title: Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI
summary: We introduce Shakti VLM, a family of vision-language models in the capacity
  of 1B and 4B parameters designed to address data efficiency challenges in
  multimodal learning. While recent VLMs achieve strong performance through
  extensive training data, Shakti models leverage architectural innovations to
  attain competitive results with fewer tokens. Key advancements include
  QK-Normalization for attention stability, hybrid normalization techniques, and
  enhanced positional encoding. A three-stage training strategy further optimizes
  learning efficiency. Evaluations show that Shakti-Shakti-VLM-1B and
  Shakti-VLM-4B excel in document understanding, Visual Reasoning, OCR
  extraction, and general multimodal reasoning. Our results highlight that high
  performance can be achieved through model design and training strategy rather
  than sheer data volume, making Shakti an efficient solution for
  enterprise-scale multimodal tasks.
paper_authors._id: 67bea8cc7e54112af6c372aa
paper_authors.name: Syed Abdul Gaffar Shakhadri
paper_authors.status: extracted_confirmed
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paper_authors.fullname: Syed Abdul Gaffar Shakhadri
paper_authors.type: user
paper_authors.user: SyedAbdul
paper_authors._id: 67bea8cc7e54112af6c372ab
paper_authors.name: Kruthika KR
paper_authors.status: extracted_pending
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paper_authors.fullname: Kruthika
paper_authors.type: user
paper_authors.user: kruthika
paper_authors._id: 67bea8cc7e54112af6c372ac
paper_authors.name: Kartik Basavaraj Angadi
paper_authors.status: extracted_pending
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paper_authors._id: 677cc34fe4cf361eedccd085
paper_authors.avatarUrl: /avatars/e97a3f9a84ed258ab4b75c12865562d6.svg
paper_authors.fullname: Kartik Basavaraj Angadi
paper_authors.type: user
paper_authors.user: KartikAngadi
paper_title: Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI
paper_summary: We introduce Shakti VLM, a family of vision-language models in the capacity
  of 1B and 4B parameters designed to address data efficiency challenges in
  multimodal learning. While recent VLMs achieve strong performance through
  extensive training data, Shakti models leverage architectural innovations to
  attain competitive results with fewer tokens. Key advancements include
  QK-Normalization for attention stability, hybrid normalization techniques, and
  enhanced positional encoding. A three-stage training strategy further optimizes
  learning efficiency. Evaluations show that Shakti-Shakti-VLM-1B and
  Shakti-VLM-4B excel in document understanding, Visual Reasoning, OCR
  extraction, and general multimodal reasoning. Our results highlight that high
  performance can be achieved through model design and training strategy rather
  than sheer data volume, making Shakti an efficient solution for
  enterprise-scale multimodal tasks.
paper_ai_summary: Shakti VLM models achieve competitive performance in multimodal learning with fewer tokens and data through architectural innovations like QK-Normalization and a three-stage training strategy.
paper_ai_keywords: QK-Normalization hybrid normalization techniques enhanced positional encoding three-stage training strategy document understanding Visual Reasoning OCR extraction general multimodal reasoning