ApX 标志

趋近智

Phi-4 Reasoning Plus

参数

14B

上下文长度

32.768K

模态

Text

架构

Dense

许可证

MIT

发布日期

-

知识截止

Mar 2025

技术规格

注意力结构

Multi-Head Attention

隐藏维度大小

-

层数

-

注意力头

-

键值头

-

激活函数

-

归一化

-

位置嵌入

Absolute Position Embedding

系统要求

不同量化方法和上下文大小的显存要求

Phi-4 Reasoning Plus

Microsoft's Phi-4 Reasoning Plus is a 14-billion parameter language model specifically optimized for complex reasoning tasks. It is an enhanced variant within the Phi-4 family, building upon the base Phi-4 model. The model's primary purpose is to address scenarios requiring long-chain reasoning, such as advanced mathematics, scientific inquiry, and code generation. Its development emphasizes delivering high-quality outputs even in computationally constrained environments.

The architecture of Phi-4 Reasoning Plus is a dense, decoder-only Transformer. It incorporates an extended context window of 32,000 tokens to facilitate processing of lengthy reasoning chains. The model utilizes rotary position embeddings to maintain coherence and track token positions effectively across extended sequences. Training involved supervised fine-tuning on a curated dataset of chain-of-thought traces, along with reinforcement learning to further enhance performance. This methodology focuses on high-quality synthetic and filtered organic data, ensuring proficiency in complex problem-solving.

Phi-4 Reasoning Plus demonstrates an increased latency compared to its counterpart, Phi-4 Reasoning, due to its generation of approximately 50% more tokens for more detailed responses. This characteristic makes it particularly suitable for high-accuracy tasks where comprehensive reasoning is paramount. The model is designed to operate efficiently on consumer-grade hardware, including mobile devices, tablets, and desktops, thereby expanding its accessibility for various AI applications.

关于 Phi-4

The Microsoft Phi-4 model family comprises small language models prioritizing efficient, high-capability reasoning. Its development emphasizes robust data quality and sophisticated synthetic data integration. This approach enables enhanced performance and on-device deployment capabilities.


其他 Phi-4 模型

评估基准

排名适用于本地LLM。

排名

#17

基准分数排名

Graduate-Level QA

GPQA

0.69

5

Professional Knowledge

MMLU Pro

0.76

6

0.61

9

General Knowledge

MMLU

0.69

10

0.58

12

0.63

13

Agentic Coding

LiveBench Agentic

0.05

14

0.55

15

排名

排名

#17

编程排名

#19

GPU 要求

完整计算器

选择模型权重的量化方法

上下文大小:1024 个令牌

1k
16k
32k

所需显存:

推荐 GPU

Phi-4 Reasoning Plus: Specifications and GPU VRAM Requirements