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Keye-VL-1.5-8B Open Source: Slow-Fast and 128k Context, Reshaping Video Multimodal AI Toolchain

Keye-VL-1.5-8B Open Source: Slow-Fast and 128k Context, Reshaping Video Multimodal AI Toolchain

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Keye-VL-1.5-8B Open Source: Slow-Fast video encoding and 128k context, bringing multimodal AI tools into the era of long videos

This is a large artificial intelligence model for video understanding. Keye-VL-1.5-8B supports 128k contextual, thinking and non-thinking reasoning modes through Slow-Fast video encoding, LongCoT cold start data pipeline and reinforcement learning alignment, and achieves high-quality understanding in multiple image and video scenarios, making it suitable for intelligence and automation of content production, retrieval, and interactive applications.


1. Positioning and highlights

1. Model positioning: video-first multimodal large model

AI tool Keye-VL-1.5-8B focuses on long video and cross-frame inference, and the artificial intelligence reasoning chain can be unified modeling between images, videos and text, supporting large context and multi-image input. Meet the large-scale application of content stations and search stations.

2. Key technologies: Slow-Fast + Long Context + Alignment Enhancement

Slow-Fast video encoding takes the high-resolution channel in the drastically changing frame, and pursues time-domain coverage in the fast channel in the static clip. Expand the context to 128k with step-by-step pre-training; and then reinforcement learning and human preference alignment to improve explainability and stability.

(1) Thinking mode and multimodal input

Provide two modes, thinking and non-thinking, which can not only deepen chain reasoning, but also pursue low latency in real-time applications. Visual tokens can be flexibly configured to cover multiple image and video inputs.

(2) Engineering-friendly and ecologically compatible

Natively adapted to vLLM and swift and other inference ecosystems, which is convenient for rapid launch and elastic scaling. It supports both offline and online deployment modes, and is suitable for enterprise A/B evaluation and grayscale publishing.


2. Landing route

1. Content and search: three steps to form a reusable assembly line

AI tools connect data cleaning, subtitle extraction and lens segmentation; The main model completes video Q&A, fact extraction and multi-image retrieval. Finally, the quality estimation and human review are closed to form a stable output.

2. Agent collaboration: ChatGPT+Claude+Keye

uses ChatGPT to generate task plans and prompts, Claude does security and style review, and Keye executives long video understanding and multimodal answers, automating artificial intelligence from planning to execution.

(1) Deployment checklist

a. Select vLLM inference and KV cache

b. Enable Slow-Fast parameters and multi-graph cap

c. Establish a termbase and retrieval enhancement

d. Configure a dual-track strategy between thinking and non-thinking

e. Access log monitoring and quality regression


3. Performance, compatibility and licensing

1. Stable performance of long videos and multiple benchmarks

The

large model performs well in long context and video understanding tasks, taking into account general multi-modal capabilities, and is suitable for multi-level scenarios from short video Q&A to long program analysis.

2. Inference and ecology

AI

tools natively support batch parallelism and prefix caching, which can significantly increase throughput when combined with automated orchestration. Smoothly connect with the existing data annotation and evaluation framework.

(1) Open source license

The model is released under an open source license, which is convenient for scientific research and enterprise customization; It is recommended to combine corporate compliance and privacy policies to complete secondary alignment and distillation compression.


4. Risks and boundaries

1. Cost and stability of ultra-long content

Ultra-long contexts will bring about memory and latency fluctuations, which can reduce costs through non-thinking mode and segmented summarization.

2. Data and compliance

When

it comes to user videos, they need to be desensitized and minimized. Create audit logs and use case blacklists to reduce the risk of misjudgment.


5. Address

item address:https://github.com/Kwai-Keye/Keye

try here:https://huggingface.co/spaces/Kwai-Ke ye/Keye-VL-1_5-8B

thesis:https://

Keye-VL-1.5-8B is open source Keye-VL-1.5-8B video comprehension Keye-VL-1.5-8B long video Keye-VL-1.5-8B 128k context Keye-VL-1.5-8B Slow-Fast encoding Keye-VL-1.5-8B LongCoT data pipeline Keye-VL-1.5-8B Reinforcement Learning Alignment Keye-VL-1.5-8B Thinking Pattern Keye-VL-1.5-8B Non-Thinking Mode Keye-VL-1.5-8B Multimodal AI Keye-VL-1.5-8B Multiple Image Input Keye-VL-1.5-8B Video Q&A Keye-VL-1.5-8B Cross-frame inference Keye-VL-1.5-8B high-quality understanding Keye-VL-1.5-8B Content Production Keye-VL-1.5-8B video retrieval Keye-VL-1.5-8B interactive application Keye-VL-1.5-8B vLLM inference Keye-VL-1.5-8B swift compatible Keye-VL-1.5-8B KV cache Keye-VL-1.5-8B batch parallel Keye-VL-1.5-8B is engineering-friendly Keye-VL-1.5-8B is deployed offline online Keye-VL-1.5-8B A/B review Keye-VL-1.5-8B grayscale released Keye-VL-1.5-8B data cleaning Keye-VL-1.5-8B Subtitle extraction Keye-VL-1.5-8B lens segmentation Keye-VL-1.5-8B Fact extraction Keye-VL-1.5-8B Retrieval Enhancements Keye-VL-1.5-8B Agent collaboration Keye-VL-1.5-8B ChatGPT linkage Keye-VL-1.5-8B Reviewed by Claude Keye-VL-1.5-8B automated assembly line Keye-VL-1.5-8B Long Context Advantage Keye-VL-1.5-8B visual token configuration Keye-VL-1.5-8B Interpretability Improvement Keye-VL-1.5-8B stability optimization Keye-VL-1.5-8B throughput and latency Keye-VL-1.5-8B termbase access Keye-VL-1.5-8B mass estimation Keye-VL-1.5-8B Closed loop for human review Keye-VL-1.5-8B Privacy & Compliance Keye-VL-1.5-8B log monitoring Keye-VL-1.5-8B mass regression Keye-VL-1.5-8B Distillation & Compression Keye-VL-1.5-8B enterprise landed Keye-VL-1.5-8B Content Station Search Station Keye-VL-1.5-8B Long Program Resolution Keye-VL-1.5-8B multimodal retrieval

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