Government Of India
A-
A
A+
Phi-4 - Advanced Language Model for Reasoning and AI Research
A 14B-parameter AI model designed for high-quality reasoning, instruction following, and general AI applications, trained on a blend of synthetic and public domain datasets.
About Model
Phi-4 is a state-of-the-art language model developed by Microsoft Research, designed for advanced reasoning, logic, and generative AI applications. Built on a blend of synthetic, public domain, and high-quality academic datasets, it ensures efficient performance in instruction following, reasoning, and problem-solving. The model is optimized for memory-efficient environments while maintaining a 16K token context length.
Phi-4 - Advanced Language Model for Reasoning and AI Research
Metadata
MIT
Microsoft
Text Generation
N.A.
Open
Sector Agnostic
12/03/25 06:35:17
0
Activity Overview
0
7 
0 
184
Tags
- Small Language Model
- Complex Reasoning
- Mathematics
- Text Generation
- Instruction Following
- Transformers
- NLP
- Microsoft
License Control
MIT
More Models from Microsoft Corporation (India) Pvt. Ltd.
Phi-1.5 - Lightweight Transformer Model for Text Generation and Coding
A 1.3B parameter Transformer model trained on structured QA, NLP tasks, and Python code, optimized for text generation, summarization, and creative writing, with no fine-tuning from human feedback.
Transformers
Code Generation
Instruction Following
Reasoning
NLP
Microsoft
- 0
- 23
- 0
1,394
Updated 1 day(s) ago
Phi-3-Mini-4K-Instruct - Lightweight AI Model for Reasoning and Efficient AI Applications
A 3.8B parameter AI model optimized for memory-efficient reasoning, instruction following, and long-context processing, designed for compute-constrained environments and latency-sensitive applications.
Text Generation
NLP
Microsoft
Transformers
Instruction Following
Reasoning
- 0
- 6
- 0
- 142
Updated 1 day(s) ago
Phi-4-Mini-Instruct - Lightweight Multilingual AI Model
A lightweight AI model optimized for multilingual text generation, reasoning, and instruction adherence, designed for memory-efficient applications and generative AI tasks.
Math
Instruction Following
Function Calling
Coding
Transformers
Microsoft
NLP
Text Generation
Reasoning
- 0
- 10
- 0
- 290
Updated 1 day(s) ago
TAPEX: Large SQL Execution Model (Table Pre-training via Learning a Neural SQL Executor)
A large-sized TAPEX model pre-trained to simulate neural SQL execution, enabling the execution of SQL queries on given tables.
TAPEX
Transformers
DataRetrieval
SQLExecution
NeuralExecutor
PreTrainedModel
BART
- 0
- 6
- 0
- 249
Updated 11 month(s) ago
TAPEX: Large Model (Table Pre-training via Learning a Neural SQL Executor)
A large-sized pre-trained model designed to enhance table-based question answering and fact verification tasks.
TableQuestionAnswering
PreTrainedModel
FactVerification
LargeModel
BART
- 0
- 5
- 0
- 194
Updated 11 month(s) ago
TAPEX: TabFact Data enabled Large Finetuned (Table Pre-training via Learning a Neural SQL Executor) Model
A large-sized TAPEX model fine-tuned on the TabFact dataset, designed to enhance performance in table-based fact verification tasks.
Transformers
BART
NaturalLanguageProcessing
FineTunedModel
TAPEX
FactVerification
DataValidation
TabFact
- 0
- 6
- 0
- 161
Updated 11 month(s) ago
TAPEX (Table Pre-training via Learning a Neural SQL Executor) Large Finetuned Model
A large-sized TAPEX model fine-tuned on the WikiTableQuestions dataset, designed to enhance performance in table-based question answering tasks.
Transformers
BART
NaturalLanguageProcessing
FineTunedModel
TAPEX
DataExtraction
TableQuestionAnswering
WikiTableQuestions
- 0
- 2
- 0
- 230
Updated 11 month(s) ago
TAPEX: Base Model (Table Pre-training via Learning a Neural SQL Executor)
A base-sized pre-trained model designed to enhance table-based question answering and fact verification tasks.
TableQuestionAnswering
TabularData
FactVerification
PreTrainedModel
BART
- 0
- 3
- 0
- 167
Updated 11 month(s) ago
TAPEX: WikiTable Questions Data enabled Base Finetuned (Table Pre-training via Learning a Neural SQL Executor) Model
A base-sized TAPEX model fine-tuned on the WikiTableQuestions dataset, designed to enhance performance in table-based question answering tasks.
WikiTableQuestions
Transformers
BART
NaturalLanguageProcessing
FineTunedModel
TAPEX
DataExtraction
TableQuestionAnswering
- 0
- 3
- 0
- 181
Updated 11 month(s) ago
TAPEX: WikiSQL Data enabled Base Finetuned (Table Pre-training via Learning a Neural SQL Executor) Model
A large-sized TAPEX model fine-tuned on the WikiSQL dataset, optimized for translating natural language questions into SQL queries for effective table-based question answering.
DataRetrieval
Transformers
BART
NaturalLanguageProcessing
FineTunedModel
TAPEX
WikiSQL
SQLQueryGeneration
- 0
- 5
- 0
- 179
Updated 11 month(s) ago
© 2026 - Copyright AIKosh. All rights reserved.