Probando OLLAMA - Tu propio ChatGPT local!

Pelado Nerd
5 Dec 202314:39

TLDROLLAMA is a tool that allows you to run large language models locally on your computer using Docker or binary installations. It's easy to use, supports MacOS and Linux (with workarounds for Windows), and offers both pre-built open-source models and the ability to create custom models. The tool can interact with models via an API or a web interface, and it's resource-efficient, using CPU only during interactions. OLLAMA is a powerful option for those wanting to experiment with or develop applications using language models like Mistral and Llama 2.

Takeaways

  • 🚀 Ollama is a tool that allows you to run a local copy of a large language model (LLM) on your computer, similar to chatGPT.
  • 💻 Ollama can be run using a binary file, which is straightforward and currently available for MacOS and Linux. Windows users can use WSL or a virtual machine.
  • 🌐 The Ollama binary can be downloaded from ollama.ai, and it facilitates interaction with LLMs through an API.
  • 📚 Ollama offers open source and free models that users can download and use, with popular ones being Mistral and Llama 2.
  • 💾 Running heavy models like Llama 2 requires significant system resources, ideally around 8 GB of RAM and ample disk space.
  • 🔍 You can interact with Ollama by using the command `ollama run` followed by the model name. If the model isn't downloaded, it will be pulled automatically.
  • 🤖 Ollama saves context, allowing for continuous and context-aware conversations with the LLM.
  • 🛠️ Users can create custom models by modifying a 'modelfile', which is analogous to a Dockerfile, and then running it with specific parameters.
  • 🎭 An interesting feature is the ability to set a 'system' personality for the model, like making it respond as a character from a game.
  • 📈 Ollama uses layers, similar to Docker, to create new models from existing ones, which is efficient and fast even with large files.
  • 🌐 Ollama can also be run in Docker and can expose an API for web interfaces to interact with the LLM.

Q & A

  • What is Ollama and how does it relate to chatGPT?

    -Ollama is a tool that allows users to run their own copy of a large language model, similar to chatGPT, locally on their computer. It provides an interface to interact with these models, and it can be run using a binary or through Docker.

  • Which operating systems is Ollama's binary version currently compatible with?

    -As of the time the video was filmed, the Ollama binary is compatible with MacOS and Linux. Windows users can run the Linux version using WSL (Windows Subsystem for Linux) or a virtual machine.

  • How can one obtain and run the Ollama binary?

    -To obtain the Ollama binary, one can visit ollama.ai and download it. After installation, it can be run from the terminal by simply typing 'ollama'.

  • What are the system requirements for running heavier models like Mistral and Llama 2?

    -To run heavier models like Mistral and Llama 2, it is recommended to have a machine with at least 8 GB of RAM and disk space between 4 and 16 GB.

  • How does Ollama handle model downloading if a user attempts to run a model that is not yet downloaded?

    -If a user tries to run a model that is not downloaded, Ollama will automatically pull the model, similar to how Docker works when an image is not available.

  • How does Ollama utilize CPU during interaction with the language model?

    -Ollama uses the CPU only during the interaction when it is generating a response. The CPU usage spikes to 100% of the cores during this time and drops back down when the interaction is complete.

  • What is a 'modelfile' in the context of Ollama?

    -A 'modelfile' is a configuration file used in Ollama to define the settings for a new model. It is similar to a Dockerfile and allows users to customize the behavior of the model, such as its creativity level or the persona it adopts.

  • How can users create a new model based on an existing one in Ollama?

    -Users can create a new model by defining a modelfile with the desired parameters and settings, and then running the command 'ollama create -f ' to create and save the new model locally.

  • What is the purpose of the 'temperature' parameter in the modelfile?

    -The 'temperature' parameter determines the creativity level of the model. A higher temperature results in more creative and varied responses, while a lower temperature makes the model's responses more direct and predictable.

  • How does Ollama save the context of a conversation?

    -Ollama saves the context of a conversation so that it can remember the history of previous interactions and respond accordingly, providing a more natural and连贯 (continuous) dialogue.

  • What is the process for running Ollama in Docker and providing a web interface for the API?

    -To run Ollama in Docker with a web interface, one would use a docker-compose file to start both the Ollama service and a web interface service, such as chatbot-ollama. This allows for persistent model storage and interaction with the Ollama API through a web-based chat interface.

  • How can users interact with the Ollama API?

    -Users can interact with the Ollama API through command-line tools like curl or through a web interface. The API allows users to specify which model to use for a given query and can be used to build applications that communicate with the language models.

Outlines

00:00

😀 Running ChatGPT Locally with Ollama

The video begins with the host introducing the process of running a local copy of ChatGPT using a tool called Ollama. The host explains that Ollama is easy to use and can be run via a binary file, which is currently available for MacOS and Linux, with Windows support coming soon. The audience is guided to the Ollama website to download the binary and shown how to run it in the terminal. The host also discusses the capability of Ollama to run large language models (LLM) and interact with them via an API. The video then explores downloading and running models from Ollama's website, as well as creating custom models with specific characteristics, such as the amount of creativity desired in the model's responses.

05:02

📚 Creating a Custom Model with Ollama

The host demonstrates how to create a new model using a 'modelfile', which is similar to a Dockerfile. The process involves starting from a base model, in this case, Llama 2, and customizing it with parameters such as temperature to control the creativity level of the model. The host illustrates how to instruct the model to behave in a specific manner, using the example of making the model respond as the character Mario from Super Mario Bros. The video then shows the creation of a new model named 'Mario' from the customized modelfile and running it to interact with the model as if it were Mario. The host also touches on the efficiency of Ollama in processing large files and the ability to save and reuse models.

10:03

💻 Docker and Web Interface for Ollama

The host moves on to explain how to run Ollama in Docker and provide a web interface for interacting with the Ollama API. The process involves using a docker-compose file to set up two services: Ollama and a chatGPT-like interface. The host emphasizes the ease of using Docker to maintain model files persistently on the machine and to set environment variables for the model path and API host. The video then shows how to interact with the Ollama API using curl commands and how to download and use a web interface for a more user-friendly interaction with the models. The host concludes by encouraging viewers to experiment with creating their own models and applications using the local chatGPT interface and Ollama API.

Mindmap

Keywords

💡Ollama

Ollama is a tool that allows users to run large language models (LLM) locally on their computers. It is mentioned as being very easy to use and is capable of running models via a binary file or through Docker. In the video, Ollama is used to demonstrate how to interact with language models and create customized models with specific behaviors, such as one that responds as the character Mario from Super Mario Bros.

💡Docker

Docker is a platform that enables users to develop, ship, and run applications in containers. In the context of the video, Docker is used to run Ollama and to provide a web interface for interacting with the language models. The script mentions Docker in relation to the ease of pulling and running models, similar to how one would use Docker to run other applications.

💡Binary

A binary in the context of the video refers to an executable file that can be directly run on a computer system. The speaker discusses downloading the Ollama binary from its website, which allows for the running of language models without the need for additional setup or compilation.

💡Large Language Models (LLM)

Large Language Models, or LLM, are advanced AI models designed to process and generate human-like language. The video focuses on using Ollama to run and interact with these models, which can be used for various applications such as creating chatbots or generating text.

💡API

An API, or Application Programming Interface, is a set of rules and protocols that allows different software applications to communicate with each other. In the video, the speaker mentions using an API to interact with the language models run by Ollama, allowing for the creation of custom interfaces and applications.

💡Models

In the context of the video, models refer to the language models that can be downloaded and used with Ollama. These models are open source and free to use, with examples given such as Mistral and Llama 2. The speaker also discusses creating custom models with specific parameters and behaviors.

💡RAM

RAM, or Random Access Memory, is the hardware in a computer system that allows for the storage and retrieval of data. The video emphasizes the importance of having sufficient RAM when running large language models, as they can be quite resource-intensive.

💡Dockerfile

A Dockerfile is a text document that contains all the commands a user could call on the command line to assemble an image. In the video, it is mentioned that creating a custom model with Ollama involves using a 'modelfile', which is similar to a Dockerfile and is used to define the settings and parameters for a new model.

💡CPU

CPU, or Central Processing Unit, is the primary component of a computer that performs most of the processing. The video discusses how Ollama utilizes the CPU when interacting with the language models, noting that it uses all available cores during active processing.

💡WSL

WSL, or Windows Subsystem for Linux, is a compatibility layer for running Linux binary executables natively on Windows. The video mentions WSL as a way for Windows users to run the Linux version of the Ollama binary if it is not yet available for their operating system.

💡Docker Compose

Docker Compose is a tool for defining and running multi-container Docker applications. The video script describes using Docker Compose to set up a web interface for interacting with Ollama and the language models, simplifying the process of running these applications in Docker.

Highlights

Ollama is a tool that allows you to run a local copy of chatGPT on your computer.

It was introduced at DockerCON and can be run using Docker or a binary.

As of the video recording, the binary is available for MacOS and Linux, with Windows support coming soon.

Windows users can run the Linux version using WSL or a virtual machine.

Ollama can run large language models (LLM) and interact with them via an API.

Users can download open source models from Ollama's website for free.

Creating your own model requires approximately 8 GB of RAM due to the models' size.

The 'ollama run' command is used to start Ollama and interact with the chosen model.

If a model is not downloaded, Ollama automatically pulls it, similar to Docker's behavior.

Ollama uses CPU resources efficiently, only consuming them during interaction.

The tool saves context, allowing for continued conversations based on previous interactions.

Users can create custom models by modifying a 'modelfile', similar to Dockerfiles.

Custom models can be created with specific behaviors, such as answering as a character like Mario from Super Mario Bros.

Ollama can be run in Docker, providing a persistent storage solution for downloaded models.

A web interface is available for interacting with the Ollama API, offering a chatGPT-like experience.

The web interface allows users to choose the model and adjust settings like creativity and accuracy.

Ollama can be used to create applications that interact with the API and leverage custom models.

The video demonstrates the ease of setting up a local chatGPT-like interface using Docker and Ollama.

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