Use Gradient Accumulation to Simulate Larger Batches on Small GPUs
Gradient accumulation lets you train models with effectively larger batch sizes than your GPU's memory can hold by splitting a large batch into smaller .
Fine Tuning Articles
50 articles
Create High-Quality Instruction Datasets for Fine-Tuning
Creating instruction datasets for fine-tuning LLMs feels like you're teaching a super-intelligent toddler – they can grasp complex concepts but need ver.
Tune Learning Rate Schedules for Stable LLM Fine-Tuning
Tune Learning Rate Schedules for Stable LLM Fine-Tuning — practical guide covering fine-tuning setup, configuration, and troubleshooting with real-world...
Fine-Tune Llama 3 for Instruction Following on Custom Data
Llama 3, when fine-tuned for instruction following, actually learns to predict the next token in a way that aligns with the style and content of your cu.
LoRA Explained: Configure Low-Rank Adaptation for Fine-Tuning
LoRA doesn't add new weights to a model; it injects trainable, low-rank matrices alongside the existing ones, effectively creating an adapter that learn.
Merge Fine-Tuned Models with mergekit for Ensemble Capabilities
Merge Fine-Tuned Models with mergekit for Ensemble Capabilities — practical guide covering fine-tuning setup, configuration, and troubleshooting with re...
Fine-Tune Mistral 7B on a Custom Dataset with QLoRA
QLoRA lets you fine-tune massive language models on hardware you probably already own, making cutting-edge AI accessible to everyone.
Monitor Fine-Tuning Runs with Weights & Biases
Fine-tuning is more like a black box than a science, and Weights & Biases is the X-ray machine that lets you see inside.
Fine-Tune Multilingual LLMs for Cross-Language Tasks
Fine-tuning a multilingual LLM for cross-language tasks is less about teaching it new languages and more about teaching it to translate between the lang.
Fine-Tune GPT-4o Mini with the OpenAI API
Fine-tuning GPT-4o Mini with the OpenAI API is less about teaching the model new facts and more about teaching it a new style or format.
Fine-Tune LLMs with ORPO: Odds Ratio Preference Optimization
ORPO is a surprisingly simple way to fine-tune LLMs by directly optimizing for preference odds, bypassing the need for a separate reward model entirely.
Detect and Fix Overfitting During LLM Fine-Tuning
Overfitting in LLM fine-tuning isn't just about memorizing the training data; it's about the model developing a brittle, overly specific understanding t.
Fine-Tune Phi-3 Mini for Edge Deployment on Small GPUs
Phi-3 Mini, when fine-tuned for edge deployment, can achieve surprisingly high performance on resource-constrained devices by leveraging quantization an.
Automate LLM Fine-Tuning Pipelines for Continuous Retraining
Fine-tuning an LLM isn't about teaching it new facts; it's about teaching it how to use the facts it already knows in a specific way.
Fine-Tune LLMs in 4-Bit with QLoRA on Consumer GPUs
QLoRA lets you fine-tune massive language models on just a few gigabytes of VRAM, effectively democratizing LLM customization for everyone.
Deploy Quantized Fine-Tuned Models for Efficient Inference
Quantized models don't just run faster; they can fundamentally change the kind of hardware you need to run them on, opening up edge deployments that wer.
Train Reward Models from Human Feedback for RLHF
The most surprising thing about training reward models is that they rarely learn to perfectly mimic human preferences; instead, they learn to extrapolat.
Implement RLHF: Train Reward Models and Run PPO Fine-Tuning
The most counterintuitive thing about Reinforcement Learning from Human Feedback RLHF is that the human feedback itself is often a proxy for a much simp.
Build the Business Case for Fine-Tuning vs Prompting vs RAG
Fine-tuning large language models can be more cost-effective than prompt engineering or Retrieval Augmented Generation RAG for specific, repetitive task.
Secure Your Fine-Tuning Pipeline: Data Privacy and Compliance
Fine-tuning large language models isn't just about getting better performance; it's a critical juncture where your sensitive data meets the open road of.
Run Supervised Fine-Tuning with TRL on Any Hugging Face Model
The most surprising truth about supervised fine-tuning SFT is that it often doesn't improve a model's reasoning ability as much as it improves its style.
Generate Synthetic Training Data with LLMs for Fine-Tuning
Large Language Models can generate synthetic training data that's often indistinguishable from real data, but they do it by learning patterns and relati.
Set Up TRL Trainer for Supervised and Preference Fine-Tuning
You can actually use TRL's SFTTrainer and PPOTrainer for supervised fine-tuning and preference fine-tuning, respectively, without needing to switch betw.
Speed Up Fine-Tuning 2x with Unsloth on Llama and Mistral
Unsloth is a library that can speed up fine-tuning of large language models LLMs like Llama and Mistral by up to 2x, and often more, by optimizing memor.
Fine-Tune Vision-Language Models for Multimodal Tasks
Vision-language models can learn to reason about images and text simultaneously, but fine-tuning them for specific tasks often leads to catastrophic for.
Deploy Fine-Tuned Models at Scale with vLLM
vLLM isn't just another inference server; it's a paradigm shift in how we serve large language models, especially when you need to handle a flood of req.
Fine-Tuning vs RAG vs Prompting: Choose the Right Approach
The most surprising truth about choosing between fine-tuning, RAG, and prompting is that you're likely already doing all three in some capacity, and the.
A/B Test Base Models Against Fine-Tuned Versions in Production
The most surprising thing about A/B testing a fine-tuned model against its base version in production is how often the fine-tuned model doesn't win, eve.
Merge LoRA Adapters into a Base Model for Deployment
Merge LoRA Adapters into a Base Model for Deployment — practical guide covering fine-tuning setup, configuration, and troubleshooting with real-world ex...
Configure Axolotl for Multi-Format Fine-Tuning Runs
Axolotl can fine-tune models across multiple formats simultaneously, meaning you can run a training job that updates weights for a model that will event.
Prevent Catastrophic Forgetting During LLM Fine-Tuning
Fine-tuning an LLM can erase everything it learned before, making it forget its original knowledge. Let's see what a fine-tuned model can do
Format Chat Templates Correctly for Instruction Fine-Tuning
Sure, let's dive into formatting chat templates for instruction fine-tuning. The most surprising thing about chat templates is that even if your model s.
Choose the Best Checkpoint After Fine-Tuning
The "best" checkpoint after fine-tuning isn't necessarily the one with the highest score on your validation set; it's the one that performs best on the .
Fine-Tune LLMs for Classification and Information Extraction
Fine-tuning LLMs for classification and information extraction is less about teaching the model new facts and more about teaching it to recognize patter.
Fine-Tune an LLM for Code Generation on Your Codebase
Fine-tuning a Large Language Model LLM for code generation on your specific codebase is less about teaching it a new language and more about teaching it.
Estimate GPU Compute Budget Before Starting a Fine-Tuning Run
Fine-tuning a large language model is like trying to teach a genius a new trick: you need to be sure they have enough brainpower and time before you sta.
Fine-Tune LLMs Continually on New Data Without Full Retraining
The true power of continual fine-tuning isn't about making a model "smarter" in a general sense; it's about making it an expert in a narrow, evolving do.
Format Conversation Datasets for Supervised Fine-Tuning
Format Conversation Datasets for Supervised Fine-Tuning — practical guide covering fine-tuning setup, configuration, and troubleshooting with real-world...
Compare Cloud GPU Costs for Fine-Tuning: A100 vs H100 vs L40
The H100 GPU, while significantly faster for training, can actually be cheaper per hour than the A100 when fine-tuning certain models.
Deduplicate and Clean Training Data for Better Fine-Tuning Results
The most surprising thing about deduplicating and cleaning training data is that it's often the only thing you need to do to dramatically improve your f.
Prepare High-Quality Datasets for LLM Fine-Tuning
The most surprising thing about preparing datasets for LLM fine-tuning is that "quality" isn't just about accuracy; it's about specificity and relevance.
Configure DeepSpeed ZeRO for Fine-Tuning Large Language Models
DeepSpeed ZeRO can actually increase your GPU memory usage in certain configurations, even though its primary goal is to reduce it.
Adapt Pre-Trained LLMs to Domain-Specific Tasks with Fine-Tuning
Fine-tuning a pre-trained LLM is less about teaching it new knowledge and more about teaching it how to apply what it already knows to a specific contex.
Fine-Tune LLMs with Direct Preference Optimization
Direct Preference Optimization DPO lets you fine-tune large language models not by telling it what's good, but by showing it what's better, bypassing th.
Fine-Tune Embedding Models for Domain-Specific Semantic Search
Fine-Tune Embedding Models for Domain-Specific Semantic Search — practical guide covering fine-tuning setup, configuration, and troubleshooting with rea...
Evaluate Fine-Tuned LLMs with Task-Specific Benchmarks
Fine-tuning an LLM for a specific task often makes it worse at general tasks, not just better at the one you trained it on.
Configure FSDP for Distributed Fine-Tuning Across Multiple GPUs
FSDP, or Fully Sharded Data Parallelism, is a PyTorch feature that can shard your model, gradients, and optimizer states across multiple GPUs, allowing .
Full Fine-Tuning vs PEFT: Choose the Right Approach
Full fine-tuning a massive language model is often less effective than using Parameter-Efficient Fine-Tuning PEFT methods for most practical application.
Fine-Tune Gemma 2 on a Custom Dataset Step by Step
Fine-tuning a large language model like Gemma 2 on your own data can unlock incredible, specialized capabilities, but the process often feels like navig.
Export Fine-Tuned Models to GGUF for Local Deployment with Ollama
The most surprising thing about exporting fine-tuned models to GGUF for Ollama is that you're not just converting a file format; you're fundamentally ch.