TabFM vs. Inkling

TabFM is a cutting-edge foundation model designed for zero-shot learning specifically tailored to manage tabular data, with the goal of simplifying the processes of classification and regression that often demand considerable manual training, hyperparameter tuning, and customized feature engineering. By reframing the difficulties associated with tabular prediction as an in-context learning challenge, TabFM eliminates the necessity of training a distinct supervised model for each dataset; rather, it merges previous training examples with target testing rows into a unified prompt, enabling it to identify the complex relationships that exist between different columns and rows during the inference phase. Since tables are fundamentally two-dimensional and do not depend on a predetermined order, TabFM utilizes a hybrid architecture that combines alternating attention mechanisms for both rows and columns, along with row compression methods, and a dedicated Transformer designed for in-context learning based on these compressed row representations. This advanced structure allows the model to adeptly capture intricate interactions and dependencies among features while ensuring computational efficiency, which is particularly beneficial for dealing with larger datasets. Moreover, this innovative methodology not only boosts performance but also markedly decreases the time and resources generally required for the development of models in tabular data applications, paving the way for more effective analytical solutions. As a result, TabFM represents a significant advancement in the realm of machine learning for tabular data, starting a new era in data analysis.

Inkling is an open-weights multimodal AI model from Thinking Machines built to support customization, agentic workflows, coding, reasoning, vision, audio, and enterprise AI use cases. The model is a Mixture-of-Experts transformer with 975 billion total parameters, 41 billion active parameters, 256 routed experts per MoE layer, and six routed experts active per token. It supports context windows up to 1 million tokens and was pretrained on 45 trillion tokens across text, images, audio, and video. Inkling is designed as a broad foundation model rather than a narrowly optimized benchmark model, giving it balanced capabilities across reasoning, coding, factuality, instruction following, vision, audio, tool use, and safety. Its controllable thinking effort lets developers adjust how much computation and generated reasoning the model uses, helping teams balance quality, latency, and cost for different production needs. The model can run agentic coding tasks, use tools, create web apps, generate polished multi-page artifacts, reason over long contexts, and work through iterative refinement loops. For multimodal tasks, Inkling can process images, answer questions about visual content, transcribe and reason over audio, follow spoken instructions, and combine visual reasoning with code-based tools such as Python. Thinking Machines trained Inkling for calibration, instruction following, factual reliability, refusal behavior, and safety across multiple modalities, including evaluations for dangerous capabilities and human-AI threat vectors. Inkling is available on Tinker for fine-tuning, with 64K and 256K context options, an Inkling Playground for testing, cookbook recipes, and support for multimodal post-training workflows. Its full weights are available on Hugging Face, and deployment support is available through APIs and infrastructure partners such as TogetherAI, Fireworks, Modal, Databricks, Baseten, SGLang, vLLM, llama.cpp, and transformers.