Resisting AI! #BigData #Analytics #DataScience #AI #MachineLearning #IoT #IIoT #PyTorch #Python #RStats #TensorFlow #Java #JavaScript #ReactJS #GoLang #CloudComputing #Serverless #DataScientist #Linux #Books #Programming #Coding #100DaysofCode https://
geni.us/ResistingAI
OPEN SOURCE
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Resisting AI: Big Data, Analytics and Machine Learning Technologies
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Llama API Integration Guide and Documentation Access
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Read more about llama-api and get a key here: https://
llama-api.com Documentation for the integration: https://
python.langchain.com/docs/modules/m
odel_io/models/chat/integrations/llama_api
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LlamaAPI Adds Function Calling Support to Llama2
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Function calling with Llama2? In our first addition to langchain_experimental, we're excited to integrate with llamaapi LlamaAPI is a hosted version of llama2 that adds in support for `functions` in the same way @OpenAI does Thanks to @edreisMD
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Meta releases Llama 2 foundation models from 7B to 70B parameters
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1/ Llama 2 – a collection of pretrained foundational models and fine-tuned chat models ranging in scale from 7B to 70B; Llama 2-Chat is competitive on a range of tasks and shows strong results on safety and helpfulness.
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Guide to Training Your Own Large Language Models
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How to train your own Large Language Models https://
bit.ly/44wZq2d #AI #MachineLearning #DeepLearning #LLMs #DataScience -
Build Semantic Search Applications Using ChromaDB Vector Database
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2) Build a semantic search application using ChromaDB https://
medium.com/ai-science/bui
ld-semantic-search-applications-using-open-source-vector-database-chromadb-a15e9e7f14ce
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LLM-GenAI Repository: Notebooks, Courses and Blogs
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1) LLM-GenAI repository – LLM and transformer notebooks
– Courses to learn more about LLMs and their application
– Blogs in this field Check for more details -
Run Llama 2 Locally on Your Computer Mac or Phone
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One of the coolest things about Llama 2 is that you can run it on your own computer, your M1/M2 Mac, or even on your phone. Follow our guide to learn how:
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Imperfect approach with simplified tutorial link
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To some extent only. This approach is far from perfect. Here is a (simplified) tutorial from a while back: https://
github.com/hardmaru/slime
volleygym/blob/master/TRAINING.md
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