ApX 标志

趋近智

Hunyuan T1

参数

70B

上下文长度

32K

模态

Text

架构

Dense

许可证

-

发布日期

22 Aug 2025

知识截止

-

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

Hunyuan T1

Tencent Hunyuan T1 represents a sophisticated large-scale reasoning model, engineered for tasks demanding profound analytical and logical capabilities. Positioned as a core component within the Tencent Hunyuan model series, it is primarily designed to facilitate complex problem-solving across various domains. This model integrates a hybrid architectural paradigm to achieve its enhanced reasoning proficiency and operational efficiency.

The underlying architecture of Hunyuan T1 is characterized by a Hybrid-Transformer-Mamba Mixture of Experts (MoE) configuration. This design synergistically combines the robust contextual processing of Transformer blocks with the high speed and memory efficiency of Mamba state-space models. The MoE framework further refines computational allocation, enabling the model to dynamically activate 52 billion parameters across 16 expert networks based on input complexity. This adaptive mechanism, built upon the TurboS fast-thinking base, is specifically optimized for efficient long-sequence processing, mitigating issues such as context loss in extended textual inputs.

Operationally, Hunyuan T1 demonstrates enhanced inference capabilities and offers an accelerated decoding speed, reportedly twice as fast as comparable models under equivalent deployment conditions. Its proficiency in handling extended context lengths, up to 256,000 tokens, supports intricate long-form reasoning. The model is developed for enterprise applications requiring precise logical reasoning, scientific analysis, code generation, and advanced problem-solving, making it suitable for scenarios demanding structured logic and consistent long-form output.

关于 Hunyuan

Tencent Hunyuan large language models with various capabilities.


其他 Hunyuan 模型

评估基准

排名适用于本地LLM。

没有可用的 Hunyuan T1 评估基准。

排名

排名

-

编程排名

-

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
31k

所需显存:

推荐 GPU