TeleChat Technical Report
arxiv(2024)
摘要
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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