Qwen 4Alibaba
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TabFMGoogle
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Related Products
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About
Qwen 4 is Alibaba’s next-generation Qwen foundation model, announced on September 22, 2026, and currently in training. It will follow the Qwen3.8 generation as part of Alibaba’s broader roadmap for increasingly capable foundation and agentic AI models. Alibaba has not yet published Qwen 4’s parameter count, architecture, benchmark results, context window, pricing, release date, or availability details. The announcement places Qwen 4 within a research strategy focused on large-scale model training, agentic reinforcement learning, multimodal intelligence, and recursive self-improvement. Alibaba separately said that later Qwen 4.5 and Qwen 5 models are expected to scale into the 5-to-10-trillion-parameter range, but that figure was not attributed specifically to Qwen 4. Because Qwen 4 has not yet been released, detailed performance comparisons and production capabilities remain unconfirmed.
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About
TabFM is a zero-shot foundation model for tabular data, designed to simplify classification and regression workflows that traditionally require manual model training, hyperparameter tuning, and domain-specific feature engineering. Built specifically for tables, TabFM reframes tabular prediction as an in-context learning problem: instead of fitting a new supervised model to each dataset, it takes historical training examples and target testing rows together as one unified prompt, then interprets relationships between columns and rows at inference time. Because tables are two-dimensional and orderless, TabFM uses a hybrid architecture that combines alternating row and column attention, row compression, and a dedicated Transformer for in-context learning over compressed row embeddings. This design lets the model capture complex feature interactions and dependencies while keeping prediction computationally efficient for larger datasets.
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Platforms Supported
Windows
Not Supported
Mac
Not Supported
Linux
Not Supported
Cloud
Supported
On-Premises
Not Supported
iPhone
Not Supported
iPad
Not Supported
Android
Not Supported
Chromebook
Not Supported
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Audience
AI developers, researchers, enterprises, agent builders, and organizations following Alibaba’s next generation of foundation models, although its final production use cases cannot yet be determined because the model remains in training
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Audience
Data scientists and machine learning teams seeking to perform zero-shot classification and regression on tabular data without task-specific training
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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Support
Phone Support
Not Supported
24/7 Live Support
Not Supported
Online
Supported
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API
Offers API
Supported
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API
Offers API
Not Supported
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Screenshots and VideosNo images available
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Pricing
No information available.
Free Version
Not Supported
Free Trial
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Pricing
Free
Free Version
Supported
Free Trial
Not Supported
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Reviews/
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Reviews/
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
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In Person
Not Supported
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Training
Documentation
Supported
Webinars
Not Supported
Live Online
Not Supported
In Person
Not Supported
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Company InformationAlibaba
Founded: 1999
China
qwen.ai
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Company InformationGoogle
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/
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Integrations
Alibaba Cloud
Supported
Alibaba Cloud Model Studio
Supported
Cherry Studio
Supported
Cline
Supported
ClinePass
Supported
Happy Shrimp 1.0
Supported
Hermes Agent
Supported
Hugging Face
Supported
Model Context Protocol (MCP)
Supported
ModelScope
Supported
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Integrations
Alibaba Cloud
Not Supported
Alibaba Cloud Model Studio
Not Supported
Cherry Studio
Not Supported
Cline
Not Supported
ClinePass
Not Supported
Happy Shrimp 1.0
Not Supported
Hermes Agent
Not Supported
Hugging Face
Not Supported
Model Context Protocol (MCP)
Not Supported
ModelScope
Not Supported
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