How To Fine-Tune AI Models On Small Datasets

One of the hardest parts of fine-tuning models? Getting high-quality data without breaching compliance. This Synthetic Data Generator Pipeline ia built to solve exactly that, and it is open-sources for you to use! You can now generate task-specific, high-quality synthetic datasets without using a single piece of real data, and still fine-tune performant models. Here’s what makes it different: → LLM-driven config generation Start with a simple prompt describing your task. The pipeline auto-generates YAMLs with structured I/O schemas, filters for diversity, and LLM-based evaluation criteria. → Streaming synthetic data generation The system emits JSON-formatted examples, prompt, response, metadata at scale. Each example includes row-level quality scores. You get transparency at both data and job level. → SFT + RFT with evaluator feedback We use models like DeepSeek R1 as judges. Low-quality clusters are automatically identified and regenerated. Each iteration teaches the model what “good” looks like. → Closed-loop optimization The pipeline fine-tunes itself, adjusting decoding params, enriching prompt structures, or expanding label schemas based on what’s missing. → Zero reliance on sensitive data No PII. No customer data. This is purpose-built for enterprise, healthcare, finance, and anyone who’s building responsibly. And it works: 📊 On an internal benchmark: - SFT with real, curated data: 79% accuracy - RFT with synthetic-only data: 73% accuracy That’s huge, especially when your hands are tied on data access. If you’re building copilots, vertical agents, or domain-specific models and want to skip the data wrangling phase, this is for you. Built by Fireworks AI 🔗 Try it out: https://lnkd.in/dXXDdyuM

LoRA is the easiest way to finetune a specialized LLM on your own data. Recent research has trained hundreds of LoRA models, arriving at four key takeaways... TL;DR: LoRA works best for narrow / less complex tasks, where LoRA finetunes consistently beat GPT-4. Mistral/Zephyr are the best base models for LoRA, and larger models are usually better (e.g., 7B > 2B). What is LoRA? When we finetune a language model, we modify the underlying parameters of the model. The core idea behind LoRA is to model this update to the model’s parameters with a low-rank decomposition, implemented in practice as a pair of linear projections. LoRA leaves the pretrained layers of the LLM fixed and injects a trainable rank decomposition matrix into each layer being adapted (i.e., linear projections in attention layers for LLMs). The Benefits of LoRA are plentiful: - A single pretrained model can be shared by several (much smaller) LoRA modules that adapt it to solve different tasks. - LoRA modules can be “baked in” to the weights of a pretrained model to avoid extra inference latency, and we can quickly switch between different LoRA modules to solve different tasks (i.e., "hot swapping"). - We only have to maintain the optimizer state for a very small number of parameters, which significantly reduces memory overhead. - Finetuning with LoRA is faster than end-to-end finetuning (i.e., roughly 25% faster in the case of GPT-3). LoRA Land: A recent paper from Predibase trains a massive number of LoRA models, aiming to provide a standardized and comprehensive finetuning analysis. They consider 10 base models (+ GPT-3.5/4) and 31 different tasks, so 310 LoRA models are trained in total. Analysis of these models led to four simple and important takeaways. (1) Finetuning via LoRA provides a large and consistent performance boost. 301/310 models surpass base model performance after finetuning with LoRA. 224/310 models surpass GPT-4 performance after finetuning with LoRA. GPT-4 uses a simple zero/single-shot prompt strategy and better performance could be achieved via prompt engineering. (2) Mistral and Zephyr are the best base models for LoRA: Mistral-7B achieves top performance on 10/31 taks (most of any model), while Zephyr-7B-beta has the highest overall average performance. (3) Size matters! 7B model variants outperform 2B model variants for 29/31 tasks. (4) Task type matters! Finetuning via LoRA works best on narrow, clearly-defined tasks (e.g., classification), while GPT-4 is best at tasks that are more broad and complex (e.g., MMLU or HumanEval). Interestingly, authors show in the paper that simple data heuristics (e.g., compressibility or input/output lengths) can be correlated with finetuning performance. In other words, LoRA is best for less complex tasks, while GPT-4 is better for harder tasks. We can derive simple data heuristics that correlate well with task complexity, thus allowing us to predict whether LoRA will be a good fit for a certain task or not!

Really nice, beginner friendly, 7 step guide to fine-tuning LLMs from Unsloth! My simple breakdown 👇 🚀 Getting Started: The 7-Step Process 1️⃣ Choose Your Model & Method For beginners, start with smaller models like Llama 3.1 (8B) and use QLoRA, which combines 4-bit quantization with LoRA to handle large models with minimal resources. This approach uses up to 4× less memory than standard methods! 2️⃣ Prepare Your Dataset Quality matters more than quantity! Structure your data as question-answer pairs for best results. While simply dumping code data can work for certain applications, well-structured datasets generally lead to better performance. 3️⃣ Optimize Your Hyperparameters The guide offers practical ranges for crucial settings: >> Learning rate: 1e-4 to 5e-5 (balance between learning speed and stability) >> Epochs: 1-3 (more than 3 reduces creativity but may decrease hallucinations) >> Context length: Start with 2048 tokens for testing 4️⃣ Avoid Common Pitfalls >> Overfitting: When your model memorizes training data instead of learning to generalize Solutions: Reduce learning rate, fewer epochs, combine with generic datasets >> Underfitting: When your model doesn't learn enough from training Solutions: Increase learning rate, more epochs, more relevant data 5️⃣ Training During training, aim for a loss value close to 0.5. The guide recommends: >> per_device_train_batch_size = 2 >> gradient_accumulation_steps = 4 >> max_steps = 60 (or num_train_epochs = 1 for full runs) >> learning_rate = 2e-4 6️⃣ Evaluation For evaluation, you can either: >> vibe check: Chat with the model to assess quality manually >> test check: Set aside 20% of your data for testing >> Use automatic evaluation tools like EleutherAI's lm-evaluation-harness 7️⃣ Save & Deploy The fine-tuned model can be saved as a small 100MB LoRA adapter file or pushed directly to Hugging Face. From there, you can run it using various inference engines like Ollama, vLLM, or Together via the LoRA inference feature. 💡 Why This Matters Fine-tuning lets you create specialized AI agents that can: >> Update domain knowledge without retraining from scratch >> Match your desired tone and communication style >> Optimize for specific tasks like sentiment analysis, customer service, or legal work >> The most exciting part? Fine-tuning can replicate all of RAG's capabilities, but RAG can't replicate all of fine-tuning's benefits. https://lnkd.in/ggWkFMMp

If you're working on AI projects with limited training data, building domain-specific AI applications, or struggling with the economics of data labeling, you should know about this new approach from the DeepSeek team. Reinforcement Fine-Tuning (RFT) is a new technique for fine-tuning large language models, cutting the required labeled data from thousands to just tens of examples. Traditional supervised fine-tuning (SFT) approaches have always been hampered by their dependence on vast amounts of labeled data. RFT takes a fundamentally different approach by utilizing a reward function to evaluate response correctness, enabling the model to learn more effectively than through simple mimicry of examples. The same technique that was used to develop DeepSeek-R1. This method proves particularly powerful in three key scenarios: (1) When no labeled data exists but correctness can be verified - such as code transpilation where outputs can be automatically tested. (2) When only limited labeled examples are available - fewer than 100 examples, where traditional methods typically overfit. (3) For tasks that benefit from chain-of-thought reasoning - where step-by-step logical thinking significantly improves results. A well-written post from Predibase here (they also added support for RFT on their platform recently!) https://lnkd.in/gHBdW5De P.S. Predibase just released an open-source model that outperforms OpenAI o1 by 67% for PyTorch-to-Triton transpilation tasks, enabling more efficient and intelligent AI models (link in comments).