Q:
Create a tabulated rendition of the product demo text using the following instructions:
1. Render in a code-block
1. Use Markdown with `## Markdown Start ##` and `## Markown End ##` placed around the generated Markdown, within the code-block
1. Table heading "LLMs For Commercial Use"

A snapshot of today's open-source LLMs space every builder should know.


𝗙𝗹𝗮𝗻-𝗨𝗟𝟮
Google’s open source LLM
Demo https://lnkd.in/gWj6deRJ 

𝗢𝗽𝗲𝗻𝗖𝗵𝗮𝘁𝗞𝗶𝘁
Create specialized and general purpose chatbots
Demo https://lnkd.in/gfY9t7Vu

𝗖𝗲𝗿𝗲𝗯𝗿𝗮𝘀-𝗚𝗣𝗧
A family of seven GPT-3 models from 111M to 13B parameters
Demo https://lnkd.in/gr4sqB_w

𝗣𝘆𝘁𝗵𝗶𝗮
A family of 16 language models from 70M-12B parameters from EleutherAI
Demo https://lnkd.in/gcE9qXkE 
Code https://lnkd.in/gRVJTVKv

𝗕𝗹𝗼𝗼𝗺 & 𝗺𝗧𝗢
A family of models capable of following human instructions in dozens of languages zero-shot
Demo https://lnkd.in/gURNQjsC

𝗢𝗽𝗲𝗻𝗔𝘀𝘀𝗶𝘀𝘁𝗮𝗻𝘁
A chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically. The demo uses a fine-tuned 30B LLaMA. 
Demo https://lnkd.in/gu4GTtTD

𝗻𝗮𝗻𝗼𝗧𝟱
Pre-training and fine-tuning T5-style models
https://lnkd.in/gf5rXKrn

𝗚𝗲𝗼𝗩
A 9B pre-trained LLM using Rotary Positional Embeddings with Relative distances (RoPER)
Code https://lnkd.in/gbsk8NeZ

A:

## Markdown Start ##

| LLMs For Commercial Use | Description | Demo | Code |
|-------------------------|-------------|------|------|
| Flan-UL2 | Google’s open source LLM | [Demo](https://lnkd.in/gWj6deRJ) | |
| OpenChatKit | Create specialized and general purpose chatbots | [Demo](https://lnkd.in/gfY9t7Vu) | |
| Cerebras-GPT | A family of seven GPT-3 models from 111M to 13B parameters | [Demo](https://lnkd.in/gr4sqB_w) | |
| Pythia | A family of 16 language models from 70M-12B parameters from EleutherAI | [Demo](https://lnkd.in/gcE9qXkE) | [Code](https://lnkd.in/gRVJTVKv) |
| Bloom & mTO | A family of models capable of following human instructions in dozens of languages zero-shot | [Demo](https://lnkd.in/gURNQjsC) | |
| OpenAssistant | A chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically. The demo uses a fine-tuned 30B LLaMA. | [Demo](https://lnkd.in/gu4GTtTD) | |
| nanoT5 | Pre-training and fine-tuning T5-style models | | [Code](https://lnkd.in/gf5rXKrn) |
| GeoV | A 9B pre-trained LLM using Rotary Positional Embeddings with Relative distances (RoPER) | | [Code](https://lnkd.in/gbsk8NeZ) |

## Markdown End ##

Redo, but for the following using table heading "LLMs For Research Use"

𝗕𝗮𝗶𝘇𝗲
Open-source chat model trained with LoRA using 100k dialogs generated by letting ChatGPT chat with itself. 
Demo https://lnkd.in/g_npB3ut 
Code https://lnkd.in/ghCwWeg6

𝗩𝗶𝗰𝘂𝗻𝗮
An Open-Source Chatbot achieving almost the same performance as Google's Bard and ChatGPT
Demo https://chat.lmsys.org/ 
Code https://lnkd.in/grTJA5G5

𝗞𝗼𝗮𝗹𝗮
A chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. 
Demo https://lnkd.in/gKWAgq58 
Code https://lnkd.in/g7Tm_MRq

𝗚𝗣𝗧𝟰𝗔𝗹𝗹
Train an assistant-style LLM with ~800k GPT-3.5-Turbo Generations based on LLaMa.
Code https://lnkd.in/gB3gKBud

𝗟𝗶𝘁-𝗟𝗟𝗮𝗠𝗔
Independent implementation of LLaMA that builds on nanoGPT
Code https://lnkd.in/gfA5rb4Z

𝗗𝗼𝗹𝗹𝘆 (𝗗𝗮𝘁𝗮𝗯𝗿𝗶𝗰𝗸𝘀)
An LLM trained using GPT-J and fine tuned on Stanford Alpaca
Code https://lnkd.in/gw_b2jdf

𝗗𝗮𝗹𝗮𝗶
The fastest way to run LLaMA and Alpaca locally (includes a user interface)
Code https://lnkd.in/gJnE-3F7

𝗔𝗹𝗽𝗮𝗰𝗮.𝗰𝗽𝗽
Run a fast ChatGPT-like model locally on your device
Code https://lnkd.in/gDRzZWSc

𝗔𝗹𝗽𝗮𝗰𝗮-𝗟𝗼𝗥𝗔
An Instruct model of similar quality to text-davinci-003 that can run on local devices 
Demo https://lnkd.in/gfh7FM7X 
Code https://lnkd.in/gZ5dKGKW

𝗹𝗹𝗮𝗺𝗮.𝗰𝗽𝗽
Inference of LLaMA model in pure C/C++ supporting LLaMA, Alpaca, GPT4All, and Vicuna
Code https://lnkd.in/gkZ8XaJJ

𝗖𝗼𝗹𝗼𝘀𝘀𝗮𝗹𝗖𝗵𝗮𝘁
LLM trained with RLHF powered by Colossal-AI
Code https://lnkd.in/gTezhGXD

A:

## Markdown Start ##

| LLMs For Research Use | Description | Demo | Code |
|-----------------------|-------------|------|------|
| Baize | Open-source chat model trained with LoRA using 100k dialogs generated by letting ChatGPT chat with itself. | [Demo](https://lnkd.in/g_npB3ut) | [Code](https://lnkd.in/ghCwWeg6) |
| Vicuna | An Open-Source Chatbot achieving almost the same performance as Google's Bard and ChatGPT | [Demo](https://chat.lmsys.org/) | [Code](https://lnkd.in/grTJA5G5) |
| Koala | A chatbot trained by fine-tuning Meta’s LLaMA on dialogue data gathered from the web. | [Demo](https://lnkd.in/gKWAgq58) | [Code](https://lnkd.in/g7Tm_MRq) |
| GPT4All | Train an assistant-style LLM with ~800k GPT-3.5-Turbo Generations based on LLaMa. | | [Code](https://lnkd.in/gB3gKBud) |
| Lit-LLaMA | Independent implementation of LLaMA that builds on nanoGPT | | [Code](https://lnkd.in/gfA5rb4Z) |
| Dolly (Databricks) | An LLM trained using GPT-J and fine-tuned on Stanford Alpaca | | [Code](https://lnkd.in/gw_b2jdf) |
| Dalai | The fastest way to run LLaMA and Alpaca locally (includes a user interface) | | [Code](https://lnkd.in/gJnE-3F7) |
| Alpaca.cpp | Run a fast ChatGPT-like model locally on your device | | [Code](https://lnkd.in/gDRzZWSc) |
| Alpaca-LoRA | An Instruct model of similar quality to text-davinci-003 that can run on local devices | [Demo](https://lnkd.in/gfh7FM7X) | [Code](https://lnkd.in/gZ5dKGKW) |
| llama.cpp | Inference of LLaMA model in pure C/C++ supporting LLaMA, Alpaca, GPT4All, and Vicuna | | [Code](https://lnkd.in/gkZ8XaJJ) |
| ColossalChat | LLM trained with RLHF powered by Colossal-AI | | [Code](https://lnkd.in/gTezhGXD) |

## Markdown End ##