Showing 11 open source projects for "drift"

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  • 1
    Alibi Detect

    Alibi Detect

    Algorithms for outlier, adversarial and drift detection

    Alibi Detect is an open source Python library focused on outlier, adversarial and drift detection. The package aims to cover both online and offline detectors for tabular data, text, images and time series. Both TensorFlow and PyTorch backends are supported for drift detection.
    Downloads: 0 This Week
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  • 2
    WhisperX

    WhisperX

    Automatic Speech Recognition with Word-level Timestamps

    ...The system enables batched inference, significantly increasing transcription speed while maintaining high accuracy. It is particularly effective for long recordings, where traditional approaches may suffer from drift, repetition, or misalignment. whisperx also supports speaker diarization, allowing identification of different speakers within a conversation. Its architecture combines multiple components to enhance both performance and usability in real-world transcription tasks. Overall, whisperx provides a more robust and scalable solution for high-quality speech-to-text applications.
    Downloads: 78 This Week
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  • 3
    NannyML

    NannyML

    Detecting silent model failure. NannyML estimates performance

    NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data science, empowering data scientist to quickly understand and automatically detect silent model failure. ...
    Downloads: 0 This Week
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  • 4
    Arize Phoenix

    Arize Phoenix

    Uncover insights, surface problems, monitor, and fine tune your LLM

    Phoenix provides ML insights at lightning speed with zero-config observability for model drift, performance, and data quality. Phoenix is an Open Source ML Observability library designed for the Notebook. The toolset is designed to ingest model inference data for LLMs, CV, NLP and tabular datasets. It allows Data Scientists to quickly visualize their model data, monitor performance, track down issues & insights, and easily export to improve.
    Downloads: 0 This Week
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  • 5
    Paper2Slides

    Paper2Slides

    From Paper to Presentation in One Click

    ...It uses an extraction approach intended to capture critical insights comprehensively, including important visuals and data points that often get missed in naive summarization. A major focus is traceability: generated slide content is designed to remain linked back to the source material so you can verify accuracy and reduce information drift. It also offers styling flexibility, letting you use built-in themes or describe a custom design direction in natural language for themed outputs.
    Downloads: 2 This Week
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  • 6
    Agent Control

    Agent Control

    Centralized agent control plane for governing runtime agent behavior

    ...The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where organizations need observability, enforcement, and governance around autonomous or semi-autonomous AI systems. The repository includes SDKs, a server, telemetry components, examples, and integrations for common agent frameworks. It is especially useful for teams building customer-facing, internal, or enterprise agents that need scalable runtime guardrails.
    Downloads: 19 This Week
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  • 7
    LongCat-Video

    LongCat-Video

    Foundational video generation model with 13.6B parameters

    ...A unified architecture handles text-to-video, image-to-video, and video-continuation tasks without separate models. It is pretrained for continuation, allowing it to create minutes-long sequences while limiting color drift and quality loss. A coarse-to-fine strategy operates across time and space to produce 720p video at 30 frames per second efficiently. Block Sparse Attention reduces high-resolution inference costs, while multi-reward GRPO training improves visual quality and prompt alignment. The repository includes inference scripts for single- and multi-GPU execution, model-download instructions, and interactive generation examples. ...
    Downloads: 19 This Week
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  • 8
    InfiniteYou

    InfiniteYou

    Flexible Photo Recrafting While Preserving Your Identity

    InfiniteYou is an open-source image-generation and “identity-preserving image editing / generation” framework from ByteDance, designed to generate high-fidelity images that preserve a subject’s identity while allowing flexible editing or re-creation according to textual prompts. Using an architecture built around diffusion transformers (DiTs), InfiniteYou introduces a component called InfuseNet that injects identity features derived from reference images into the generation process — via...
    Downloads: 0 This Week
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  • 9
    remotepower

    remotepower

    A lightweight control plane for Linux servers

    A lightweight control plane for Linux servers. No database, no cloud, no nonsense. One agent per host — everything else lives in flat JSON. v3.2.1 ships SNMP polling for agentless devices, MCP write tools for AI hosts, OIDC SSO, an actionable alerts inbox, and inbound webhooks + syslog.
    Downloads: 20 This Week
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  • 10
    Transformers4Rec

    Transformers4Rec

    Transformers4Rec is a flexible and efficient library

    ...Generally, the next user interaction is related to the sequence of the user's previous choices. In some cases, it might be a repeated purchase or song play. User interests can also suffer from interest drift because preferences can change over time. Those challenges are addressed by the sequential recommendation task.
    Downloads: 0 This Week
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  • 11
    llmverify-npm

    llmverify-npm

    You need a safety layer between your LLM and your users.

    ...Local-first verification, safety monitoring, and guardrails for any LLM 100% Local | No Telemetry | Privacy-First | 500 free calls/day Links Product Page: subodhkc.com/products/llmverify npm: npmjs.com/package/llmverify GitHub: github.com/subodhkc/llmverify-npm Python Package (PyPI): github.com/subodhkc/llmverify Author: Subodh KC — AI governance, compliance, and security leader Feature One-liner What it catches Verify output await verify(aiResponse) Hallucinations, harmful content, quality issues Block injections isInputSafe(userMessage) Prompt injection, jailbreaks, malicious input Redact PII redactPII(text) Emails, phones, SSNs, credit cards, API keys Fix broken JSON repairJSON(broken) Missing brackets, trailing commas, unquoted keys Monitor health monitorLLM(client) Latency spikes, token drift, behavioral changes Sentinel tests sentinel.quick(client, model) LLM regressions b
    Downloads: 0 This Week
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