Qwen-7B

Qwen-7B

Alibaba
TabFM

TabFM

Google
+
+

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About

Qwen-7B is the 7B-parameter version of the large language model series, Qwen (abbr. Tongyi Qianwen), proposed by Alibaba Cloud. Qwen-7B is a Transformer-based large language model, which is pretrained on a large volume of data, including web texts, books, codes, etc. Additionally, based on the pretrained Qwen-7B, we release Qwen-7B-Chat, a large-model-based AI assistant, which is trained with alignment techniques. The features of the Qwen-7B series include: Trained with high-quality pretraining data. We have pretrained Qwen-7B on a self-constructed large-scale high-quality dataset of over 2.2 trillion tokens. The dataset includes plain texts and codes, and it covers a wide range of domains, including general domain data and professional domain data. Strong performance. In comparison with the models of the similar model size, we outperform the competitors on a series of benchmark datasets, which evaluates natural language understanding, mathematics, coding, etc. And more.

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.

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Platforms Supported

Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook

Audience

Large language model developers

Audience

Data scientists and analytics teams that need accurate tabular classification and regression without repetitive training, tuning, and feature-engineering work

Support

Phone Support
24/7 Live Support
Online

Support

Phone Support
24/7 Live Support
Online

API

Offers API

API

Offers API

Screenshots and Videos

Screenshots and Videos

Pricing

Free
Open source
Free Version
Free Trial

Pricing

Free
Free Version
Free Trial

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Reviews/Ratings

Overall 0.0 / 5
ease 0.0 / 5
features 0.0 / 5
design 0.0 / 5
support 0.0 / 5

This software hasn't been reviewed yet. Be the first to provide a review:

Review this Software

Training

Documentation
Webinars
Live Online
In Person

Training

Documentation
Webinars
Live Online
In Person

Company Information

Alibaba
Founded: 1999
China
github.com/QwenLM/Qwen-7B

Company Information

Google
Founded: 1998
United States
research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/

Alternatives

Athene-V2

Athene-V2

Nexusflow

Alternatives

Evo 2

Evo 2

Arc Institute
ChatGLM

ChatGLM

Zhipu AI
Mistral 7B

Mistral 7B

Mistral AI
CodeQwen

CodeQwen

Alibaba
Qwen2

Qwen2

Alibaba
MLBox

MLBox

Axel ARONIO DE ROMBLAY

Categories

Categories

Integrations

Alibaba Cloud
C
C++
CSS
Elixir
F#
GaiaNet
HTML
Kotlin
LM-Kit.NET
ModelScope
Python
Qwen Studio
QwenCloud
R
Ruby
SQL
Scala
TypeScript
Visual Basic

Integrations

Alibaba Cloud
C
C++
CSS
Elixir
F#
GaiaNet
HTML
Kotlin
LM-Kit.NET
ModelScope
Python
Qwen Studio
QwenCloud
R
Ruby
SQL
Scala
TypeScript
Visual Basic
Claim Qwen-7B and update features and information
Claim Qwen-7B and update features and information
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Claim TabFM and update features and information