Showing 7 open source projects for "self code"

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  • 1
    DINOv2

    DINOv2

    PyTorch code and models for the DINOv2 self-supervised learning

    ...The repository includes code for training, evaluating, and feature extraction, with utilities to run k-NN or linear evaluation baselines to assess representation quality. Pretrained checkpoints cover multiple model sizes so practitioners can trade accuracy for speed and memory depending on their deployment constraints.
    Downloads: 4 This Week
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  • 2
    Claude Relay Service

    Claude Relay Service

    Claude Code image, a one-stop open source transit service

    claude-relay-service is an open-source proxy and relay platform that enables unified access to multiple AI model providers through a single self-hosted gateway. The project is designed to help users centralize subscriptions and API usage for services such as Claude, OpenAI, Gemini, and related tools. It acts as a middleware layer that forwards requests while managing authentication, routing, and cost-sharing scenarios. The system is particularly useful for teams or communities that want to...
    Downloads: 0 This Week
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  • 3
    Antigravity Claude Proxy

    Antigravity Claude Proxy

    Proxy that exposes Antigravity provided claude / gemini models

    Antigravity Claude Proxy is a purpose-built proxy server that enables developers to interface with Claude models through a standardized RESTful API, allowing tools and workflows that expect generic HTTP APIs to operate on Anthropic’s Claude without native support. The project acts as a translation layer, receiving web requests in common formats (such as OpenAI-style endpoints) and forwarding them to Anthropic’s API in the required structure, while converting responses back into a familiar...
    Downloads: 1 This Week
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  • 4
    ChatGPT Clone

    ChatGPT Clone

    ChatGPT interface with better UI

    ChatGPT Clone demonstrates a ChatGPT-style conversational interface wired to large-language-model backends, packaged so developers can self-host and extend. The goal is to replicate the core chat UX—message history, streaming tokens, code blocks, and system prompts—while letting you plug in different provider APIs or local models. It showcases a clean separation between the web client and the message orchestration layer so you can experiment with prompts, roles, and memory strategies. ...
    Downloads: 16 This Week
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  • 5
    ConvNeXt V2

    ConvNeXt V2

    Code release for ConvNeXt V2 model

    ...The result is a convnet that competes strongly with transformer architectures on recognition benchmarks while being efficient and hardware-friendly. The repository provides official PyTorch implementations for multiple model sizes (Atto, Femto, Pico, up through Huge), conversion from JAX weights, code for pretraining/fine-tuning, and pretrained checkpoints. It supports both self-supervised pretraining and supervised fine-tuning.
    Downloads: 0 This Week
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  • 6
    Multi-Agent Emergence Environments

    Multi-Agent Emergence Environments

    Environment generation code for the paper "Emergent Tool Use"

    ...It was designed for the experiments described in the paper and blog post “Emergent Tool Use from Multi-Agent Autocurricula”, which investigated how complex cooperative and competitive behaviors can evolve through self-play. The repository provides environment generation code that builds on the mujoco-worldgen package, enabling dynamic creation of simulated physical environments. Developers can construct custom environments by combining modular components such as Boxes, Ramps, and RandomWalls using a flexible layering approach that reduces code duplication. ...
    Downloads: 0 This Week
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  • 7
    wav2vec2-large-xlsr-53-portuguese

    wav2vec2-large-xlsr-53-portuguese

    Portuguese ASR model fine-tuned on XLSR-53 for 16kHz audio input

    wav2vec2-large-xlsr-53-portuguese is an automatic speech recognition (ASR) model fine-tuned on Portuguese using the Common Voice 6.1 dataset. It is based on Facebook’s wav2vec2-large-xlsr-53, a multilingual self-supervised learning model, and is optimized to transcribe Portuguese speech sampled at 16kHz. The model performs well without a language model, though adding one can improve word error rate (WER) and character error rate (CER). It achieves a WER of 11.3% (or 9.01% with LM) on Common...
    Downloads: 0 This Week
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