How to Train Your Own Large Language Models

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How to Train Your Own Large Language Models

Рет қаралды 37,915

Күн бұрын

Given the success of OpenAI’s GPT-4 and Google’s PaLM, every company is now assessing its own use cases for Large Language Models (LLMs). Many companies will ultimately decide to train their own LLMs for a variety of reasons, ranging from data privacy to increased control over updates and improvements. One of the most common reasons will be to make use of proprietary internal data.
In this session, we’ll go over how to train your own LLMs, from raw data to deployment in a user-facing production environment. We’ll discuss the engineering challenges, and the vendors that make up the modern LLM stack: Databricks, Hugging Face, and MosaicML. We’ll also break down what it means to train an LLM using your own data, including the various approaches and their associated tradeoffs.
Topics covered in this session:
- How Replit trained a state-of-the-art LLM from scratch
- The different approaches to using LLMs with your internal data
- The differences between fine-tuning, instruction tuning, and RLHF
Talk by: Reza Shabani
Here’s more to explore:
LLM Compact Guide: dbricks.co/43WuQyb
Big Book of MLOps: dbricks.co/3r0Pqiz
Connect with us: Website: databricks.com
Twitter: / databricks
LinkedIn: / databricks
Instagram: / databricksinc
Facebook: / databricksinc

Пікірлер: 7

@syednaveed1391 10 ай бұрын

Super useful. I am a physician, tried fine tune using cancer documents. It didn't work. Found your video Thanks

@BlackThorne 11 ай бұрын

00:35 🧠 Business Use Case: Training Large Language Models (LLMs) 01:45 💡 Reasons for Training LLMs: Customization, Cost Efficiency 02:40 🔀 Training Process: Data Pipelines, Model Training, Inference 05:16 📊 Data Processing: Filtering, Anonymization, Pre-processing 08:23 🔤 Tokenizer & Vocabulary Training: Custom Vocabulary, Benefits, Challenges 13:09 🎯 Model Evaluation: Human Eval Framework, Code Metrics vs. NLP Metrics 18:35 ⚙ Model Training & Specs: Model Size, Training Objective, Attention Mechanisms 20:55 📈 Model Training Challenges: Data Determinism, Loss Curve Spikes 23:41 🔄 Generation vs. Evaluation: Separating the Process 24:08 🚀 Deployment: Building Inference Stack, Managed Services 24:52 🖥 Model training involves GPU and model size considerations, pre/post-processing, and server/client-side logic. 25:49 🧠 Evaluating your model is crucial; define success criteria early to guide the training process. 26:02 🔄 Rapid iteration is valuable for testing model behavior and improving user experience. 26:29 ⏳ Ensure compatibility between training and inference stacks to avoid sub-optimal results. 26:57 🔄 Customization drives the desire to train LLMs with one's data; various approaches exist. 27:51 📚 Retrieval-based augmentation involves fetching relevant context to guide model responses. 28:08 🤖 Contextual prompting improves model's domain-specific knowledge, even if not originally trained. 28:21 💡 Embeddings and semantic similarity prioritize context selection for retrieval. 30:01 🎯 Fine-tuning methods vary in complexity; consider instruction tuning and training from scratch. 31:25 🔄 Models struggle with varying data formats, short-form content, and changing facts. 32:35 🌍 Custom domain data presents challenges; careful selection and use of embeddings is key. 33:37 🌶 Fine-tuning is complex; unsupervised fine-tuning for new domain knowledge has limitations. 35:18 🚫 Agents might become redundant as models absorb useful functionalities. 36:00 🔄 Balancing training data mix is challenging; no established formula, lots of variables. 37:11 💾 Data iteration tools are crucial as data, not GPUs, becomes the bottleneck for model advancement.

@dilipjha08 2 ай бұрын

Thanks for knowledge sharing to the technology user. It was very details about the dlt as well as streaming tables and comprison between it and demo of the topic was very perfect.