TeleChat Technical Report
In this technical report, we present TeleChat, a collection of large language
models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It
includes pretrained language models as well as fine-tuned chat models that is
aligned with human preferences. TeleChat is initially pretrained on an
extensive corpus containing a diverse collection of texts from both English and
Chinese languages, including trillions of tokens. Subsequently, the model
undergoes fine-tuning to align with human preferences, following a detailed
methodology that we describe. We evaluate the performance of TeleChat on
various tasks, including language understanding, mathematics, reasoning, code
generation, and knowledge-based question answering. Our findings indicate that
TeleChat achieves comparable performance to other open-source models of similar
size across a wide range of public benchmarks. To support future research and
applications utilizing LLMs, we release the fine-tuned model checkpoints of
TeleChat's 7B and 12B variant, along with code and a portion of our pretraining
data, to the public community.
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Zihan Wang
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2025-07-25T08:40:52.188Z
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Zihan Wang
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Xinzhang Liu
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Shixuan Liu
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2025-07-29T07:13:55.441Z
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Shixuan Liu
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Yitong Yao
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Yuyao Huang
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Zhongjiang He
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Xuelong Li
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Yongxiang Li
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Zhonghao Che
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Zhaoxi Zhang
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Yan Wang
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Xin Wang
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Luwen Pu
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Huihan Xu
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Ruiyu Fang
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Yu Zhao
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Jie Zhang
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Xiaomeng Huang
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Zhilong Lu
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Jiaxin Peng
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Wenjun Zheng
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Shiquan Wang
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Bingkai Yang
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Xuewei he
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Zhuoru Jiang
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Qiyi Xie
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Yanhan Zhang
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Zhongqiu Li
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Lingling Shi
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Weiwei Fu
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Yin Zhang
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Zilu Huang
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Sishi Xiong
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Yuxiang Zhang
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2024-12-05T10:20:45.386Z
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Yuxiang Zhang
user
TokerZ
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Chao Wang
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Shuangyong Song
TeleChat Technical Report
In this technical report, we present TeleChat, a collection of large language
models (LLMs) with parameters of 3 billion, 7 billion and 12 billion. It
includes pretrained language models as well as fine-tuned chat models that is
aligned with human preferences. TeleChat is initially pretrained on an
extensive corpus containing a diverse collection of texts from both English and
Chinese languages, including trillions of tokens. Subsequently, the model
undergoes fine-tuning to align with human preferences, following a detailed
methodology that we describe. We evaluate the performance of TeleChat on
various tasks, including language understanding, mathematics, reasoning, code
generation, and knowledge-based question answering. Our findings indicate that
TeleChat achieves comparable performance to other open-source models of similar
size across a wide range of public benchmarks. To support future research and
applications utilizing LLMs, we release the fine-tuned model checkpoints of
TeleChat's 7B and 12B variant, along with code and a portion of our pretraining
data, to the public community.
TeleChat, a series of large language models with varying parameter sizes, is pretrained on multilingual data and fine-tuned for human preference alignment, achieving comparable performance across multiple benchmarks.
large language models LLMs parameters pretrained language models fine-tuned chat models multilingual data human preferences language understanding mathematics reasoning code generation knowledge-based question answering model checkpoints pretraining data