Venice.aiVenice isn't your typical AI platform.
We've built our foundation on open-source AI models because they align with our beliefs: transparency, privacy, and uncensored exploration of ideas. Our Latin motto also emphases this commitment: ad intellectum infinitum – “toward infinite understanding.”
On the other hand, the leading closed-source AI models are black boxes, raising concerns about control, bias, and surveillance. No one outside these companies knows what data they were trained on, what their exact weights are, or what filters, content policies and system prompts control their output. The closed-source AI companies have visibility into your every interaction - attached to your identity - and save all of it forever. They store your conversations, review them, and potentially share your data with governments and advertisers.
At Venice, we've taken a different path. By using open-source models, we offer cutting-edge AI that anyone can examine and scrutinize - including training data quality, biases and appropriateness for the model's intended uses. Today’s open-source models are on-par with, and sometimes exceed, the performance and capabilities of leading closed models.
But what exactly are the differences between open-source and closed-source AI models, and why should you care?
This blog dives into these questions, and we'll also outline Venice's model selection criteria and introduce our latest addition: the groundbreaking Llama 3.1 405B model.
But first, why do we believe in the fundamental value of open-source development in AI?
Open-source AI is vital to prevent monopolization of truth
As AI becomes increasingly influential, relying solely on closed-source models controlled by a few tech companies risks centralizing power and limiting access to this transformative technology. For example, during an election year, an AI company could subtly influence millions of voters by censoring or prioritizing certain political viewpoints in its responses, effectively becoming an unaccountable gatekeeper of information.
Conversely, open-source AI prevents this monopolization of truth.
It ensures no single entity or cartel can dictate what machine intelligence is permitted to know or say. This allows for diverse approaches to AI development and acts as a safeguard against potential misuse, bias, or censorship.
While some believe that government regulators should legislate what is true, or proper, or permissible to know or say, we think that no human or group of humans ought to have such power. You don’t fight bias and misinformation with monopoly control by a central party. This is a recipe for corruption, abuse, and dystopia.
Instead, open, competing systems are more effective and less dangerous.
The principles of open-source software create an environment where AI development can evolve through thoughtful iteration and experimentation, free from political or corporate agendas.
What are the differences between closed and open-source models?
Open-source AI models are transparent, as developers make their code, architecture, and "weights" publicly available. Anyone can inspect, modify, and contribute to their development, with thousands of developers worldwide continually refining these models, often outpacing the update cycles of closed-source alternatives. A simple search for “Llama” on the Hugging Face website reveals a staggering 47K+ community fine-tuned Llama models. These models also provide unrestricted access, enabling anyone in the world to utilize their power.
Closed-source models, in contrast, are proprietary and generally "black boxes." Their inner workings are hidden, known only to the companies that develop them, such as OpenAI’s GPT-4 model or Anthropic’s Claude. These companies tightly control access to these models, typically offering them through restricted APIs. Users, researchers and developers can't inspect the code, modify the architecture, or contribute to the model's development.
Updates to closed-source models occur at the discretion of the developing company, often without transparency about what changes have been made. Furthermore, the training data and fine-tuning processes of closed-source models remain confidential, making it challenging to assess potential biases or limitations. Users must rely on the company's claims about the model's capabilities and ethical considerations, without the ability to independently verify these claims.
Here's a quick comparison of key criteria:
AI is moving forward at breakneck speed, and recent developments have shown that open-source models are now often roughly equivalent with, and in some areas surpassing, their closed-source counterparts. This has become best exemplified by the recent release of Meta's Llama 3.1 405B model, which demonstrates competitive performance with leading proprietary models across various benchmarks, which we discuss later in this blog.
How Venice selects open-source models
At Venice, we carefully evaluate open-source models based on several factors.
Performance benchmarks are crucial - we assess how well each model performs across various tasks. We always aim to offer cutting-edge AI that can compete with, and often surpass, closed-source alternatives.
We also consider computational requirements to ensure efficient real-time interactions. Developer and community support is another vital aspect, as we look for models with active development.
Importantly, we prioritize models with minimal inherent content restrictions. While all models have some built-in limitations based on their training data, we seek out those with the least restrictive boundaries. We analyze each model's base training and content restrictions to identify those offering the most open and unbiased responses.
We also assess each model's adaptability, focusing on how well we can instruct it to ignore overly cautious content policies while maintaining factual accuracy. This approach allows us to provide AI interactions that respect your intellectual freedom and curiosity, while maintaining our commitment to delivering unbiased and uncensored information.
Our current lineup of models reflects this careful selection process, offering a range of capabilities for both text and image generation.
Text Generation Models
Venice offers a range of open-source text generation models to suit your needs:
Web-Enabled Nous Theta: Specialized for web-based interactions, optimized for search queries and providing up-to-date information, this model is also exceptionally fast and is the default in Venice.
Dogge 70B: Offering an excellent performance-to-size ratio with 70 billion parameters, versatile for various language processing tasks
Llama 3.1 405B: With 405 billion parameters, it provides the most cutting edge open-source AI experience, with exceptional language understanding and generation, ideal for complex reasoning tasks. While relatively slower than the other models, its answers are often the most thorough (unless you need web access or live info).
Image Generation Models
Complementing our text generation capabilities, we've also curated a selection of powerful open-source image generation models:
Playground v2.5: A versatile choice for various image creation tasks.
FLUX: A brand new model gaining wide popularity for best-in-class photo-realism.
Fluently XL Final: Ideal for creating naturalistic, highly detailed images with high-resolution output.
Dreamshaper & PixArt Sigma: Specialized in animated image generation, stylized images and abstract visualizations.
These models form the backbone of Venice's image generation capabilities. On the text side, let's explore the latest addition to Venice in more detail: the game-changing Llama 3.1 405B.
Meta’s Llama 3.1 405B is now available to all Venice users
On the day it was published, Venice was pleased to enable Meta’s brand new open-sourced frontier model, the largest (and empirically best) published to date, with 405 billion parameters.
We recommend using this model if you want the most intelligent results. However, a few caveats:
405B is not connected to the internet. We recommend continuing to use the web-enabled version of Nous Theta for your questions that require real-time information.
Inference on 405B will be slower relative to smaller models. Future optimizations may be possible, but we wanted to offer users access to this model as soon as possible.
This model is still somewhat censored, however we’re expecting to provide access to an uncensored version of Llama 3.1 soon.
So, what excited us about this new model? Meta’s 3.1 405B demonstrates that open-source models have not only become competitive, but across a number of metrics, impressively exceed the capabilities and performance of many centralized, closed-source models on 7 out of 15 standard LLM metrics.
Here are a few key metric highlights:
MMLU (Multi-task Language Understanding)
Rating: Competitive with GPT-4 Omni and exceeds all other models in its class.
This benchmark is designed to measure knowledge acquired - similar to how we evaluate humans. In essence, it’s like an SAT score for LLM’s. The benchmark covers 57 subjects across STEM, the humanities, and more, and ranges in difficulty from an elementary to an advanced professional level, testing both world knowledge and problem solving ability.
ARC Challenge (Abstraction and Reasoning Corpus)
Rating: Exceeds all other models in its class.
This benchmark is designed to measure the AI’s skill acquisition and track progress towards achieving human-level AI. Think of it as an IQ test for LLM’s, evaluating an AI's ability to tackle each task from scratch, using only the kind of prior knowledge about the world that humans naturally possess, known as core knowledge.
GSM8K (Grade School Math 8K)
Rating: Exceeds all other models in its class.
This benchmark measures the LLM’s ability to answer basic mathematical problems based on the GSM8K dataset, designed to test models' ability to understand and reason about mathematical word problems with varying linguistic complexity. Conceptually simple problems can be challenging for language models due to the diversity of problems that require performing a sequence of calculations using basic arithmetic operations and include a number of solution steps to solve.
Venice puts powerful open-source AI in your hands
The monopolization of AI is a threat to the free flow of information and ideas.
As AI becomes more influential in our lives, relying on closed-source models risks creating a single, unaccountable arbiter of truth and intelligence.
Open-source models, like Llama 3.1 405B, are the antidote to this risk. These models allow anyone to inspect, modify, and contribute to their development, ensuring no single entity can dictate what machine intelligence knows or says. Open-source AI models are rapidly advancing, now offering capabilities that rival or exceed their closed-source counterparts.
At Venice, we're focused on making these powerful models accessible to everyone through our easy-to-use interface.
By using Venice, you're supporting a future where truth isn't captured by authoritarian interests or hidden behind black boxes. As this revolutionary technology continues to advance, we remain committed to offering you the latest open-source innovations.
Try Venice now and experience the full power of open-source, private, and uncensored AI.
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