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ease
features
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support

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Description

Kimi K2 Thinking is a sophisticated open-source reasoning model created by Moonshot AI, specifically tailored for intricate, multi-step workflows where it effectively combines chain-of-thought reasoning with tool utilization across numerous sequential tasks. Employing a cutting-edge mixture-of-experts architecture, the model encompasses a staggering total of 1 trillion parameters, although only around 32 billion parameters are utilized during each inference, which enhances efficiency while retaining significant capability. It boasts a context window that can accommodate up to 256,000 tokens, allowing it to process exceptionally long inputs and reasoning sequences without sacrificing coherence. Additionally, it features native INT4 quantization, which significantly cuts down inference latency and memory consumption without compromising performance. Designed with agentic workflows in mind, Kimi K2 Thinking is capable of autonomously invoking external tools, orchestrating sequential logic steps—often involving around 200-300 tool calls in a single chain—and ensuring consistent reasoning throughout the process. Its robust architecture makes it an ideal solution for complex reasoning tasks that require both depth and efficiency.

Description

TabFM is an innovative zero-shot foundation model specifically created for handling tabular data, aimed at streamlining classification and regression processes that usually necessitate extensive manual model training, hyperparameter optimization, and tailored feature engineering. By transforming the challenge of tabular prediction into an in-context learning task, TabFM avoids the need to train a new supervised model for every dataset; instead, it consolidates historical training examples and target testing rows into a single cohesive prompt, allowing it to discern the intricate relationships between various columns and rows during inference. Given that tables are inherently two-dimensional and do not rely on a specific order, TabFM employs a hybrid architecture that integrates alternating attention mechanisms for both rows and columns, row compression techniques, and a specialized Transformer designed for in-context learning based on these compressed row embeddings. This sophisticated framework enables the model to effectively capture complex interactions and dependencies among features while maintaining computational efficiency, particularly advantageous for processing larger datasets. Furthermore, this approach not only enhances performance but also significantly reduces the time and resources typically required for model development in tabular data tasks.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

GPT-5.1 Instant
GPT-5.1 Thinking
GPT-5.2
GPT-5.2 Instant
GPT-5.2 Pro
GPT-5.2 Thinking
GPT-5.4
GPT-5.4 Pro
GPT-5.4 mini
GPT-5.4 nano
GPT-5.5
GPT-5.5 Pro
GPT-5.6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
GPT-6
GPT‑5.4 Thinking
Hugging Face
Nebius Token Factory
Zo Computer

Integrations

GPT-5.1 Instant
GPT-5.1 Thinking
GPT-5.2
GPT-5.2 Instant
GPT-5.2 Pro
GPT-5.2 Thinking
GPT-5.4
GPT-5.4 Pro
GPT-5.4 mini
GPT-5.4 nano
GPT-5.5
GPT-5.5 Pro
GPT-5.6 Luna
GPT-5.6 Sol
GPT-5.6 Terra
GPT-6
GPT‑5.4 Thinking
Hugging Face
Nebius Token Factory
Zo Computer

Pricing Details

Free
Open source
Free Trial
Free Version

Pricing Details

Free
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

Moonshot AI

Founded

2023

Country

United States

Website

moonshotai.github.io/Kimi-K2/thinking.html

Vendor Details

Company Name

Google

Founded

1998

Country

United States

Website

research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

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