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https://raw.githubusercontent.com/ollama/ollama/main/README.md
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[ [ "window", "coming", "soon", "!", ",", "install", "ollama", "window", "via", "wsl2", "." ], [ "window coming soon !", ", install ollama window via wsl2 ." ] ]
Windows Coming soon! For now, you can install Ollama on Windows via WSL2.
https://github.com/ollama/ollama
-1
[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]
https://raw.githubusercontent.com/ollama/ollama/main/README.md
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[ [ "linux", "&", "wsl2", "``", "`", "curl", "http", ":", "//ollama.ai/install.sh", "|", "sh", "``", "`", "[", "manual", "install", "instruction", "]", "(", "http", ":", "//github.com/jmorganca/ollama/blob/main/docs/linux.md", ...
Linux & WSL2 ``` curl https://ollama.ai/install.sh | sh ``` [Manual install instructions](https://github.com/jmorganca/ollama/blob/main/docs/linux.md)
https://github.com/ollama/ollama
-1
[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]
https://raw.githubusercontent.com/ollama/ollama/main/README.md
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Building Install `cmake` and `go`: ``` brew install cmake go ``` Then generate dependencies: ``` go generate ./... ``` Then build the binary: ``` go build . ``` More detailed instructions can be found in the [developer guide](https://github.com/jmorganca/ollama/blob/main/docs/development.md)
https://github.com/ollama/ollama
-1
[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]
https://raw.githubusercontent.com/ollama/ollama/main/README.md
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[ [ "extension", "&", "plugins", "-", "[", "raycast", "extension", "]", "(", "http", ":", "//github.com/massimilianopasquini97/raycast_ollama", ")", "-", "[", "discollama", "]", "(", "http", ":", "//github.com/mxyng/discollama", "...
Extensions & Plugins - [Raycast extension](https://github.com/MassimilianoPasquini97/raycast_ollama) - [Discollama](https://github.com/mxyng/discollama) (Discord bot inside the Ollama discord channel) - [Continue](https://github.com/continuedev/continue) - [Obsidian Ollama plugin](https://github.com/hinterdupfinger/ob...
https://github.com/ollama/ollama
-1
[ "go", "golang", "llama", "llama2", "llm", "llms", "mistral", "ollama" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
[ [ "install" ], [ "install" ] ]
[ [ "install" ], [ "install" ] ]
Install
https://github.com/geekan/MetaGPT
-1
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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Pip installation > Ensure that Python 3.9+ is installed on your system. You can check this by using: `python --version`. > You can use conda like this: `conda create -n metagpt python=3.9 && conda activate metagpt` ```bash pip install metagpt metagpt --init-config
https://github.com/geekan/MetaGPT
0
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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[ [ "print", "repo", "structure", "file", "``", "`", "detail", "installation", "please", "refer", "[", "cli_install", "]", "(", "http", ":", "//docs.deepwisdom.ai/main/en/guide/get_started/installation.html", "#", "install-stable-version", ...
it will print the repo structure with files ``` detail installation please refer to [cli_install](https://docs.deepwisdom.ai/main/en/guide/get_started/installation.html#install-stable-version)
https://github.com/geekan/MetaGPT
-1
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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Docker installation > Note: In the Windows, you need to replace "/opt/metagpt" with a directory that Docker has permission to create, such as "D:\Users\x\metagpt" ```bash
https://github.com/geekan/MetaGPT
-1
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https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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Step 2: Run metagpt demo with container docker run --rm \ --privileged \ -v /opt/metagpt/config/config2.yaml:/app/metagpt/config/config2.yaml \ -v /opt/metagpt/workspace:/app/metagpt/workspace \ metagpt/metagpt:latest \ metagpt "Create a 2048 game" ``` detail installation please refer to [docker_in...
https://github.com/geekan/MetaGPT
1
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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QuickStart & Demo Video - Try it on [MetaGPT Huggingface Space](https://huggingface.co/spaces/deepwisdom/MetaGPT) - [Matthew Berman: How To Install MetaGPT - Build A Startup With One Prompt!!](https://youtu.be/uT75J_KG_aY) - [Official Demo Video](https://github.com/geekan/MetaGPT/assets/2707039/5e8c1062-8c35-440f-bb20-...
https://github.com/geekan/MetaGPT
-1
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/geekan/MetaGPT/main/README.md
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Tutorial - 🗒 [Online Document](https://docs.deepwisdom.ai/main/en/) - 💻 [Usage](https://docs.deepwisdom.ai/main/en/guide/get_started/quickstart.html) - 🔎 [What can MetaGPT do?](https://docs.deepwisdom.ai/main/en/guide/get_started/introduction.html) - 🛠 How to build your own agents? - [MetaGPT Usage & Developm...
https://github.com/geekan/MetaGPT
-1
[ "agent", "gpt", "hacktoberfest", "llm", "metagpt", "multi-agent" ]
https://raw.githubusercontent.com/run-llama/llama_index/main/README.md
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💻 Example Usage ``` pip install llama-index ``` Examples are in the `examples` folder. Indices are in the `indices` folder (see list of indices below). To build a simple vector store index using OpenAI: ```python import os os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY" from llama_index import VectorStoreIn...
https://github.com/run-llama/llama_index
0
[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]
https://raw.githubusercontent.com/run-llama/llama_index/main/README.md
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🔧 Dependencies The main third-party package requirements are `tiktoken`, `openai`, and `langchain`. All requirements should be contained within the `setup.py` file. To run the package locally without building the wheel, simply run: ```bash pip install poetry poetry install --with dev ```
https://github.com/run-llama/llama_index
0
[ "agents", "application", "data", "fine-tuning", "framework", "llamaindex", "llm", "rag", "vector-database" ]
https://raw.githubusercontent.com/QuivrHQ/quivr/main/README.md
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Getting Started 🚀 Follow these instructions to get a copy of the project up and running on your local machine for development and testing purposes. You can find everything on the [documentation](https://docs.quivr.app/).
https://github.com/QuivrHQ/quivr
-1
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https://raw.githubusercontent.com/QuivrHQ/quivr/main/README.md
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Prerequisites 📋 Ensure you have the following installed: - Docker - Docker Compose
https://github.com/QuivrHQ/quivr
-1
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https://raw.githubusercontent.com/QuivrHQ/quivr/main/README.md
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60 seconds Installation 💽 You can find the installation video [here](https://www.youtube.com/watch?v=cXBa6dZJN48). - **Step 0**: Supabase CLI Follow the instructions [here](https://supabase.com/docs/guides/cli/getting-started) to install the Supabase CLI that is required. ```bash supabase -v
https://github.com/QuivrHQ/quivr
-1
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https://raw.githubusercontent.com/QuivrHQ/quivr/main/README.md
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Check that the installation worked ``` - **Step 1**: Clone the repository: ```bash git clone https://github.com/quivrhq/quivr.git && cd Quivr ``` - **Step 2**: Copy the `.env.example` files ```bash cp .env.example .env ``` - **Step 3**: Update the `.env` files ```bash vim .env
https://github.com/QuivrHQ/quivr
2
[ "ai", "api", "chatbot", "chatgpt", "database", "docker", "frontend", "html", "javascript", "llm", "openai", "postgresql", "privacy", "rag", "react", "rest-api", "security", "typescript", "vector", "ycombinator" ]
https://raw.githubusercontent.com/JushBJJ/Mr.-Ranedeer-AI-Tutor/main/README.md
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Table of Contents - [Mr. Ranedeer: Your personalized AI Tutor!](#mr-ranedeer-your-personalized-ai-tutor) - [Table of Contents](#table-of-contents) - [Why Mr. Ranedeer?](#why-mr-ranedeer) - [Requirements and Compatibility](#requirements-and-compatibility) - [Recommended](#recommended) - [Not Recommended](#...
https://github.com/JushBJJ/Mr.-Ranedeer-AI-Tutor
-1
[ "ai", "education", "gpt-4", "llm" ]
https://raw.githubusercontent.com/JushBJJ/Mr.-Ranedeer-AI-Tutor/main/README.md
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Quick Start Guide 1. Click [this link](https://chat.openai.com/g/g-9PKhaweyb-mr-ranedeer) (**MUST HAVE CHATGPT PLUS**) 2. Press the "Continue this conversation" button 3. Configure your preferences 4. Start learning! URL: [https://chat.openai.com/g/g-9PKhaweyb-mr-ranedeer](https://chat.openai.com/g/g-9PKhaweyb-mr-ran...
https://github.com/JushBJJ/Mr.-Ranedeer-AI-Tutor
2
[ "ai", "education", "gpt-4", "llm" ]
https://raw.githubusercontent.com/JushBJJ/Mr.-Ranedeer-AI-Tutor/main/README.md
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Guides - [How to Use Mr. Ranedeer](https://github.com/JushBJJ/Mr.-Ranedeer-AI-Tutor/blob/main/Guides/How%20to%20use%20Mr.%20Ranedeer.md) - [Configuration Guide](https://github.com/JushBJJ/Mr.-Ranedeer-AI-Tutor/blob/main/Guides/Config%20Guide.md)
https://github.com/JushBJJ/Mr.-Ranedeer-AI-Tutor
-1
[ "ai", "education", "gpt-4", "llm" ]
https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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2. Python for Machine Learning Python is a powerful and flexible programming language that's particularly good for machine learning, thanks to its readability, consistency, and robust ecosystem of data science libraries. - **Python Basics**: Python programming requires a good understanding of the basic syntax, data t...
https://github.com/mlabonne/llm-course
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https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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[ [ "🧑‍🔬", "llm", "scientist", "section", "course", "focus", "learning", "build", "best", "possible", "llm", "using", "latest", "technique", ".", "!", "[", "]", "(", "img/roadmap_scientist.png", ")" ], [ "🧑‍🔬 llm scientist...
🧑‍🔬 The LLM Scientist This section of the course focuses on learning how to build the best possible LLMs using the latest techniques. ![](img/roadmap_scientist.png)
https://github.com/mlabonne/llm-course
-1
[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]
https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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1. The LLM architecture While an in-depth knowledge about the Transformer architecture is not required, it is important to have a good understanding of its inputs (tokens) and outputs (logits). The vanilla attention mechanism is another crucial component to master, as improved versions of it are introduced later on. ...
https://github.com/mlabonne/llm-course
2
[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]
https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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2. Building an instruction dataset While it's easy to find raw data from Wikipedia and other websites, it's difficult to collect pairs of instructions and answers in the wild. Like in traditional machine learning, the quality of the dataset will directly influence the quality of the model, which is why it might be the...
https://github.com/mlabonne/llm-course
-1
[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]
https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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3. Pre-training models Pre-training is a very long and costly process, which is why this is not the focus of this course. It's good to have some level of understanding of what happens during pre-training, but hands-on experience is not required. * **Data pipeline**: Pre-training requires huge datasets (e.g., [Llama 2...
https://github.com/mlabonne/llm-course
-1
[ "course", "large-language-models", "llm", "machine-learning", "roadmap" ]
https://raw.githubusercontent.com/mlabonne/llm-course/main/README.md
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4. Supervised Fine-Tuning Pre-trained models are only trained on a next-token prediction task, which is why they're not helpful assistants. SFT allows you to tweak them to respond to instructions. Moreover, it allows you to fine-tune your model on any data (private, not seen by GPT-4, etc.) and use it without having t...
https://github.com/mlabonne/llm-course
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6. Evaluation Evaluating LLMs is an undervalued part of the pipeline, which is time-consuming and moderately reliable. Your downstream task should dictate what you want to evaluate, but always remember Goodhart's law: "When a measure becomes a target, it ceases to be a good measure." * **Traditional metrics**: Metric...
https://github.com/mlabonne/llm-course
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7. Quantization Quantization is the process of converting the weights (and activations) of a model using a lower precision. For example, weights stored using 16 bits can be converted into a 4-bit representation. This technique has become increasingly important to reduce the computational and memory costs associated wi...
https://github.com/mlabonne/llm-course
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👷 The LLM Engineer This section of the course focuses on learning how to build LLM-powered applications that can be used in production, with a focus on augmenting models and deploying them. ![](img/roadmap_engineer.png)
https://github.com/mlabonne/llm-course
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1. Running LLMs Running LLMs can be difficult due to high hardware requirements. Depending on your use case, you might want to simply consume a model through an API (like GPT-4) or run it locally. In any case, additional prompting and guidance techniques can improve and constrain the output for your applications. * *...
https://github.com/mlabonne/llm-course
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4. Advanced RAG Real-life applications can require complex pipelines, including SQL or graph databases, as well as automatically selecting relevant tools and APIs. These advanced techniques can improve a baseline solution and provide additional features. * **Query construction**: Structured data stored in traditional...
https://github.com/mlabonne/llm-course
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5. Inference optimization Text generation is a costly process that requires expensive hardware. In addition to quantization, various techniques have been proposed to maximize throughput and reduce inference costs. * **Flash Attention**: Optimization of the attention mechanism to transform its complexity from quadrati...
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7. Securing LLMs In addition to traditional security problems associated with software, LLMs have unique weaknesses due to the way they are trained and prompted. * **Prompt hacking**: Different techniques related to prompt engineering, including prompt injection (additional instruction to hijack the model's answer), ...
https://github.com/mlabonne/llm-course
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https://raw.githubusercontent.com/FlowiseAI/Flowise/main/README.md
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⚡Quick Start Download and Install [NodeJS](https://nodejs.org/en/download) >= 18.15.0 1. Install Flowise ```bash npm install -g flowise ``` 2. Start Flowise ```bash npx flowise start ``` With username & password ```bash npx flowise start --FLOWISE_USERNAME=user --FLOWISE_PASSWOR...
https://github.com/FlowiseAI/Flowise
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Prerequisite - Install [Yarn v1](https://classic.yarnpkg.com/en/docs/install) ```bash npm i -g yarn ```
https://github.com/FlowiseAI/Flowise
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Setup 1. Clone the repository ```bash git clone https://github.com/FlowiseAI/Flowise.git ``` 2. Go into repository folder ```bash cd Flowise ``` 3. Install all dependencies of all modules: ```bash yarn install ``` 4. Build all the code: ```bash yarn build ``` 5. ...
https://github.com/FlowiseAI/Flowise
2
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