Large Language Models

https://huggingface.co/datasets/bird-of-paradise/transformer-from-scratch-tutorial/blob/main/Transformer_Implementation_Tutorial.ipynb https://github.com/hkproj/pytorch-transformer

Resources: Transformer ‌Transformer is an encoder and decoder model. It uses a mechanism called Attention. Transformer model consists of encoder and decoder. Analogy Imagine you are at a loud party. You hear fragments of conversations. To understand a specific sentence, your brain does three things instantly: Transformer Architecture The Transformer architecture is an Encoder-Decoder structure. ... Read More

LLMs: Fine-tuninghttps://developers.google.com/machine-learning/crash-course/llm/tuning Foundation Models A Foundation LLM (or base/pre-trained model) is a general-purpose model trained on vast amounts of data. It understands grammar and can perform creative tasks like writing poetry. However, to solve specific problems (like classification or regression), it often serves as a starting platform rather than a finished solution. Fine Tuning Fine-tuning ... Read More

Introduction to Large Language Modelshttps://developers.google.com/machine-learning/crash-course/llm LLMs: What is a Large Language Model? An LLM is a predictive technology that estimates the next “token” (word, character, or subword) in a sequence. They outperform older models (like N-grams) because they use vastly more parameters and can process significantly more context at once. Transformer It is most successful ... Read More

Introduction to Large Language Modelshttps://developers.google.com/machine-learning/crash-course/llm What is Language Model? At its simplest, a language model is a statistical tool that predicts the next piece of text in a sequence. The N-gram Approach Early language models used “N-grams,” which are simply ordered sequences of words where N represents the number of words. Context Context refers to ... Read More

Then, one can chat like this: To run flask application for Ollama: Troubleshootings:1. Check is ollama is running curl http://localhost:11434

https://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks Accessing: 3. HuggingFace Hubhttps://github.com/ML-Bioinfo-CEITEC/genomic_benchmarks/blob/main/notebooks/How_To_Use_Datasets_From_HF.ipynb Training Benchmark genomic datasets resources:

Summary:

2. You may need access in both HuggingFace and Meta Website. 3. Once approved you receive email from both (normall its quick within minutes) 4. Specially with email from meta, you get the link to download. 5. Then just follow, these steps– https://github.com/meta-llama/llama-models/blob/main/README.md Links: In Google colab: !cp -r /root/.llama/checkpoints/Llama-2-7b /content/drive/MyDrive/