AI’s Next Big Step? Insights From Thinking Machines’ Inkling

Thinking Machines has officially released the full weights of its latest multimodal AI model, Inkling, making it available on Hugging Face under the open-source license Apache 2.0. This marks a significant development in AI transparency, as the company openly admits that Inkling is not the strongest model currently available, but emphasizes its commitment to open access and honesty about its capabilities.

The Inkling model is a Mixture-of-Experts transformer with 975 billion total parameters and 41 billion active. It supports a 1-million-token context window and was trained on 45 trillion tokens across text, images, audio, and video. Notably, it is multimodal from input to output, processing text, images, and audio without relying on vision adapters, with training data and model architecture designed for joint processing.

Released openly on Hugging Face under Apache 2.0, the model’s weights can be downloaded, modified, and deployed independently. However, the company’s separate Model Acceptable Use Policy (AUP) reportedly restricts certain uses, such as surveillance and automated decision-making, raising questions about the true openness of the release. The company’s transparency includes sharing benchmark scores from external sources, which show strengths in speech and safety benchmarks but middling performance in pure text tasks.

Implications for AI Development and Openness

This release signals a shift toward greater transparency in the AI community, emphasizing open access over proprietary control. By releasing the full weights and openly acknowledging its limitations, Thinking Machines challenges the industry norm of guarded model releases. The move could influence how future models are shared, with potential impacts on innovation, regulation, and responsible AI deployment.

Industry Trends Toward Open-Weight Models

Over the past year, there has been increasing debate about the transparency and openness of large AI models. Companies like OpenAI and Meta have released models with varying degrees of openness, but many restrict access or impose licensing conditions. Thinking Machines’ approach with Inkling, emphasizing open weights and honest performance disclosures, aligns with a broader movement toward democratizing AI research and development. The release also follows recent government and industry calls for more transparency and safety in AI deployment.

“We believe in openness and honesty about our models’ capabilities and limitations. Our goal is to foster responsible innovation.”

— Thinking Machines spokesperson

AI Systems Performance Engineering: Optimizing Model Training and Inference Workloads with GPUs, CUDA, and PyTorch

View Latest Price

As an affiliate, we earn on qualifying purchases.

As an affiliate, we earn on qualifying purchases.

Questions About Usage Restrictions and Long-term Access

It remains unclear how the reported separate Model Acceptable Use Policy (AUP) will be enforced and whether it limits the model’s open-source nature. The exact scope of restrictions and their legal enforceability are not yet verified, raising questions about how freely the model can be used in practice.

Next Steps in Model Adoption and Benchmark Validation

Expect independent researchers and developers to test Inkling’s performance across various domains, with particular focus on safety, bias, and real-world applications. Further clarification on the AUP and licensing terms will likely emerge as the model is adopted more widely, potentially influencing future open-weight releases.

Key Questions

Why is the open release of Inkling significant?

The open release allows researchers and developers to inspect, modify, and deploy the model independently, promoting transparency and responsible AI development.

What are the limitations of Inkling’s openness?

Although the weights are open under Apache 2.0, reports suggest there may be additional restrictions via a separate Acceptable Use Policy, which could limit certain applications.

How does Inkling compare to other models?

Benchmark scores show strong performance in speech and safety tasks but middling results in some text-only benchmarks. Its multimodal design is a notable feature.

What does this mean for the future of AI models?

This move could set a precedent for more open and transparent model releases, encouraging industry-wide shifts toward openness and responsible use.

Source: ThorstenMeyerAI.com