Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI
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.
67bea8cc7e54112af6c372aa
Syed Abdul Gaffar Shakhadri
extracted_confirmed
2025-02-26T05:52:19.355Z
63d9e09f1cae35c27bf80cb2
https://cdn-avatars.huggingface.co/v1/production/uploads/1675223055197-noauth.jpeg
Syed Abdul Gaffar Shakhadri
user
SyedAbdul
67bea8cc7e54112af6c372ab
Kruthika KR
extracted_pending
2025-02-26T05:38:21.529Z
5fb7ae48e6ae537272bdeb3c
/avatars/e5d01cb428f4b22161e0d17895a5c678.svg
Kruthika
user
kruthika
67bea8cc7e54112af6c372ac
Kartik Basavaraj Angadi
extracted_pending
2025-02-26T05:38:21.529Z
677cc34fe4cf361eedccd085
/avatars/e97a3f9a84ed258ab4b75c12865562d6.svg
Kartik Basavaraj Angadi
user
KartikAngadi
Shakti-VLMs: Scalable Vision-Language Models for Enterprise AI
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.
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.
QK-Normalization hybrid normalization techniques enhanced positional encoding three-stage training strategy document understanding Visual Reasoning OCR extraction general multimodal reasoning