Trelis Research Courses

Improving LLM Accuracy with Monte Carlo Tree Search

Explore Monte Carlo Tree Search to enhance LLM accuracy, focusing on boosting Llama 3 8B performance. Learn implementation, testing, and limitations through code examples and mathematical problem-solving.

• YouTube
• 33 minutes
• On-Demand
• Free Video

Top Ten Tips for Fine-tuning Large Language Models

Discover ten essential tips for effective fine-tuning of language models, from starting small to advanced techniques like LoRA and preference tuning. Gain practical insights for optimal results.

• YouTube
• 25 minutes
• On-Demand
• Free Video

Preparing Fineweb - A Finely Cleaned Common Crawl Dataset for Language Models

Explore Common Crawl data processing, comparing datasets like C4 and RefinedWeb. Learn about quality filters, deduplication strategies, and LLM-assisted filtering for creating high-quality datasets like Fineweb Edu.

• YouTube
• 37 minutes
• On-Demand
• Free Video

Data Preparation Tips and Tricks for Machine Learning

Master data preparation techniques for AI models, including filtering, balancing, and synthetic dataset creation. Explore clustering methods, chat templates, and handling mixed-language data for improved model performance.

• YouTube
• 1 hour
• On-Demand
• Free Video

Anonymizing Sensitive Data in LLM Prompts - Techniques and Tools

Learn techniques for anonymizing sensitive data in LLM prompts, including using libraries like Presidio and Outlines. Explore practical demos and gain insights on vLLM, TGI, and GGUF for Mac.

• YouTube
• 36 minutes
• On-Demand
• Free Video

LoRA Fine-tuning Explained - Choosing Parameters and Optimizations

Explore LoRA fine-tuning techniques, from parameter selection to optimization strategies, enhancing your ability to customize language models effectively.

• YouTube
• 1 hour 4 minutes
• On-Demand
• Free Video

Fine-tuning Llama 3 on Wikipedia Datasets for Low-Resource Languages

Learn to fine-tune Llama 3 on Wikipedia datasets for low-resource languages. Master dataset creation, LoRA setup, blending techniques, and parameter optimization for improved language model performance.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Fine-tuning Multi-modal Video and Text Models

Explore fine-tuning techniques for multi-modal video and text models, covering dataset generation, clipping, querying, and evaluation using IDEFICS 2 and Jupyter Notebooks.

• YouTube
• 22 minutes
• On-Demand
• Free Video

Transformers from Scratch - Part 2: Building and Training a Weather Prediction Model

Dive deep into advanced transformer concepts, exploring encoder-decoder architectures, GPT-4o, and positional embeddings. Apply knowledge to weather prediction and analyze model performance.

• YouTube
• 57 minutes
• On-Demand
• Free Video

Training and Serving Custom Multi-modal Models - IDEFICS 2 and LLaVA Llama 3

Explore advanced multi-modal model fine-tuning and deployment techniques, focusing on IDEFICS 2 and LLaVA Llama 3 for enhanced image-text processing capabilities.

• YouTube
• 38 minutes
• On-Demand
• Free Video

IDEFICS 2 API Endpoint, vLLM vs TGI, and General Fine-tuning Tips

Explore IDEFICS 2 API, vLLM vs TGI, and fine-tuning techniques. Gain insights on deploying multimodal models, transformer architectures, and advanced training methods for AI development.

• YouTube
• 1 hour
• On-Demand
• Free Video

Very Few Parameter Fine-Tuning with ReFT and LoRA

Explore advanced fine-tuning techniques ReFT and LoRA for efficient parameter optimization in transformer models. Learn implementation, comparison, and practical tips for improved model performance.

• YouTube
• 55 minutes
• On-Demand
• Free Video

Tiny Text and Vision Models - Fine-Tuning and API Setup

Explore fine-tuning and API setup for tiny text and vision models, covering multi-modal architectures, LoRA adapters, and deployment strategies for custom APIs.

• YouTube
• 44 minutes
• On-Demand
• Free Video

Full Fine-Tuning vs LoRA and QLoRA - Comparison and Best Practices

Explore full fine-tuning vs (Q)LoRA techniques, comparing VRAM usage, training time, and quality. Learn parameter selection, tips for QLoRA, and step-by-step guidance for TinyLlama and Mistral 7B fine-tuning.

• YouTube
• 53 minutes
• On-Demand
• Free Video

Multi-GPU Fine-tuning with DDP and FSDP

Master multi-GPU fine-tuning techniques using DDP and FSDP. Explore VRAM optimization, distributed training strategies, and practical implementation with code examples and demos.

• YouTube
• 1 hour 8 minutes
• On-Demand
• Free Video