How to [**Implement advanced RAG methods**](https://medium.com/decodingml/the-4-advanced-rag-algorithms-you-must-know-to-implement-5d0c7f1199d2) to optimize your retrieval and post-retrieval algorithm
How to [**Evaluate_Data_and_Models.pdf**](uploads/c3c2f531983d53d91d0d2ee71d16f44e/Evaluate_Data_and_Models.pdf) with Python Packages Yellowbrick and PiML (with Code)
How to use [**Whisper-CPP for speech recognition**](https://ai.plainenglish.io/whisper-cpp-the-quiet-genius-in-cpu-ai-speech-recognition-b54842e36532) - tutorial
How to [**Implement advanced RAG methods**](https://medium.com/decodingml/the-4-advanced-rag-algorithms-you-must-know-to-implement-5d0c7f1199d2) to optimize your retrieval and post-retrieval algorithm
How to [**run Local LLMs**](https://medium.com/@aadityaubhat/local-llms-on-apple-silicon-39194de71ab7) On Apple Silicon
How to use [**Whisper-CPP for speech recognition**](https://ai.plainenglish.io/whisper-cpp-the-quiet-genius-in-cpu-ai-speech-recognition-b54842e36532) - tutorial
How to [**Run Mixtral 8x7b.pdf**](uploads/1574f27f3dc96815e5b296a78aa167c6/Run_Mixtral_8x7b.pdf) on Google Colab for Free
How to [**run Local LLMs**](https://medium.com/@aadityaubhat/local-llms-on-apple-silicon-39194de71ab7) On Apple Silicon
How to start [**testing models on Hugging Face.pdf**](uploads/8fefb32f55207dd19e6b4158697f0eea/testing_models_on_Hugging_Face.pdf)(for beginners)
How to [**Run Mixtral 8x7b.pdf**](uploads/1574f27f3dc96815e5b296a78aa167c6/Run_Mixtral_8x7b.pdf) on Google Colab for Free
Simple approach to [**Testing LLM Language.pdf**](uploads/2d20d28547294864f01ef0e81bd3f43f/Testing_LLM_Language.pdf) capabilities (models from Hugging Face)
How to start [**testing models on Hugging Face.pdf**](uploads/8fefb32f55207dd19e6b4158697f0eea/testing_models_on_Hugging_Face.pdf)(for beginners)
A **guide to Open source LLMs** ([Part 1.pdf](uploads/9dbdb1c88d65215006f971cc29bd1e2a/Part_1.pdf)[Part 2.pdf](uploads/acc6443b6e0977ff7acda61c5778d6cd/Part_2.pdf) )
Simple approach to [**Testing LLM Language.pdf**](uploads/2d20d28547294864f01ef0e81bd3f43f/Testing_LLM_Language.pdf) capabilities (models from Hugging Face)
[**Evaluating LLM**](https://arxiv.org/html/2404.18796v1) Generations with a Panel of Diverse Models
A **guide to Open source LLMs** ([Part 1.pdf](uploads/9dbdb1c88d65215006f971cc29bd1e2a/Part_1.pdf)[Part 2.pdf](uploads/acc6443b6e0977ff7acda61c5778d6cd/Part_2.pdf) )
A complete guide to [**running local LLM models.pdf**](uploads/3764cbf71cc3d05bcb52eb1944c1224b/running_local_LLM_models.pdf)
[**Evaluating LLM**](https://arxiv.org/html/2404.18796v1) Generations with a Panel of Diverse Models
How to build apps with [**Tiny LLMs.pdf**](uploads/5504a5460eb3dba484c68a5d656ea269/Tiny_LLMs.pdf)
A complete guide to [**running local LLM models.pdf**](uploads/3764cbf71cc3d05bcb52eb1944c1224b/running_local_LLM_models.pdf)
[**Phi-2.pdf**](uploads/435b3b21528d10582d4a4aa3a8584a02/Phi-2.pdf)\*\* -\*\* a small but efficient model easy to fine-tune on GPU
How to build apps with [**Tiny LLMs.pdf**](uploads/5504a5460eb3dba484c68a5d656ea269/Tiny_LLMs.pdf)
How to use and train [**TinyGPT-V**](https://towardsdatascience.com/exploring-small-vision-language-models-with-tinygpt-v-499d37a1456d) - an “Small” Vision-Language Model example
[**Phi-2.pdf**](uploads/435b3b21528d10582d4a4aa3a8584a02/Phi-2.pdf)\*\* -\*\* a small but efficient model easy to fine-tune on GPU
How to use and train [**Mamba.pdf**](uploads/b32aa83b0666f82b0267deb6905927c1/Mamba.pdf) - a small LLM with a new architecture
How to use and train [**TinyGPT-V**](https://towardsdatascience.com/exploring-small-vision-language-models-with-tinygpt-v-499d37a1456d) - an “Small” Vision-Language Model example
Prompt Engineering Technique Called [**Step-Back Prompting**](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)
How to use and train [**Mamba.pdf**](uploads/b32aa83b0666f82b0267deb6905927c1/Mamba.pdf) - a small LLM with a new architecture
How to [**search through documents.pdf**](uploads/54b50b1bd38d0c132f4a8633a4bf0e94/search_through_documents.pdf) using ChatGPT with LLM and Vector database
Prompt Engineering Technique Called [**Step-Back Prompting**](https://cobusgreyling.medium.com/a-new-prompt-engineering-technique-has-been-introduced-called-step-back-prompting-b00e8954cacb)
How to build a [**tool for searchable documents.pdf**](uploads/c6d393fbc1520ade32d0ac97591d42e3/tool_for_searchable_documents.pdf) with data splitter, no database
How to [**search through documents.pdf**](uploads/54b50b1bd38d0c132f4a8633a4bf0e94/search_through_documents.pdf) using ChatGPT with LLM and Vector database
[**Semantic Search at Scale.pdf**](uploads/0e68a7b2e3c2a6639af575becf564297/Semantic_Search_at_Scale.pdf) - Index Millions of Docs with Fast Inference Times using FAISS and Sentence Transformers
How to build a [**tool for searchable documents.pdf**](uploads/c6d393fbc1520ade32d0ac97591d42e3/tool_for_searchable_documents.pdf) with data splitter, no database
Integrating Open Source LLMs and LangChain for [**Free Generative Question Answering from Documents**](https://ai.plainenglish.io/%EF%B8%8F-langchain-streamlit-llama-bringing-conversational-ai-to-your-local-machine-a1736252b172)
[**Semantic Search at Scale.pdf**](uploads/0e68a7b2e3c2a6639af575becf564297/Semantic_Search_at_Scale.pdf) - Index Millions of Docs with Fast Inference Times using FAISS and Sentence Transformers
Addressing the Challenge of [**Answering Questions Based on a Large Text Corpus**](https://towardsdatascience.com/the-research-agent-4ef8e6f1b741)
Integrating Open Source LLMs and LangChain for [**Free Generative Question Answering from Documents**](https://ai.plainenglish.io/%EF%B8%8F-langchain-streamlit-llama-bringing-conversational-ai-to-your-local-machine-a1736252b172)
Developing an [**Autonomous Dual-Chatbot System.pdf**](uploads/7afe69696c5f8b7e734f5633fd3c5333/Autonomous_Dual-Chatbot_System.pdf) for Research Paper Digesting
Addressing the Challenge of [**Answering Questions Based on a Large Text Corpus**](https://towardsdatascience.com/the-research-agent-4ef8e6f1b741)
[**Llama 2 and RAG stack**](https://jfan001.medium.com/how-to-connect-llama-2-to-your-own-data-privately-3e14a73e82a2) for local vector database
Developing an [**Autonomous Dual-Chatbot System.pdf**](uploads/7afe69696c5f8b7e734f5633fd3c5333/Autonomous_Dual-Chatbot_System.pdf) for Research Paper Digesting
How to [**Fine-Tune local Llama 2 Model**](https://levelup.gitconnected.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-70b8c851d931) in a Colab Notebook
[**Llama 2 and RAG stack**](https://jfan001.medium.com/how-to-connect-llama-2-to-your-own-data-privately-3e14a73e82a2) for local vector database
[**FineTuning the LLM.pdf**](uploads/fee1e5b9a4eea3889e561cb0402dc17d/FineTuning_the_LLM.pdf) Mistral 7B using LoRA and PEFT (Parameter Efficient Finetuning)
How to [**Fine-Tune local Llama 2 Model**](https://levelup.gitconnected.com/fine-tune-your-own-llama-2-model-in-a-colab-notebook-70b8c851d931) in a Colab Notebook
[**Finetuning**](https://medium.com/@geronimo7/finetuning-llama2-mistral-945f9c200611) Llama 2 and Mistral A with QLoRA
[**FineTuning the LLM.pdf**](uploads/fee1e5b9a4eea3889e561cb0402dc17d/FineTuning_the_LLM.pdf) Mistral 7B using LoRA and PEFT (Parameter Efficient Finetuning)
API using [**FastAPI for Local LLM**](https://artificialcorner.com/a-fastapi-for-your-local-llm-part-1-7bdfd2605b53)
[**Finetuning**](https://medium.com/@geronimo7/finetuning-llama2-mistral-945f9c200611) Llama 2 and Mistral A with QLoRA
Retrieval Augmented Generation Using [**Ollama_framework.pdf**](uploads/c685e21fb7a2ff3236e6bd2a183e3ca0/Ollama_framework.pdf)
API using [**FastAPI for Local LLM**](https://artificialcorner.com/a-fastapi-for-your-local-llm-part-1-7bdfd2605b53)
How to [**Adapt.pdf**](uploads/57640e11c6703153b227555c0ca08413/Adapt.pdf) pre-trained model to a new domain using HuggingFace
Retrieval Augmented Generation Using [**Ollama_framework.pdf**](uploads/c685e21fb7a2ff3236e6bd2a183e3ca0/Ollama_framework.pdf)
[**Building a Reward Model**.pdf](uploads/5f57b475de79463da0ecb967a5e2cfcd/Building_a_Reward_Model.pdf) for your LLM Using RLHF in Python
How to [**Adapt.pdf**](uploads/57640e11c6703153b227555c0ca08413/Adapt.pdf) pre-trained model to a new domain using HuggingFace
How to [**Fine-Tune local Llama 2 Model.pdf**](uploads/68437a8e205cc32b7eaec0677bbf9711/Fine-Tune_local_Llama_2_Model.pdf) in a Colab Notebook
[**Building a Reward Model**.pdf](uploads/5f57b475de79463da0ecb967a5e2cfcd/Building_a_Reward_Model.pdf) for your LLM Using RLHF in Python
Using LLM’s for [**Document Retrieval and Reranking**](https://medium.com/llamaindex-blog/using-llms-for-retrieval-and-reranking-23cf2d3a14b6)
How to [**Fine-Tune local Llama 2 Model.pdf**](uploads/68437a8e205cc32b7eaec0677bbf9711/Fine-Tune_local_Llama_2_Model.pdf) in a Colab Notebook
Eight Major Methods For [**FineTuning an LLM.pdf**](uploads/eb944b65628d872c467e5f96c5b55ab9/FineTuning_an_LLM.pdf)
Using LLM’s for [**Document Retrieval and Reranking**](https://medium.com/llamaindex-blog/using-llms-for-retrieval-and-reranking-23cf2d3a14b6)
How To [**Validate Machine Learning Models**](https://towardsdatascience.com/how-you-should-validate-machine-learning-models-f16e9f8a8f7a)
Eight Major Methods For [**FineTuning an LLM.pdf**](uploads/eb944b65628d872c467e5f96c5b55ab9/FineTuning_an_LLM.pdf)
[**CLIP.pdf**](uploads/67ebd9db5479cacfa55de942026b14af/CLIP.pdf) - a strategy for creating vision and language representations to make classifiers without any training data.
How To [**Validate Machine Learning Models**](https://towardsdatascience.com/how-you-should-validate-machine-learning-models-f16e9f8a8f7a)
[**How Unstructured and LlamaIndex can help bring the power of LLM’s to your own data**](https://medium.com/@jerryjliu98/how-unstructured-and-llamaindex-can-help-bring-the-power-of-llms-to-your-own-data-3657d063e30d)
[**CLIP.pdf**](uploads/67ebd9db5479cacfa55de942026b14af/CLIP.pdf) - a strategy for creating vision and language representations to make classifiers without any training data.
How to Install and Run [**Dolly2 and LangChain**](https://ashukumar27.medium.com/dolly2-and-langchain-a-game-changer-for-text-data-analytics-7518d48d0ad7): A Game Changer for Data Analytics
[**How Unstructured and LlamaIndex can help bring the power of LLM’s to your own data**](https://medium.com/@jerryjliu98/how-unstructured-and-llamaindex-can-help-bring-the-power-of-llms-to-your-own-data-3657d063e30d)
Using Large Language Models for [**Data Labelling**](https://betterprogramming.pub/using-large-language-models-for-data-labeling-1357f2880a38)
How to Install and Run [**Dolly2 and LangChain**](https://ashukumar27.medium.com/dolly2-and-langchain-a-game-changer-for-text-data-analytics-7518d48d0ad7): A Game Changer for Data Analytics
Introduction and Development Guide for [**StarCoder**](https://levelup.gitconnected.com/starcoder-a-new-ai-model-that-surprised-me-on-coding-assistance-b49e9d334bcf) — Coding Assistance AI model
Using Large Language Models for [**Data Labelling**](https://betterprogramming.pub/using-large-language-models-for-data-labeling-1357f2880a38)
Four Approaches to [**build on top of Generative AI Foundational Model**](https://towardsdatascience.com/four-approaches-to-build-on-top-of-generative-ai-foundational-models-43c1a64cffd5)
Introduction and Development Guide for [**StarCoder**](https://levelup.gitconnected.com/starcoder-a-new-ai-model-that-surprised-me-on-coding-assistance-b49e9d334bcf) — Coding Assistance AI model
How to use [**<span dir="">Decision Transformer for reinforcement learning</span>**](https://pub.towardsai.net/taking-a-walk-in-the-openai-gym-using-decision-transformer-to-power-reinforcement-learning-52be4951912e)
Four Approaches to [**build on top of Generative AI Foundational Model**](https://towardsdatascience.com/four-approaches-to-build-on-top-of-generative-ai-foundational-models-43c1a64cffd5)
How to [**evaluate LLM**](https://humanloop.com/blog/evaluating-llm-apps) Applications
How to use [**<span dir="">Decision Transformer for reinforcement learning</span>**](https://pub.towardsai.net/taking-a-walk-in-the-openai-gym-using-decision-transformer-to-power-reinforcement-learning-52be4951912e)
How to [**Docker GPT-3** and run it **on Kubernetes** ](https://yashwanth-nimmala.medium.com/next-big-thing-chatgpt-on-kubernetes-9b3e276852b6)(K8s)
How to [**evaluate LLM**](https://humanloop.com/blog/evaluating-llm-apps) Applications
[**Leveraging imitation.pdf**](uploads/8c36a4f79e6045dcb9fb3af1cba2206d/Leveraging_imitation.pdf) to create high-quality, open-source LLMs - example ORCA
How to [**Docker GPT-3** and run it **on Kubernetes** ](https://yashwanth-nimmala.medium.com/next-big-thing-chatgpt-on-kubernetes-9b3e276852b6)(K8s)
[**Fine-tuning 20B LLMs**](https://huggingface.co/blog/trl-peft) with RLHF on a 24GB consumer GPU
[**Leveraging imitation.pdf**](uploads/8c36a4f79e6045dcb9fb3af1cba2206d/Leveraging_imitation.pdf) to create high-quality, open-source LLMs - example ORCA
How to [**build Large AI Models** ](https://medium.com/geekculture/how-to-build-large-ai-models-like-chatgpt-efficiently-1ec0bc33874f)like ChatGPT efficiently
[**Fine-tuning 20B LLMs**](https://huggingface.co/blog/trl-peft) with RLHF on a 24GB consumer GPU
[**Language Cross Transfer**](https://arxiv.org/abs/2301.09626) - paper about transferring the language knowledge between LLMs
How to [**build Large AI Models** ](https://medium.com/geekculture/how-to-build-large-ai-models-like-chatgpt-efficiently-1ec0bc33874f)like ChatGPT efficiently
Using [**ChatDOC**](https://medium.com/@chatdocai/revolutionizing-rag-with-enhanced-pdf-structure-recognition-22227af87442) for RAG with Enhanced PDF Structure Recognition
[**Language Cross Transfer**](https://arxiv.org/abs/2301.09626) - paper about transferring the language knowledge between LLMs
Using [**ChatDOC**](https://medium.com/@chatdocai/revolutionizing-rag-with-enhanced-pdf-structure-recognition-22227af87442) for RAG with Enhanced PDF Structure Recognition
[**PDF Parsing.pdf**](uploads/9e01a81a39dca88e4061e5a402bd1286/PDF_Parsing.pdf) for RAG, the extraction of information from documents
[**PDF Parsing.pdf**](uploads/9e01a81a39dca88e4061e5a402bd1286/PDF_Parsing.pdf) for RAG, the extraction of information from documents
