A.I. cleanup (akash-network#383)

alpaca-cpp/README.md

@@ -2,63 +2,13 @@
 
 Run a fast ChatGPT-like model locally on your device. The screencast below is not sped up and running on an M2 Macbook Air with 4GB of weights.
 
-[![asciicast](screencast.gif)](https://asciinema.org/a/dfJ8QXZ4u978Ona59LPEldtKK)
-
-
-This combines the [LLaMA foundation model](https://github.com/facebookresearch/llama) with an [open reproduction](https://github.com/tloen/alpaca-lora) of [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) a fine-tuning of the base model to obey instructions (akin to the [RLHF](https://huggingface.co/blog/rlhf) used to train ChatGPT) and a set of modifications to [llama.cpp](https://github.com/ggerganov/llama.cpp) to add a chat interface. 
-
-## Get Started (7B)
-
-Download the zip file corresponding to your operating system from the [latest release](https://github.com/antimatter15/alpaca.cpp/releases/latest). On Windows, download `alpaca-win.zip`, on Mac (both Intel or ARM) download `alpaca-mac.zip`, and on Linux (x64) download `alpaca-linux.zip`. 
-
-Download `ggml-alpaca-7b-q4.bin` and place it in the same folder as the `chat` executable in the zip file. There are several options: 
-
-Once you've downloaded the model weights and placed them into the same directory as the `chat` or `chat.exe` executable, run:
-
-```
-./chat
-```
-
-The weights are based on the published fine-tunes from `alpaca-lora`, converted back into a pytorch checkpoint with a [modified script](https://github.com/tloen/alpaca-lora/pull/19) and then quantized with llama.cpp the regular way. 
-
-## Building from Source (MacOS/Linux)
-
-
-```sh
-git clone https://github.com/antimatter15/alpaca.cpp
-cd alpaca.cpp
-
-make chat
-./chat
-```
-
-
-## Building from Source (Windows)
-
-- Download and install CMake: <https://cmake.org/download/>
-- Download and install `git`. If you've never used git before, consider a GUI client like <https://desktop.github.com/>
-- Clone this repo using your git client of choice (for GitHub Desktop, go to File -> Clone repository -> From URL and paste `https://github.com/antimatter15/alpaca.cpp` in as the URL)
-- Open a Windows Terminal inside the folder you cloned the repository to
-- Run the following commands one by one:
-
-```ps1
-cmake .
-cmake --build . --config Release
-```
-
-- Download the weights via any of the links in "Get started" above, and save the file as `ggml-alpaca-7b-q4.bin` in the main Alpaca directory.
-- In the terminal window, run this command:
-```ps1
-.\Release\chat.exe
-```
-- (You can add other launch options like `--n 8` as preferred onto the same line)
-- You can now type to the AI in the terminal and it will reply. Enjoy!
+This combines the [LLaMA foundation model](https://github.com/facebookresearch/llama) with an [open reproduction](https://github.com/tloen/alpaca-lora) of [Stanford Alpaca](https://github.com/tatsu-lab/stanford_alpaca) a fine-tuning of the base model to obey instructions (akin to the [RLHF](https://huggingface.co/blog/rlhf) used to train ChatGPT) and a set of modifications to [llama.cpp](https://github.com/ggerganov/llama.cpp) to add a chat interface.
 
 ## Credit
 
-This combines [Facebook's LLaMA](https://github.com/facebookresearch/llama), [Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [alpaca-lora](https://github.com/tloen/alpaca-lora) and [corresponding weights](https://huggingface.co/tloen/alpaca-lora-7b/tree/main) by Eric Wang (which uses [Jason Phang's implementation of LLaMA](https://github.com/huggingface/transformers/pull/21955) on top of Hugging Face Transformers), and [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov. The chat implementation is based on Matvey Soloviev's [Interactive Mode](https://github.com/ggerganov/llama.cpp/pull/61) for llama.cpp. Inspired by [Simon Willison's](https://til.simonwillison.net/llms/llama-7b-m2) getting started guide for LLaMA. [Andy Matuschak](https://twitter.com/andy_matuschak/status/1636769182066053120)'s thread on adapting this to 13B, using fine tuning weights by [Sam Witteveen](https://huggingface.co/samwit/alpaca13B-lora). 
+This combines [Facebook's LLaMA](https://github.com/facebookresearch/llama), [Stanford Alpaca](https://crfm.stanford.edu/2023/03/13/alpaca.html), [alpaca-lora](https://github.com/tloen/alpaca-lora) and [corresponding weights](https://huggingface.co/tloen/alpaca-lora-7b/tree/main) by Eric Wang (which uses [Jason Phang's implementation of LLaMA](https://github.com/huggingface/transformers/pull/21955) on top of Hugging Face Transformers), and [llama.cpp](https://github.com/ggerganov/llama.cpp) by Georgi Gerganov. The chat implementation is based on Matvey Soloviev's [Interactive Mode](https://github.com/ggerganov/llama.cpp/pull/61) for llama.cpp. Inspired by [Simon Willison's](https://til.simonwillison.net/llms/llama-7b-m2) getting started guide for LLaMA. [Andy Matuschak](https://twitter.com/andy_matuschak/status/1636769182066053120)'s thread on adapting this to 13B, using fine tuning weights by [Sam Witteveen](https://huggingface.co/samwit/alpaca13B-lora).
 
 
 ## Disclaimer
 
-Note that the model weights are only to be used for research purposes, as they are derivative of LLaMA, and uses the published instruction data from the Stanford Alpaca project which is generated by OpenAI, which itself disallows the usage of its outputs to train competing models. 
+Note that the model weights are only to be used for research purposes, as they are derivative of LLaMA, and uses the published instruction data from the Stanford Alpaca project which is generated by OpenAI, which itself disallows the usage of its outputs to train competing models.

babyagi-ui/README.md

@@ -26,41 +26,6 @@ This is a port of [babyagi](https://github.com/yoheinakajima/babyagi) with [Lang
 
 and more ...
 
-## 👉 Getting Started
-
-1. Clone the repository
-
-```sh
-git clone https://github.com/miurla/babyagi-ui
-```
-
-2. Go to the project holder
-
-```sh
-cd babyagi-ui
-```
-
-3. Install packages with npm
-
-```sh
-npm install
-```
-
-4. Setup your .env file. And set the variables.
-   - You need to create an index in advance with [Pinecone](https://www.pinecone.io/).
-     - [Reference setting](./public/pinecone-setup.png)
-   - Set your SerpAPI Key, if you want to use the search tool with BabyBeeAGI.
-
-```sh
-cp .env.example .env
-```
-
-5. Run the project
-
-```sh
-npm run dev
-```
-
 ## ⚠️ Warning
 
 This script is designed to be run continuously as part of a task management system. Running this script continuously can result in high API usage, so please use it responsibly. Additionally, the script requires the OpenAI API to be set up correctly, so make sure you have set up the API before running the script.

babyagi/README.md

@@ -12,15 +12,14 @@ This README will cover the following:
 
 - [Warning about running the script continuously](#continous-script-warning)
 
-# How It Works<a name="how-it-works"></a>
+# How It Works
 
 The script works by running an infinite loop that does the following steps:
 
 1. Pulls the first task from the task list.
 2. Sends the task to the execution agent, which uses OpenAI's API to complete the task based on the context.
 3. Enriches the result and stores it in [Chroma](https://docs.trychroma.com)/[Weaviate](https://weaviate.io/).
 4. Creates new tasks and reprioritizes the task list based on the objective and the result of the previous task.
-   </br>
 
 ![image](https://user-images.githubusercontent.com/21254008/235015461-543a897f-70cc-4b63-941a-2ae3c9172b11.png)
 
@@ -32,45 +31,20 @@ The `prioritization_agent()` function is where OpenAI's API is used to repriorit
 
 Finally, the script uses Chroma/Weaviate to store and retrieve task results for context. The script creates a Chroma/Weaviate collection based on the table name specified in the TABLE_NAME variable. Chroma/Weaviate is then used to store the results of the task in the collection, along with the task name and any additional metadata.
 
-# How to Use<a name="how-to-use"></a>
 
-To use the script, you will need to follow these steps:
-
-1. Clone the repository via `git clone https://github.com/yoheinakajima/babyagi.git` and `cd` into the cloned repository.
-2. Install the required packages: `pip install -r requirements.txt`
-3. Copy the .env.example file to .env: `cp .env.example .env`. This is where you will set the following variables.
-4. Set your OpenAI API key in the OPENAI_API_KEY and OPENAPI_API_MODEL variables. In order to use with Weaviate you will also need to setup additional variables detailed [here](docs/weaviate.md).
-5. Set the name of the table where the task results will be stored in the TABLE_NAME variable.
-6. (Optional) Set the name of the BabyAGI instance in the BABY_NAME variable.
-7. (Optional) Set the objective of the task management system in the OBJECTIVE variable.
-8. (Optional) Set the first task of the system in the INITIAL_TASK variable.
-9. Run the script: `python babyagi.py`
-
-All optional values above can also be specified on the command line.
-
-# Running inside a docker container
-
-As a prerequisite, you will need docker and docker-compose installed. Docker desktop is the simplest option https://www.docker.com/products/docker-desktop/
-
-To run the system inside a docker container, setup your .env file as per steps above and then run the following:
-
-```
-docker-compose up
-```
-
-# Supported Models<a name="supported-models"></a>
+# Supported Models
 
 This script works with all OpenAI models, as well as Llama and its variations through Llama.cpp. Default model is **gpt-3.5-turbo**. To use a different model, specify it through LLM_MODEL or use the command line.
 
 ## Llama
 
-Llama integration requires llama-cpp package. You will also need the Llama model weights. 
+Llama integration requires llama-cpp package. You will also need the Llama model weights.
 
 - **Under no circumstances share IPFS, magnet links, or any other links to model downloads anywhere in this repository, including in issues, discussions or pull requests. They will be immediately deleted.**
 
 Once you have them, set LLAMA_MODEL_PATH to the path of the specific model to use. For convenience, you can link `models` in BabyAGI repo to the folder where you have the Llama model weights. Then run the script with `LLM_MODEL=llama` or `-l` argument.
 
-# Warning<a name="continous-script-warning"></a>
+# Warning
 
 This script is designed to be run continuously as part of a task management system. Running this script continuously can result in high API usage, so please use it responsibly. Additionally, the script requires the OpenAI API to be set up correctly, so make sure you have set up the API before running the script.
 
@@ -93,8 +67,6 @@ To help the BabyAGI community stay informed about the project's progress, Bluepr
 
 To view the BabyAGI 7-day activity report, go here: [https://app.blueprint.ai/github/yoheinakajima/babyagi](https://app.blueprint.ai/github/yoheinakajima/babyagi)
 
-[<img width="293" alt="image" src="https://user-images.githubusercontent.com/334530/235789974-f49d3cbe-f4df-4c3d-89e9-bfb60eea6308.png">](https://app.blueprint.ai/github/yoheinakajima/babyagi)
-
 
 # Inspired projects
 

chatchat/README.md

@@ -70,28 +70,6 @@ Please refer to https://github.com/users/okisdev/projects/7
 
 > Please modify the environment variables before deployment, more details can be found in the [documentation](https://docs.okis.dev/chat/deployment/).
 
-#### Local Deployment
-
-```bash
-git clone
-cd ChatChat
-yarn
-yarn dev
-```
-
-#### Docker
-
-```bash
-docker build -t chatchat .
-docker run -p 3000:3000 chatchat -e BASE_URL="" -e DATABASE_URL="" -e NEXTAUTH_URL="" -e NEXTAUTH_SECRET="" -e OPENAI_API_KEY="" -e OPENAI_API_ENDPOINT="" -e EMAIL_HOST="" -e EMAIL_PORT="" -e EMAIL_USERNAME="" -e EMAIL_PASSWORD="" -e EMAIL_FORM=""
-```
-
-OR
-
-```bash
-docker run -p 3000:3000 ghcr.io/okisdev/chatchat:latest -e BASE_URL="" -e DATABASE_URL="" -e NEXTAUTH_URL="" -e NEXTAUTH_SECRET="" -e OPENAI_API_KEY="" -e OPENAI_API_ENDPOINT="" -e EMAIL_HOST="" -e EMAIL_PORT="" -e EMAIL_USERNAME="" -e EMAIL_PASSWORD="" -e EMAIL_FORM=""
-```
-
 ## LICENSE
 
 [AGPL-3.0](./LICENSE)
@@ -102,8 +80,4 @@ docker run -p 3000:3000 ghcr.io/okisdev/chatchat:latest -e BASE_URL="" -e DATABA
 
 ## Technology Stack
 
-nextjs / tailwindcss / shadcn UI
-
-```
-
-```
+```nextjs / tailwindcss / shadcn UI```