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#ai #irish 250,000 #images #sale #ai #generative
➜ https://warriorplus.com/o2/a/crvnyz/0
#ai #irish 250,000 #images #sale #ai #generative
➜ https://warriorplus.com/o2/a/crvnyz/0
#gold #irish #prediction #end #2024
Gold price is going to rise to 2100$-2200$ from level (2000-2014)
Are you eager to delve into the fascinating realm of #large #language #models (#llms)?
🌟👩💻 Embark on this thrilling adventure with our comprehensive #llm #course, featuring detailed roadmaps and engaging interactive Colab notebooks. 🛣️🖥️
📖 Course Outline(https://lnkd.in/dgc-Hgm3):
This #llm #course is built on three foundational aspects:
📘 LLM Basics: Gain a solid understanding of mathematics, Python, and neural networks.
🧪 LLM Researcher: Learn to develop advanced LLMs using the newest methodologies.
👨💼 #llm #practitioner: Acquire skills in designing and implementing LLM-based solutions with practical approaches.
📓 #interactive #resources:
Uncover a rich collection of hands-on notebooks and insightful articles to enhance your learning in #llms. 📚🌟 Here's a glimpse of what's inside:
Tools:
🕵️ LLM AutoEval: Simplify LLM evaluations using RunPod.
🛌 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:
🚀 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.
🚀 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? 🚀 Join us to discover, innovate, and transform the field together!
🌟🔗 #llm #machinelearning #techinnovation
#ai #generative #news on #linkedin #join #group
https://www.linkedin.com/groups/12965480/
