Are you eager to delve into the fascinating realm of #large #language #models (#llms)?

πŸŒŸπŸ‘©β€πŸ’» Embark on this thrilling adventure with our comprehensive #llm #course, featuring detailed roadmaps and engaging interactive Colab notebooks. πŸ›£οΈπŸ–₯️

πŸ“– Course Outline(https://lnkd.in/dgc-Hgm3):

This #llm #course is built on three foundational aspects:

πŸ“˜ LLM Basics: Gain a solid understanding of mathematics, Python, and neural networks.
πŸ§ͺ LLM Researcher: Learn to develop advanced LLMs using the newest methodologies.
πŸ‘¨β€πŸ’Ό #llm #practitioner: Acquire skills in designing and implementing LLM-based solutions with practical approaches.
πŸ““ #interactive #resources:
Uncover a rich collection of hands-on notebooks and insightful articles to enhance your learning in #llms. πŸ“šπŸŒŸ Here's a glimpse of what's inside:

Tools:
πŸ•΅οΈ LLM AutoEval: Simplify LLM evaluations using RunPod.
πŸ›Œ LazyMergekit: Merge models effortlessly with just a click.
⚑ AutoGGUF: Convert LLMs into GGUF format seamlessly.
🌲 Model Genealogy: Visualize connections in the lineage of merged models.

Fine-tuning:
πŸš€ Fine-tuning Llama 2 in Google Colab: A beginner's guide to your first Llama 2 model.
🌠 Fine-tuning LLMs with Axolotl: Explore the leading-edge tool for fine-tuning LLMs.
πŸš€ Enhancing Mistral-7b with DPO: Improve supervised fine-tuned models using DPO.

Quantization:
🌍 Introduction to Quantization: Learn to optimize LLMs with 8-bit quantization.
🌠 GPTQ and 4-bit Quantization: Run open-source LLMs efficiently on standard hardware.
🌍 GGUF and llama.cpp for Quantization: Apply GGUF to Llama 2 models and share them on the HF Hub.
🌠 ExLlamaV2: A rapid Library for LLM execution. Quantize and run EXL2 models efficiently.

Additional Topics:
🌈 Decoding Techniques in LLMs: Understand text generation from beam search to nucleus sampling.
🎨 Visualizing GPT-2's Loss Landscape: Explore a 3D plot depicting the loss landscape through weight changes.
🌍 Enhancing ChatGPT with Knowledge Graphs: Augment ChatGPT's responses using knowledge graphs.
🀝 Combining LLMs with mergekit: Effortlessly create your models without needing a GPU!

Ready to advance your skills in LLMs? πŸš€ Join us to discover, innovate, and transform the field together!

πŸŒŸπŸ”— #llm #machinelearning #techinnovation

image