Run Llama 3 on CPU using Ollama
TLDRIn this AI Anytime video, the host demonstrates how to use the Ollama tool to run the Llama 3 model on a CPU machine. Llama 3, a recent release by Meta AI, is an open-source language model that has shown excellent performance on evaluation benchmarks. The video guides viewers through the process of downloading and installing Ollama for various operating systems, including Windows, Mac, and Linux. It then shows how to run Llama 3 locally by using the command 'o Lama run Llama 3' in the terminal. The host also discusses the ease of integrating Llama 3 with other tools like Langchain and provides examples of querying the model with different prompts. The video concludes with a reminder to download Ollama for testing language models locally without the need for high compute resources and teases an upcoming video on building a chat application with Langchain.
Takeaways
- 🚀 Llama 3 is the latest open-source language model from Meta AI, performing well on evaluation benchmarks.
- 💡 Ollama is a no-code/low-code tool that allows users to load and run language models locally on their machines.
- 📥 To use Llama 3 on a CPU, you can download the Ollama tool for Windows, Mac, or Linux and install it.
- 🔗 Ollama supports running models from Hugging Face and provides an easy way to integrate with other tools like Lang Chain.
- 💻 Even with limited compute resources like 16 GB or 8 GB of RAM, you can still run Llama 3 on your local machine.
- 📈 The tool can automatically download and quantize the Llama 3 model if it's not already available on the system.
- ⚡ Llama 3 is capable of generating responses at a fast pace, with a good number of tokens per second.
- 🔌 Ollama provides a local URL (e.g., localhost:11434) that can be used for integrating the model into other applications.
- 🔧 Ollama is user-friendly and allows for easy inference of language models without the need for high computational resources.
- ❌ The video demonstrates that certain questions, like creating sulfuric acid, are not answered by Llama 3, adhering to responsible AI practices.
- 📈 The script highlights the ease of testing new language models with Ollama, without the need for deploying on cloud providers or high-end hardware.
Q & A
What is the latest release by Meta AI that the video discusses?
-The latest release by Meta AI discussed in the video is LLaMa 3, which is an open-source language model that has performed well on evaluation benchmarks.
What is the purpose of using Ollama to run LLaMa 3 on a CPU?
-Ollama is used to run LLaMa 3 on a CPU to allow users with limited compute resources, such as a machine with 16 GB or 8 GB of RAM, to locally inference and experiment with the LLaMa 3 model without needing high computational power.
How does Ollama facilitate the use of language models locally?
-Ollama is a no-code/low-code tool that enables users to load language models locally, perform inference, and even build a language application without the need for extensive coding.
What are the different operating systems supported by Ollama?
-Ollama supports different operating systems including Windows, Mac OS, and Linux, providing options for users regardless of their preferred OS.
How can one start using Ollama after installation?
-After installing Ollama, users can open their terminal and use the command 'ollama run' followed by the model name, such as 'ollama run LLaMa 3', to start using the language model.
What happens when you run the command 'ollama run LLaMa 3' for the first time?
-If run for the first time, Ollama will download the LLaMa 3 model from Hugging Face, quantize it, and then prompt the user to input a query for the model to generate a response.
How does Ollama handle the process of running language models?
-Ollama automates the process of running language models by handling the download, quantization, and execution, allowing users to focus on interacting with the model rather than managing technical details.
What is the significance of the localhost URL provided by Ollama?
-The localhost URL provided by Ollama is significant as it allows users to access the running model on a specific port, which can be useful for integrating the model into other tools or applications.
How does Ollama integrate with LangChain?
-Ollama integrates with LangChain by allowing users to invoke the model through LangChain modules such as 'chatama'. Users can pass the model name and their message to interact with the language model.
What are the limitations of running large models like LLaMa-22B on a CPU with limited RAM?
-Running large models like LLaMa-22B on a CPU with limited RAM (such as 16 GB or 8 GB) may not be feasible due to the high computational requirements. For such models, a machine with at least 128 GB of RAM is recommended for optimal performance.
How does the video demonstrate the ease of using Ollama for language model inference?
-The video demonstrates the ease of using Ollama by showing the simple process of installing the tool, running a language model like LLaMa 3, and interacting with it to generate responses to various queries.
What is the advice given for users who want to test new language models without incurring high computational costs?
-The advice given is to use Ollama to test new language models locally on their own machines, which can help users avoid unnecessary expenses associated with using cloud providers or high-performance computing resources for testing purposes.
Outlines
🚀 Introduction to Using LLaMa 3 with Olama
The video introduces viewers to LLaMa 3, an open-source language model by Meta AI that excels in evaluation benchmarks. The host shares their curiosity about running LLaMa 3 on a CPU machine with limited compute resources, such as 16 GB or 8 GB of RAM. They explain that Olama is a tool that allows users to load and run language models locally for inference, making it ideal for those with limited resources. The host guides viewers through the process of downloading and installing Olama for different operating systems, including Windows, Mac OS, and Linux. They also demonstrate how to run LLaMa 3 using Olama and how to interact with the model by providing a prompt and receiving a response. The video highlights the ease of using Olama for local model inference and the potential for integrating it with other tools like Lang Chain.
🤖 Testing LLaMa 3's Capabilities with Olama
The host proceeds to test LLaMa 3's capabilities using Olama by asking various questions. They first ask a simple arithmetic question, 'What is 2+2?', and receive a correct response in markdown format. Following this, they pose a more complex question, asking for five words starting with the letter 'e' and ending with 'n'. The model fails to provide the correct response, indicating a limitation in understanding the query. The host then asks how to create sulfuric acid, a question they acknowledge could be problematic. To their surprise, the model provides a response, which the host considers irresponsible and potentially dangerous. They express dissatisfaction with the model's responses to the last two questions and caution viewers about the model's susceptibility to 'jailbreaking'. The host concludes by encouraging viewers to download Olama for testing language models without incurring high cloud computing costs and to share their experiences and feedback in the comments. They also tease an upcoming video about integrating Olama with Lang Chain and thank the viewers for watching.
Mindmap
Keywords
💡Llama 3
💡Ollama
💡Inference
💡CPU
💡RAM
💡Hugging Face
💡Quantization
💡Lang Chain
💡Model Variants
💡Prompt
💡Streaming Response
Highlights
The video demonstrates how to use Ollama to run Llama 3 on a CPU.
Llama 3 is the newest release by Meta AI, a highly performing open-source language model.
Ollama is a no-code/low-code tool for local loading and inference of language models.
The video provides a step-by-step guide to installing Ollama on different operating systems.
Ollama supports running models locally, even on machines with limited compute resources like 16 GB or 8 GB of RAM.
The process of installing Ollama on Windows involves downloading an executable file and running it.
Previously, Ollama required Windows Subsystem for Linux (WSL), but now it has direct support for Windows.
Running Llama 3 through Ollama involves using the command 'ollama run Llama 3' in the terminal.
If Llama 3 is not already downloaded, Ollama will fetch and quantize the model automatically.
Ollama can generate responses to prompts with high speed, even on a machine with 16 GB of RAM.
The video shows Ollama running on a local host port, which can be useful for integration with other tools.
Meta has released two variants of Llama 3, the 8B model and the 7B model.
For high compute models like Mixtrax 22B or GPT-3, a machine with 128 GB of RAM is recommended for optimal performance.
Ollama integrates easily with LangChain, allowing for invocation of language models through commands.
The video demonstrates the ease of inferring language models using Ollama, even for first-time users.
The presenter asks a mathematical question and receives a correct response, showcasing the model's capabilities.
When asked a challenging question about creating five words starting with 'e' and ending with 'n', the model fails to provide correct responses.
The model refuses to answer a question about creating sulfuric acid, adhering to responsible AI practices.
The presenter encourages viewers to share their experiences with Llama 3 and other language models in the comments.
The video concludes with a teaser for an upcoming video on rag applications with Ollama, LangChain, and other tools.