Jumat, 31 Mei 2024

Install Multiple Python Versions on Ubuntu With Pyenv

Install Multiple Python Versions on Ubuntu With Pyenv

If you are exploring Python-based projects and packages, you may end up in a situation where you need a different Python version. I faced this situation while trying to install PyCoral library that works only with Python versions 3.6 to 3.9 and my system had Python 3.11.

The good thing is that you do not need to install the existing Python version provided by your distribution and install the new one.

You can have multiple Python versions installed thanks to a tool called the . In this tutorial, I'll show you how to install and use this tool to get more than one Python version simultaneously on your Ubuntu and Debian-based distributions.

Install pyenv on Ubuntu

Since pyenv builds Python from the source, you’ll need to install build dependencies to use pyenv.

On Ubuntu and Debian based distros, you can use this command to get all the necessary packages.

sudo apt install -y make build-essential libssl-dev zlib1g-dev libbz2-dev libreadline-dev libsqlite3-dev wget curl llvm libncurses5-dev
libncursesw5-dev xz-utils tk-dev libffi-dev liblzma-dev python3-openssl

Some of the above-mentioned packages might already have been installed.

Once you have the dependencies, you can install pyenv using the official script:

curl https://pyenv.run | bash

It will download the script and run it.

When the script ends, it should give you some instructions to follow for loading pyenv automatically. This output is dependent on the shell you use. For me on bash, it showed:

# Load pyenv automatically by appending
# the following to 
# ~/.bash_profile if it exists, otherwise ~/.profile (for login shells)
# and ~/.bashrc (for interactive shells) :

export PYENV_ROOT="$HOME/.pyenv"
[[ -d $PYENV_ROOT/bin ]] && export PATH="$PYENV_ROOT/bin:$PATH"
eval "$(pyenv init -)"

So, I edited the ~/.bashrc file and put the mentioned code at the end of it.

Once done, either reload the shell with the command below or just restart your terminal:

exec "$SHELL"

Let's verify that pyenv is installed on your system by checking the version of pyenv:

pyenv --version

If you see a number and not an error, you have pyenv setup on your Ubuntu Linux. It's time to see how to use it.

Install different Python versions with pyenv

You can list various Python installations and virtual environments offered by pyenv with this command:

pyenv --list

However, it will show a huge output with all the offerings that might not be of interest to you.

If you are interested in various Python3 versions, use the magic of regex and grep. Let's say you are interested in Python versions from 3.6 to 3.9. For that, use:

pyenv install --list | grep " 3\.[6789]"

It will still be a big list but not as huge as the one you saw before.

I am doing it to try the Coral projects using the PyCoral libraries and it only works with Python 3.6 to Python 3.9. If I look at the status of Python versions, 3.9 seems like a good version to go with. I'll go with the 3.9.19:

pyenv install -v 3.9.19

Since Python is being built from source, it may take some time and the output will be huge. Just ensure that it doesn't result in error.

Python version location

Different versions of Python you install with pyenv are located in ~/.pyenv/versions/ directory.

abhishek@raspberrypi:~ $ ls ~/.pyenv/versions/
3.9.19

Listing installed Python versions

You can see which additional Python versions with:

pyenv versions

This should show the system version (Python version installed by your distro) and the additional versions installed by pyenv:

* system (set by /home/abhishek/.pyenv/version)
  3.9.19
💡
Asterisk * in the above command output suggests the currently active Python version.

Using additional Python versions

If you just want to run something with a specific Python version, you can use it like this:

pyenv exec python3.9.19 test.py

Alternatively, you can change the default Python version to your choice:

pyenv global 3.9.19

You can confirm the active Python version has changed by looking at the entry with * in the versions list:

abhishek@itsfoss:~ $ pyenv global 3.9.19
abhishek@itsfoss:~ $ pyenv versions     
  system
* 3.9.19 (set by /home/abhishek/.pyenv/version)
abhishek@raspberrypi:~ $ 

Later on, you can switch back to the system version:

pyenv global system

Removing addition Python versions

That's quite simple as well. You can either remove the its folder:

rm -rf ~/.pyenv/versions/<python_version_to_delete>

Or, use the pyenv command in this fashion:

pyenv uninstall <python_version>

Prefer using virtual environments

Even if you install multiple Python versions, it would be a good idea to In this tutorial, I'll show you how to install and use this tool to get more than one Python version simultaneously on your Ubuntu and Debian-based distributions.. This way, you won't be messing up with packages at system level.

Let's say I want to use Python 3.9.19 for a project named coral. I'll create virtual environment for this project (name doesn't need to match):

pyenv virtualenv 3.9.19 coral

And now, I switch to this virtual environment:

pyenv local coral

You can verify that a virtual environment is in use:

abhishek@itsfoss:~ $ pyenv which python
/home/abhishek/.pyenv/versions/coral/bin/python

Remember that you can still not use 'apt install python-package-name', hoping that it gets installed with the different Python version in the virtual environment. You'll have to use pip for this purpose:

pip install python-package-name

This will install the mentioned Python package in the specific virtual environment with your specified Python version.

💬 I hope you like this tutorial on managing different Python versions on Ubuntu. Although I am not a Python expert, but I'll try to help you if you have any questions.



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Rabu, 29 Mei 2024

FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

This week, It's FOSS crossed 120,000 followers mark on Twitter (or is it X?). That's an achievement. If you use Twitter, do follow us for timely updates, tips and memes.

💬 Let's see what else you get in this edition:

  • Ubuntu focusing on RISC-V.
  • A new Ultramarine Linux release.
  • Google's search algorithm secret sauce being spilled.
  • And other Linux news, videos and, of course, memes!
  • DigitalOcean supports this edition of FOSS Weekly.

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📰 Linux news

Mozilla has laid out an interesting course of action regarding the need for more openness in AI.

Mozilla Lists Open AI Benefits As We Approach The Peak of the Hype
Mozilla’s research should come in handy for everyone.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

🧠 What we’re thinking about

Oops, Google's secret sauce is out in the open. 🙃

Secrets from the Algorithm: Google Search’s Internal Engineering Documentation Has Leaked
Learn what you always wish you knew about Google’s algorithms.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

🧮 Linux Tips, Tutorials and More

These are some of the best open-source LLMs for most use cases:

14 Top Open Source LLMs For Research and Commercial Use
There are hundreds of open-source LLMs, here, we handpick some of the best ones for you to check out.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

Ever wondered how to check logged-in users in Linux?

Show Logged in Users on Linux
Got a multiuser Linux system and wondering who is logged in it? Here are various ways to find that out.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

Were you aware of these commands? These can be very useful:

6 Linux Commands I Use for Malware Analysis
Vlad Ananin of ANY.RUN shares his favorite Linux commands that help him in analyzing malware.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

🛍️ Linux eBook bundle

From the prestigious No Starch Press, a DRM-free eBook bundle covering various aspects of Linux and DevOps. I highly recommend getting it.

Humble Tech Book Bundle: Networking, Sysadmin, and DevOps by No Starch
IT pros, this book bundle will get you up to speed with DevOps, teach you Linux tips and tricks, and demystify containerization. Your purchase helps charity!
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

📹 Video tutorial


✨ Project highlights

Interested in an open-source app that can help you keep other Android apps in check?

A Powerful Open-Source Android App to Help You Block App’s Internet Access
Take control of internet access on your Android device with this app.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

Or, how about a minimal audio player?

Decibels is a Minimal Audio Player, And That’s About it!
A focused audio player to play what you need for the moment.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

🧩 New quiz

Brush up your Linux command skills with this quiz!

Linux Commands to Save Time
Do you think you know some of the most useful commands to supercharge your terminal experience? Take this quiz to find out.
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

💡 Quick handy tip

It is possible to open man pages using Firefox. It opens the man page inside the GNOME Help app.

For this, first enter the following in the address bar of Firefox:

man:<command>

There should not be any space. This will ask to open the Link with Help app. Click on open.

FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

Now, the man page will be opened inside the Help viewer. You can scroll to read. Interlink also works.

FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

It works in other browsers too, if you have "Help" installed.


🤣 Meme of the week

Oh, it does get confusing. But it would help if you refer to our list of the best Linux distributions.

FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

🗓️ Tech Trivia

Windows 2.1 was released on May 27, 1988. Interestingly, as an operating system, it never really caught on until Windows 3 came about.


🧑‍🤝‍🧑 FOSSverse corner

FOSSers are discussing whether autocorrect is racist.

Is autocorrect discriminatory?
There are people complaining that autocorrect is racist because it tends to modify the names of people of non-english background That seems a bit far fetched to me, but I imagine they could stop using the algorithm on names . Is this real , or is it a case of looking for something to complain about? If it is real, can linux get in first with a fix?
FOSS Weekly #24.22: Ultramarine Linux, Open Source LLMs, Decibels Music Player and More

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Share it with your Linux-using friends and encourage them to subscribe (hint: it's here).

Share the articles in Linux Subreddits and community forums.

Follow us on Google News and stay updated in your News feed.

Opt for It's FOSS Plus membership and support us 🙏

Enjoy using Linux 😄



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Selasa, 28 Mei 2024

I Installed AI Apps in a Single Click on My Linux System With Pinokio

I Installed AI Apps in a Single Click on My Linux System With Pinokio

AI is everywhere. But, not all kinds of AI tools are accessible to the end-users.

Sure, you can try something like ChatGPT effortlessly or a couple of other AI tools through your web browser. But, what about running an AI app locally on your computer while protecting your privacy?

You do not have to pay anyone and still be able to safely use an AI app using your computer to generate text, generate images, videos, separate audio, and lots more.

Pinokio is an open-source tool that lets you do that with a single click. Here, let me tell you more about it and how to use it.

Pinokio: Making AI Apps Easy to Run

I Installed AI Apps in a Single Click on My Linux System With Pinokio
AI-generated image using Pinokio by It's FOSS

Normally, to deploy an AI app locally, you need to go through a decent amount of mumbo jumbo instructions, and with that method, an end-result is not guaranteed either.

Pinokio comes to the rescue, describing itself as an "AI Browser". It is an app that utilizes 1-click scripts to install and configure various AI-powered apps or chatbots on your computer.

It stores them all in a single folder, and lets you customize some of it after you install it (if needed).

📋
Pinokio needs a good amount of system resources for all the AI apps to run smoothly, and enough storage (30-40 GB of free storage at least) to install all kinds of dependencies needed to run what you need.

If you have a decent system with at least four–six cores, and a mid-range GPU with 6-12 GB of VRAM. You should be able to run some things smoothly.

I have an NVIDIA RTX 3060 Ti graphics card with 8 GB of VRAM. And, I did face some performance hiccups for tasks like text to video.

Don't be discouraged by not trying it — but if you think your system gets stuck and is unresponsive, you might have to try other tools that aren't as resource intensive. Some AI apps are more resource-intensive. Honestly, that's the only downside of trying to run an AI app locally.

Features of Pinokio AI Browser

To give you an overview of what Pinokio is capable, let me highlight the key points:

  • It runs locally, without sharing any data to the cloud, making it a privacy-friendly option.
  • It is a free and open-source application.
  • You get cross-platform support
  • Easy to use

How to Install an AI App Using Pinokio?

I Installed AI Apps in a Single Click on My Linux System With Pinokio

First, you need to install Pinokio AI browser on your Linux system. They offer multiple packages on the GitHub releases section for you to get it installed including AppImage, .deb, and .rpm.

I tried it on Ubuntu 24.04 LTS, and I installed the deb package. I tried the AppImage file as well, but the 1-click installation process did not work as expected.

So, I encourage you to try the deb/rpm package instead as per your distribution.

For help, you can refer to our tutorials:

Once you have installed, just search for Pinokio and launch the browser. It first asks you to set the path to store things, and the theme for your user experience:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

Of course, I went with the dark theme (sorry to the dark mode fans looking at the bright white screenshot 😉)

Once you hit “Save”. It takes you to the welcome page with a button to visit the Discover page.

I Installed AI Apps in a Single Click on My Linux System With Pinokio

You can explore a wide range of options when you click on to visit the Discover page.

I Installed AI Apps in a Single Click on My Linux System With Pinokio

It does not show the scripts available in any specific order (popular to lesser-known) or best to worst. So, all you get is a grid list to explore options.

All you have to do is select the one you want to try, and then hit the download button as shown in the image below:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

For the first time, it will install several dependencies needed for all kinds of projects:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

Once done, it will prompt you to download the script for the AI tool you selected:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

When you hit download here, the script will start and the installation/configuration required will start making progress. Some AI apps might take a while, and others could be quick if the packages required already exist.

Some AI apps need a lot of download (upwards of 6 GB). So, make sure, you have a good internet connection and storage space to accommodate that. Here's how it looks when it starts downloading/installing:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

When the installation is complete, it will show up a "Start" option in the sidebar and then when you start it, you can click on the "Web UI" option to access the app that you want to use.

I Installed AI Apps in a Single Click on My Linux System With Pinokio
🚧
I tried about 10 different models, and ended up downloading plenty of files. However, only a handful of them worked. So, it might need some troubleshooting with some apps, and some front-end apps might need you to set up the backend manually before you can use it.

The Pinokio AI browser hasn't seen any development activity for 9–10 months. Maybe, with articles like this, more contributors can help revive the project and fix existing issues.

Some of the AI applications that successfully work include:

  • Foocus — an image generation app
  • FaceFusion — face swapping/enhancement
  • AudioSep — separate anything from an audio clip that you describe

I also stumbled upon interesting chatbots like ChatGPT, but it didn't work. Maybe you can try exploring the open-source ChatGPT alternatives for this job:

13 Best Open Source ChatGPT Alternatives
Looking for open-source ChatGPT alternatives? We curated some of the best ones for you to take a look at.
I Installed AI Apps in a Single Click on My Linux System With Pinokio

And, among the ones I was able to run, the Foocus image generator immediately became my favorite, and I plan to use it for a while, at least, for my social media.

I Installed AI Apps in a Single Click on My Linux System With Pinokio

What do you think about it? I am sure this will be most of us! Cats, catnips, anime, and more stuff! Looks mind-blowing right? 🤯

The image generator gives you the ability to generate photorealistic images, anime style, and more. You can adjust the quality, add constraints, specify the number of images, aspect ratio of the final image, and more.

There was also a model that lets us generate comic pages using text prompts. I did not give it a try. You might want to explore that as well.

Curious about video generators? That's also there. But, I got "out of memory" error when I ran the VideoCrafter app. Maybe it needs more than 8 GB VRAM.

This is how my storage folder looks like, with one app installed:

I Installed AI Apps in a Single Click on My Linux System With Pinokio

So, yeah, it may not be the most convenient solution in every way. But, if you want to run AI apps locally without needing to know countless technical details, Pinokio browser seems like a fantastic option for the apps that work in it.

You can also refer to the documentation for greater details.

The AI Year is Exciting and Worrying

At the end of the day, I must mention that I'm excited to use all the AI tools at my disposal powered by generative models (or some open-source LLMs). And, if we can do all this now, what would we be able to do in the near future?

💬 Just leaving you with food for thought, let me know what you think in the comments down below!



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Kamis, 23 Mei 2024

14 Top Outstanding Open Source LLMs For Research and Commercial Use

14 Top Outstanding Open Source LLMs For Research and Commercial Use

Large Language Models (or LLMs) are machine learning models (built on top of transformer neural network) that aim to solve language problems like auto-completion, text classification, text generation, and more. It is a subset of Deep Learning.

The "large" refers to the model being pre-trained by a massive set of data (could be in sizes of petabytes), accompanied by millions or billions of parameters and further fine-tuned for a specific use-case.

While LLMs made projects like ChatGPT and Google Gemini possible, they are proprietary. So, there's a lack of transparency, and the entire control of the LLM (and its usage) is at the discretion of the company that owns it.

So, what's the solution to this? Open source LLMs.

With an open-source LLM, you can have multiple benefits like transparency, no vendor lock-in, free to use for commercial purposes, and total control over customization of the model (in terms of performance, carbon footprint, and more).

Similar reasons why we need open-source ChatGPT chatbot alternatives:

13 Best Open Source ChatGPT Alternatives
Looking for open-source ChatGPT alternatives? We curated some of the best ones for you to take a look at.
14 Top Outstanding Open Source LLMs For Research and Commercial Use
📋
And as promoters of open-source, we're not alone. IBM, Meta, Intel, AMD, CERN, Hugging Face, and more companies agree with the open innovation for AI idea and have come up with an AI Alliance in 2023.

Now that you know why we need open-source LLMs, let me highlight some of the best ones available.

1. Falcon 180B

The Technology Innovation Institute (TII) in the United Arab Emirates (UAE) launched an open LLM, which performs close to its proprietary competitors.

The model includes 180 billion parameters and was trained on 3.5 trillion tokens.

In the Falcon AI model family, there are smaller/newer models like Falcon 2 11B, which is a scalable solution, and outperforms similar models from Meta and Google.

💡
Parameters: 180 billion
License: Falcon-180B TII License (based on Apache 2.0)
Usage: Allowed for commercial use and should be fine-tuned for specific tasks

2. Dolly 2.0

Dolly 2.0 is one of the most prominent open-source LLM models developed by Databricks, fine-tuned on a human-generated instruction dataset. Unlike its predecessor, the dataset is its original, generated by more than 5000 employees of the company.

It includes 12 billion parameters, and is geared towards summarization, QA, classification, and some brainstorming. Dolly 2.0 does not aim to compete with existing models, but the focus was more on providing a human-generated dataset along with an LLM. They claimed it to be the first of its kind back then that tried to mimic ChatGPT's interaction.

💡
Parameters: 12 billion
License: MIT
Usage: Allowed for research and commercial use
📋
Many open-source LLMs offer variations of the models, fine-tuned for a specific task. You must check the license for them; it may not be the same as the one listed here.

3. Cerebras-GPT

14 Top Outstanding Open Source LLMs For Research and Commercial Use

Cerebras-GPT is a family of seven GPT-3 open-source LLM models ranging from 111 million to 13 billion parameters.

These models aim for higher accuracy with a faster training times, lower cost, and consume less energy. It is trained using DeepMind's Chinchilla formula.

💡
Parameters: 12 billion
License: Apache 2.0
Usage: Allowed for research and commercial use and fine-tuned for ChatGPT like interaction

4. Bloom

Bloom is an open-access multilingual LLM model that is trained to continue text from prompts, and can be trained for other text-based tasks.

The model includes 176 billion parameters, and can output in 46 different languages and 13 programming languages.

💡
Parameters: 176 billion
License: RAIL License v1.0
Usage: Allowed for research and non-commercial entities

5. DLite V2

14 Top Outstanding Open Source LLMs For Research and Commercial Use

DLite by AI Squared aims to present lightweight LLMs that you can use almost anywhere.

The family of model includes variations ranging from 123 million to 1.5 billion parameters. It utilizes the same database built by Databricks for Dolly 2.0. Hence, it is tuned for the same use-case.

💡
Parameters: 1.5 billion
License: Apache 2.0
Usage: Allowed for research and commercial use and fine-tuned for ChatGPT like interaction

6. MPT-7B

14 Top Outstanding Open Source LLMs For Research and Commercial Use

MPT-7B is another prominent open-source model by Databricks. Unlike Dolly 2.0, it competes and surpasses Meta's LLaMA-7B in many ways.

The model was trained using a mixed set of data from various sources, including 6.7 billion parameters. It also aims to be an affordable alternative to the closed source LLM models available.

💡
Parameters: 6.7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use and fine-tuned for ChatGPT like interaction

7. XGen 7B

14 Top Outstanding Open Source LLMs For Research and Commercial Use

XGen 7B is an LLM model by Salesforce, one of the leading companies in its industry. The model has been trained for text generation and code tasks, claiming performance benefits over similar models like MPT.

💡
Parameters: 6.7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use and fine-tuned for text generation/code tasks

8. OpenLLaMA

14 Top Outstanding Open Source LLMs For Research and Commercial Use

OpenLLaMA is a reproduction of Meta's popular LLaMa model trained with a different dataset to make it available under a permissive license, when compared to Meta's custom commercial license.

Currently, you can find OpenLLaMA 7Bv2 and a 3Bv3 model, with all of them trained using a mixture of datasets.

You can learn more about it on its GitHub page.

💡
Parameters: 7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use

9. StarCoder

14 Top Outstanding Open Source LLMs For Research and Commercial Use

StarCoder is an interesting open-source LLM trained using data from GitHub (commits, issues etc) fine-tuned for coding tasks.

It includes 15 billion parameters and trained on 80+ programming languages. Considering the data is from GitHub, you will need to add attribution wherever necessary when you use the model.

💡
Parameters: 15 billion
License: Open RAIL-M v1
Usage: Allowed for research and commercial use (with restrictions not to misuse it)

10. CodeT5

14 Top Outstanding Open Source LLMs For Research and Commercial Use

Another open-source from Salesforce, fine-tuned for coding tasks and code generation. CodeT5 is one of the most competing code LLMs that features 16 billion parameters, and claims to outperform OpenAI's code-cushman001 model.

💡
Parameters: 16 billion
License: BSD 3-Clause
Usage: Allowed for research and non-commercial use

11. Yi-1.5

14 Top Outstanding Open Source LLMs For Research and Commercial Use

Yi-1.5 is an exciting open-source LLM featuring a whopping 34.4 billion parameters.

It is fine-tuned for coding, math, reasoning, and instruction-following capability. Originally, this model wasn't open-source, but later it was. And, that's a good thing.

💡
Parameters: 34.4 billion
License: Apache 2.0
Usage: Allowed for research and commercial use

12. SOLAR-10.7B

14 Top Outstanding Open Source LLMs For Research and Commercial Use

SOLAR-10.7B is a pre-trained open-source LLM by Upstage.ai that just generates random text. You need to fine-tune it to adapt to your particular requirements.

When compared to models with larger parameters, it promises to provide a good model performance efficiency and scalability even with 10.7 billion parameters. It is an incredibly popular adaptable language model.

💡
Parameters: 10.7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use

13. Mistral 7B

14 Top Outstanding Open Source LLMs For Research and Commercial Use

Mistral 7B is a powerful language model that claims to outperform LLaMa 1/2 in various ways. The large language model is capable of math, reasoning, word knowledge, QA, and more.

It is a pre-trained generative text model with 7 billion parameters. Hence, it does not have any moderation mechanisms. So, you need to fine-tune it to output moderated results.

💡
Parameters: 7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use

14. OLMo-7B

14 Top Outstanding Open Source LLMs For Research and Commercial Use

OLMo-7B is yet another open language model pre-trained with Dolma dataset featuring three trillion token open corpus. The entire training data, model weights, and the code is open-source, and available to use without restriction.

💡
Parameters: 7 billion
License: Apache 2.0
Usage: Allowed for research and commercial use

💬 What is your favorite open-source LLM? Have you tried running any of them? Share your thoughts in the comments below!



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