[Submitted on 10 Jul 2026]
Title:Index SLM Technical Report
View a PDF of the paper titled Index SLM Technical Report, by Lusheng Zhang and 7 other authors
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Abstract:We present Index-1.9B, a series of open small language models developed at Bilibili. The series comprises four models: Index-1.9B-Base, a foundation model with 1.9 billion non-embedding parameters pre-trained on 2.8 trillion predominantly Chinese and English tokens; Index-1.9B-Pure, a control variant trained with an identical recipe but with all instruction-like data strictly filtered from the corpus; Index-1.9B-Chat, aligned from the base model with supervised fine-tuning and direct preference optimization; and Index-1.9B-Character, which augments the chat model with retrieval-augmented generation for few-shot role-playing customization. Pre-training employs a Warmup-Stable-Decay learning-rate schedule in which the concentration of curated data is raised substantially during the decay phase, together with a Norm-Head output layer that stabilizes training under large learning rates. On a suite of standard benchmarks covering examination, reasoning, mathematics, and code, Index-1.9B-Base attains an average score of 64.92, competitive with or exceeding open models of several times its size. We further report controlled studies on model depth, learning-rate magnitude and scheduling, the interaction between learning-rate decay and data quality, and the effect of including instruction data during pre-training, and we document an unexplained surge in benchmark performance midway through the constant-learning-rate phase. All models, together with evaluation code, are released at this https URL.
Comments: 26 pages, 10 figures
Subjects:
Computation and Language (cs.CL)
Cite as: arXiv:2607.09885 [cs.CL]
(or arXiv:2607.09885v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2607.09885
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Tianjiao Li [view email] [v1] Fri, 10 Jul 2026 18:19:47 UTC (1,284 KB)
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