracy. This article explains the three hidden costs of long context and provides a framework for deciding when to use it.
Oct 17
Chunking Is a Retrieval Decision: Up to 9% Recall Is on the Line
Most RAG systems fail due to a single, overlooked setting: chunk size. This article explains how rethinking chunking as a dynamic retrieval parameter, not a static preprocessing step, can boost recall by up to 9% and why the optimal size depends entirely on your users' questions.