How Can I Run Terminal in Google Colab?

Google Colab is a cloud-based Jupyter Pocket book surroundings that lets you write and execute Python code effectively. It runs on cloud-based digital machines, which means customers don’t have to configure native environments. This makes it a wonderful selection for knowledge science, machine studying, and common Python scripting. Nonetheless, typically you might have to execute shell instructions instantly, reminiscent of putting in packages, managing recordsdata, or working system-level utilities. Whereas Colab gives a method to execute shell instructions inside notebooks, it additionally permits entry to a full terminal surroundings. On this information, we’ll present you methods to entry the terminal in Google Colab, set up and use Ollama to tug machine studying fashions, after which run inference with LangChain and Ollama.

Step 1: Set up and Load colab-xterm

To entry the terminal in Google Colab, it’s worthwhile to set up and allow the colab-xterm extension. Run the next instructions in a Colab cell:

!pip set up colab-xterm
%load_ext colabxterm

As soon as put in and loaded, you may launch the terminal by working:

%xterm

It will open a terminal interface instantly inside your Colab surroundings.

Set up the Ollama within the terminal utilizing Linux command.

curl -fsSL https://ollama.com/set up.sh | sh

Step 2: Pulling a Mannequin Utilizing Ollama

After you have entry to the terminal, you may obtain and use machine studying fashions. For instance, to tug the deepseek-r1:7b or llama3  mannequin utilizing Ollama, run the next command within the terminal:

!ollama pull deepseek-r1:7b

or 

!ollama pull llama3

It will obtain the mannequin and put together it for utilization in your Colab pocket book.

Step 3: Putting in Required Libraries

After downloading the mannequin, set up the mandatory Python libraries to work together with the mannequin. Run these instructions in a brand new Colab cell:

!pip set up langchain
!pip set up langchain-core
!pip set up langchain-community

These libraries are important for working with giant language fashions in a structured approach.

Step 4: Working Inference with LangChain and Ollama

As soon as all dependencies are put in, you should utilize LangChain to work together with the mannequin. Add the next code in a Colab cell:

from langchain_community.llms import Ollama

# Load the mannequin

llm = Ollama(mannequin="llama3")

# Make a request to the mannequin

from langchain_community.llms import Ollama

llm = Ollama(mannequin = "llama3")

llm.invoke("inform me about Analytics Vidhya")

Analytics Vidhya!nnAnalytics Vidhya is a well-liked on-line group and
platform that focuses on knowledge science, machine studying, and analytics
competitions. The platform was based in 2012 by three knowledge fans:
Vivek Kumar, Ashish Thottumkal, and Pratik Jain.nnHere's what makes
Analytics Vidhya particular:nn1. **Competitions**: The platform hosts common
competitions (known as "challenges") which are open to anybody serious about
knowledge science, machine studying, or analytics. Individuals can select from a
number of challenges throughout varied domains, reminiscent of finance, advertising,
healthcare, and extra.n2. **Actual-world datasets**: Challenges typically function
real-world datasets from well-known organizations or corporations, which
contributors should analyze and clear up utilizing their abilities in knowledge science and 
machine studying.n3. **Judging standards**: Every problem has a set of
judging standards, which ensures that submissions are evaluated based mostly on
particular metrics (e.g., accuracy, precision, r

It will load the llama3 mannequin and generate a response for the given immediate.

Additionally Learn: How you can Run OpenAI’s o3-mini on Google Colab?

Conclusion

By following these steps, you may simply entry a terminal in Google Colab, enabling you to put in dependencies, obtain machine studying fashions utilizing Ollama, and work together with them by way of LangChain. This transforms Colab into a flexible AI growth surroundings, permitting you to experiment with cutting-edge fashions, automate workflows, and streamline your machine studying analysis—all inside a cloud-based pocket book.

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Hello, I’m Pankaj Singh Negi – Senior Content material Editor | Enthusiastic about storytelling and crafting compelling narratives that remodel concepts into impactful content material. I really like studying about know-how revolutionizing our way of life.