Dolly 2.0 : Free ChatGPT-like Model for Commercial Use - How To Install And Use Locally On Your PC

Databricks’ #Dolly v2 is a free, open source, commercially useable ChatGPT-style #AI model. Dolly 2.0 could spark a new wave of fully open source LLMs similar to #ChatGPT . Open source community working hardest to bring up a model that can compete with GPT4. Our discord: https://bit.ly/SECoursesDiscord

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Technology & Science: News, Tips, Tutorials, Tricks, Best Applications, Guides, Reviews ⤵️ https://www.youtube.com/playlist?list=PL_pbwdIyffsnkay6X91BWb9rrfLATUMr3

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Gist file used in the video. The scripts are shared there ⤵️ https://gist.github.com/FurkanGozukara/c8eb2e2213a30182edb25333faea4dc5

Model link on Hugging Face databricks/dolly-v2-12b ⤵️ https://huggingface.co/databricks/dolly-v2-12b

How To Install Python, Setup Virtual Environment VENV ⤵️ https://youtu.be/B5U7LJOvH6g

BitSandBytes windows fork ⤵️ https://github.com/Keith-Hon/bitsandbytes-windows

databricks/dolly-v2-7b ⤵️ https://huggingface.co/databricks/dolly-v2-7b

databricks/dolly-v2-3b ⤵️ https://huggingface.co/databricks/dolly-v2-3b

0:00 Introduction to how to install and use Databricks’ Dolly v2 1:12 This is a video that is kind of teach a man how to fish not give a fish 1:26 I am sharing a Gradio interface to use Dolly v2 model with performance optimization 1:52 How to download / clone a big repository from Hugging Face 2:41 How to make a venv and install Dolly 2 model running requirements 5:19 Requirements installed. How to run Dolly v2 to do inference / text generation 7:14 How to download and use Gradio script to do inference with Dolly v2 8:16 How to use quantization : load in 8 bit of Hugging Face models 8:24 How to install and use BitSandBytes in Windows 8:55 How to run Gradio interface of Dolly v2 10:58 How to improve results you get from Dolly v2 11:55 How to use Microsoft Visual Studio to quickly run Python apps and debug them 12:21 How to change Python environment in Microsoft Visual Studio Community Free Edition 13:04 How to debug a Python application in Microsoft Visual Studio

Databricks’ dolly-v2-12b, an instruction-following large language model trained on the Databricks machine learning platform that is licensed for commercial use. Based on pythia-12b, Dolly is trained on ~15k instruction/response fine tuning records databricks-dolly-15k generated by Databricks employees in capability domains from the InstructGPT paper, including brainstorming, classification, closed QA, generation, information extraction, open QA and summarization. dolly-v2-12b is not a state-of-the-art model, but does exhibit surprisingly high quality instruction following behavior not characteristic of the foundation model on which it is based.

Model Overview dolly-v2-12b is a 12 billion parameter causal language model created by Databricks that is derived from EleutherAI’s Pythia-12b and fine-tuned on a ~15K record instruction corpus generated by Databricks employees and released under a permissive license (CC-BY-SA)

Performance Limitations dolly-v2-12b is not a state-of-the-art generative language model and, though quantitative benchmarking is ongoing, is not designed to perform competitively with more modern model architectures or models subject to larger pretraining corpuses.

The Dolly model family is under active development, and so any list of shortcomings is unlikely to be exhaustive, but we include known limitations and misfires here as a means to document and share our preliminary findings with the community. In particular, dolly-v2-12b struggles with: syntactically complex prompts, programming problems, mathematical operations, factual errors, dates and times, open-ended question answering, hallucination, enumerating lists of specific length, stylistic mimicry, having a sense of humor, etc.

Dataset Limitations Like all language models, dolly-v2-12b reflects the content and limitations of its training corpuses.

The Pile: GPT-J’s pre-training corpus contains content mostly collected from the public internet, and like most web-scale datasets, it contains content many users would find objectionable. As such, the model is likely to reflect these shortcomings, potentially overtly in the case it is explicitly asked to produce objectionable content, and sometimes subtly, as in the case of biased or harmful implicit associations.

databricks-dolly-15k: The training data on which dolly-v2-12b is instruction tuned represents natural language instructions generated by Databricks employees during a period spanning March and April 2023 and includes passages from Wikipedia as references passages for instruction categories like closed QA and summarization. ... https://www.youtube.com/watch?v=ku6UvK1bsp4