Developing a Local LLM Pipeline Using Unlabeled Data

I’ve been exploring how we can make LLMs genuinely reflect individual users without the need for manual data labeling. In this piece, I introduce a method called Adversarial Contrastive Distillation (ACD) to enable Self-Supervised Persona Fine-Tuning. The idea is straightforward: use a teacher model to generate “contrarian” examples, then fine-tune a smaller model to better align with a user’s tone, style, or beliefs automatically. To put this into practice, I fine-tuned a DistilBERT model on four years of WhatsApp chat history with a close friend. The result was an accuracy jump from 66.91 percent to 85.69 percent, requiring zero manual labeling. If you are working on scalable personalization in LLMs or just curious about how self-supervision can make AI more you, I’d love to hear your thoughts. https://lnkd.in/djPuWJXZ

Most ML teams stall at the same bottleneck: unlabeled data. Labeling is slow. It’s expensive. And it’s the reason so many good ideas die early. So my team and I have been testing LLMs as autonomous judges assigning labels at scale, no humans in the loop until it really matters. Here’s the play: ◼️ Feed your raw, unlabeled data to an LLM, multiple actually. ◼️ Have multiple models assign labels independently. ◼️ When they agree, treat it as ground truth. ◼️ When they disagree, flag it for review. This completely changes the economics of data labeling. Instead of paying humans to label everything, we're only paying for the ambiguous cases where the models disagree.

“In this work, we introduce Test-Time Reinforcement Learning (TTRL), a novel method for training LLMs using RL on unlabeled data. TTRL enables self-evolution of LLMs by utilizing the priors in the pre-trained models. Our experiments demonstrate that TTRL consistently improves performance across a variety of tasks and models. Notably, TTRL boosts the pass@1 performance of Qwen-2.5-Math-7B by approximately 211% on the AIME 2024 with only unlabeled test data”

A blueprint for designing production LLM systems: From Notebooks to production For example, we will fine-tune an LLM and do RAG on social media data, but it can easily be adapted to any data. We have 4 core components. We will follow the feature/training/inference (FTI) pipeline architecture. 𝟭. 𝗗𝗮𝘁𝗮 𝗖𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It is based on an ETL that: - crawls your data from blogs and socials - standardizes it - loads it to a NoSQL database (e.g., MongoDB) As: - we work with text data, which is naturally unstructured - no analytics required → a NoSQL database fits like a glove. 𝟮. 𝗙𝗲𝗮𝘁𝘂𝗿𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It takes raw articles, posts and code data points from the data warehouse, processes them, and loads them into a logical feature store. Let's focus on the logical feature store. As with any RAG-based system, a vector database is one of the central pieces of the infrastructure. We directly use a vector database as a logical feature store. Unfortunately, the vector database doesn't offer the concept of a training dataset. To implement this, we will wrap the retrieved data into a versioned, tracked, and shareable MLOps artifact. To conclude: - the training pipeline will use the instruct datasets as artifacts (offline) - the inference pipeline will query the vector DB for RAG (online) 𝟯. 𝗧𝗿𝗮𝗶𝗻𝗶𝗻𝗴 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It consumes instruct datasets from the feature store, fine-tunes an LLM with it, and stores the tuned LLM weights in a model registry. More concretely, when a new instruct dataset is available in the logical feature store, we will trigger the training pipeline, consume the artifact, and fine-tune the LLM. We run multiple experiments to find the best model and hyperparameters. We will use an experiment tracker to compare and select the best hyperparameters. After the experimentation phase, we store and reuse the best hyperparameters for continuous training (CT). The LLM candidate's testing pipeline is triggered for a detailed analysis. If it passes, the model is tagged as accepted and deployed to production. Our modular design lets us leverage an ML orchestrator to schedule and trigger the pipelines for CT. 𝟰. 𝗜𝗻𝗳𝗲𝗿𝗲𝗻𝗰𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲 It is connected to the model registry and logical feature store. From the model registry, it loads a fine-tuned LLM, and from the logical feature store, it accesses the vector DB for RAG. It receives client requests as queries through a REST API. It uses the fine-tuned LLM and vector DB to do RAG to answer the queries. Everything is sent to a prompt monitoring system to analyze, debug, and understand the system. #artificialintelligence #machinelearning #mlops