Hi developers! The topic that is going to capture the attention of everyone in the room is the open-source large language models.
These open-source LLMs give the developers the freedom to create without the headaches of expensive costs and licensing complexity.
For instance, open-source models like LLaMA 3.1 can be utilized without the expenses associated with proprietary alternatives.
Whereas the permissive licensing of some other open-source LLMs allows developers to modify and deploy models tailored to their specific needs.
Privacy also plays a major role in choosing open source models over proprietary LLMs.
Let’s take a look at how using these open source LLMs may help you change how you engage with development.
What are open source models?
Open source models are AI tools whose design, code, and functionality are made freely accessible to everyone.
This means that developers or users can not only use them but also study, modify, and improve these models without restrictive licenses or high costs. I
In simpler terms, imagine a shared recipe: you can use it as it is, tweak it to suit your taste, or even make it better for others to try.
For beginner developers, this openness makes them a game-changer.
Whether you’re exploring programming, working on data analysis, or looking for inspiration during a writing project, open source models offer powerful tools without the financial or technical barriers of proprietary software.
Picture having a highly capable assistant on demand – helping you craft sound text, interpret complex data, or even brainstorm ideas – without hefty price tags or limitations. By embracing open source models, you not only unlock a world of possibilities but also join a community that thrives on sharing and innovation.
The opportunities are as limitless as your imagination.
Why developers should care about open-source models?
Open-source models are a lifesaver to most of us in the tech space for a couple of reasons:
Cost-effective advancement
Let’s not pretend that commercial AI solutions do not come with a heavy price tag. Life is very hard, but with open-source models, you do not have to break the bank to advance.
They remove those costs, allowing the advancement of AI to be done according to any individual's budget.
Bespoke applications
We all have projects with different specifications. The beauty is that with open source models, you can change those models and many features to fit your requirements. Do not be bound by standard solutions-you will create solutions that get the job done.
For example, with Hugging Face’s question-answering models, you can build a tool that answers user queries based on your specific dataset.
Check out the tutorial here to see how easily these models can be tailored to your needs.
Clear and honest
Trust and transparency are the basic principles.
With open-source models, you know precisely how they work because you can access and modify the source code to meet your specifications and standards.
Open-source models and the people behind them
It's not just the tech – it's people.
There are communities you can join, ideas you can contribute in the future,and things you can learn and generally take part in that drive change across many fields.
Bottleneck-free
Open-source models are there for you whether you are just starting out or already in the process of expanding your business to millions of clients. You don’t have to regularly upgrade in order not to outgrow the solution and all its components.
Some of the best and dominant open source LLM models
Let's begin with the most noteworthy developments among open-source models available in our community nowadays:
LLaMA by Meta
LLaMA is well known for its reliability in performing a multitude of tasks, particularly when it comes to conversations and coding assistance. It is flexible in nature, which leads to a greater degree of developer adoption.
My take: LLaMA’s reliability and flexibility make it one of my favorite tools for building conversational AI and coding assistance. It adapts beautifully to different workflows, but it can feel resource-heavy for smaller teams or individual developers. Fine-tuning for niche applications is rewarding but often requires more effort than anticipated.
Mistral 7B
Mistral 7B achieves a good balance between power and efficiency. When it comes to performance and the need for low weight, Mistral 7B can be used in both experimental and production conditions.
My take: Mistral 7B nails the balance between power and efficiency, making it perfect for both experimentation and production. Its lightweight nature is a blessing for developers like me who value agility, but for complex tasks, it sometimes lacks the depth of larger models. Still, it’s an excellent choice when scalability isn’t your primary concern.
EleutherAI’s GPT-J
EleutherAI's GPT-J has grown in popularity as a realistic text creator, and it has been incorporated into many parameters requiring content. As a customizable tool, it can adapt to various needs.
My take: GPT-J impresses me with its ability to generate realistic text while remaining highly customizable. It’s a go-to for content generation and chat applications, though its resource demands can be daunting without the right infrastructure. Occasionally, it struggles with nuanced understanding, but for most general tasks, it’s reliable and versatile.
MPT by MosaicML
MPT is designed for scalability and claims to deliver great performance while handling demanding workloads. It’s a reasonable option if scaling up effectively is your goal.
My take: MPT’s scalability and performance for demanding workloads are standout features, especially when you need reliability at scale. However, its full potential shines only if you have the infrastructure to back it. For smaller projects, it might feel like too much model for too little workload.
BLOOM by BigScience
Serving 40+ language families, BLOOM can be used in various international settings without any problems. It is designed to accommodate numerous users, making it suitable for multi-language applications.
My take: BLOOM is my favorite for multi-language projects—it handles over 40 language families with ease, making it ideal for international applications. However, its size and complexity require serious infrastructure, which could be a hurdle for beginners. Despite that, it’s an unmatched choice for developers with global ambitions.
We're ready to unpack how Pieces integration with some of these open-source large language models is a game-changer. I have been using it for a while, and it is something else.
Supercharging your workflow with co-pilot
What if you were given the option to utilize local models that were improved for developer efficiency and meant to increase your development productivity by 10x?
Imagine the integration of all that code, snippets, and workflows. It starts to spiral out of control at that point. Meet Pieces.app – I like to think of it as a development assistant that has AI
This is how it has changed my workflow:
Snipping of codes with Pieces
You know how you are forever struggling with different coding parts and have to copy-paste elements? Well, Pieces automatically records and classifies such codes on the fly. But here's the kicker – it creates context and even adds metadata.
And when you're deep inside a project with GPT pretending to look for snippets from last week, just look for it in Pieces.
Smart AI
This is where Pieces integration makes the most sense with open-sourced LLMs. It doesn’t just hold your codes; it’s intelligent.
Fiddling around with the fine tunes of Mistral 7B? Pieces could go ahead and throw in some optimizations, detail some complex sections, and assist in debugging.
It is almost like having a very senior developer shadow you without the stench of the tea.
Seamless integration
It almost integrates everything well. VSCode, PyCharm, and more.
The other day I was editing some BLOOM code and having Pieces snippets in my IDE ready to go was extremely helpful.
Forget the madness of switching contexts, I can bring the local model to my IDE itself.
Local processing for privacy
This is a very important point when dealing with LLMs and sensitive data – Everything is done locally with Pieces. There is no fear of revealing your proprietary improvements or information on a cloud server. You can download your local model and choose to process everything on the device. Our focus on privacy and security goes beyond the ability to leverage LLLMs within Pieces Copilot.
Contextual search that actually works
Ever searched for that one piece of code you wrote ages ago and you just can’t seem to find it?
I find Pieces' search to be extremely intuitive. It grasps the situation in which the user is operating and thus provides them with fast and precise results.
The fun part?
You can use local models to work with Pieces without exposing any of your trade secrets to cloud providers.
What are the major challenges to open source projects?
It is correct that the use of open-source models is not without its problems, but as long as the right tools and communities are present, we’ll be able to overcome them together:
Integrating models
Integrating these models can be quite challenging. Seek help from forums or online communities — there is always somebody willing to help you.
Getting the alerts
The open-source world is also a very active space, and it can sometimes be strenuous to keep up. To do this, get subscribed to the appropriate news, join community channels and go through thought leaders.
Regularity in testing
Performance monitoring and some maintenance are essential and should be done regularly. When testing, use continuous data for benchmarking and adjust whenever needed.
How to manage open source projects
In order to ensure the long-term success of open-source projects, it is essential to manage them in a way that goes beyond simply producing high-quality code.
Based on my experience and observations, I have compiled a list of five certain ways to make sure your open-source project thrives and draws in a dedicated team of volunteers.
Let’s also take a look at some exciting open-source LLM projects that demonstrate these best practices.
Start with clear documentation
Your README serves as the entry point to your project—ensure it is welcoming!
Clarify the objective of your project, provide instructions for its use, and outline the ways in which others may help. Incorporate examples and quick-start tutorials for users seeking immediate engagement.
Hugging Face Transformers remains the gold standard when it comes to clear documentation and a platform to explore open-source LLMs.
Its well-structured documentation, tutorials, and model hub make it easy for both beginners and experts to leverage cutting-edge open-source models and also play around with them to finetune it.
An excellent example of how your project should present information for contributors and users alike is Mistral 7B, an open-source LLM that gives a clear model card on Hugging Face. This card guides users on usage, restrictions, and fine-tuning tactics.
Define contribution guidelines
What is the proper way for contributors to submit issues or pull requests? Establish these guidelines. Which set of rules for code should they adhere to?
To keep things clear and make sure new contributors know what to do, a CONTRIBUTING.md file is a must-have.
A great example is of LLaMA 2 by Meta the community forks always includes contribution guidelines.
These guidelines explain how to report bugs, make suggestions for changes, and add new features.
Build and nurture a community
Participate in community conversations by responding to concerns, inviting new members, and bringing up practical uses of your project. Bring attention to the fact that users are creating chatbots, automating translations, and analysing text data, among other unique use cases.
Here, platforms like Hugging Face really shine; users actively participate in a dynamic ecosystem of cooperation and innovation by sharing their trials with fine-tuning on a regular basis.
Talking about communities we have our very active Pieces for Developers Community where you can participate and share about your projects and connect with like minded individuals.
What kinds of organizations use open source LLMs?
More and more businesses in all sorts of sectors are using LLMs, or big language models, that are available as open-source.
These include private companies that use LLMs for NLP, academic institutions that use them for data analysis and ML projects, and public sector organizations that use them to improve efficiency and decision-making.
Many firms are drawn to open-source LLMs because of their versatility and data privacy approach as other available models like GPT-4o only provide API access and organizations take caution when it comes to sharing their data with these hosted models.
This makes them a good choice for using AI and language processing technologies.
Emerging open source LLM projects to keep an eye on
LLaMA 3.1 by Meta AI
Meta AI's LLaMA 3.1 offers models ranging from 8 billion to 405 billion parameters, supporting multiple languages and a context length of 128,000 tokens. This advancement enhances performance in complex reasoning tasks and maintains context in extended conversations.
BLOOM by BigScience
BLOOM is a 176-billion-parameter multilingual model supporting 46 natural languages and 13 programming languages. Developed through a large-scale collaboration, it emphasizes open access and transparency in AI development.
Falcon 180B by Technology Innovation Institute (TII)
The Falcon series includes models like Falcon 40B and Falcon 180B, recognized for their performance in the open-source community.
These models are designed to be efficient and versatile across various applications.
h2oGPT by H2O.ai
h2oGPT provides a collection of open-source large language models with parameter counts varying from 7 billion to 40 billion.
You can get these models with open rights, which makes it easier to use them in business and helps make AI technology more accessible to everyone.
H2OGPT's architecture is more complicated, with extra parts like a hierarchical encoder and a multi-resolution decoder.
GPT-3's architecture is easier, with only one transformer encoder and decoder.
To leave you with something resourceful check out the Awesome-LLM repository on GitHub as it is a good place to start for people who want to learn more about current trends in the LLM scene.
It has many projects, papers, tools, and datasets that help people learn about and create LLMs.
I love the Open LLM section which is constantly updated with the latest developments.
