{"schema_version":"onlylabs.public_analysis_evidence.v1","title":"Replicate analysis evidence pack","description":"Public onlylabs evidence pack for cited agent analysis: captured pages, ranked public signals, and stored web-search provenance used by the background analysis workflow.","url":"https://onlylabs.fyi/labs/replicate","json_url":"https://onlylabs.fyi/analysis/replicate/evidence.json","generated_at":"2026-06-11T16:31:33.095Z","org":{"slug":"replicate","name":"Replicate","category":"neocloud","category_label":"Neocloud","dossier_url":"https://onlylabs.fyi/labs/replicate"},"analysis":null,"workflow":{"version":"onlylabs-deepagents-analysis-v3","provider":null,"model":null,"agent":null,"public_pack_mode":"local-pages-and-events","live_web_fetches":false,"note":"Public evidence exports do not trigger live Exa calls; stored Exa provenance is included when analysis metadata contains it."},"stats":{"pages":28,"events":140,"web":0,"evidence":88,"signal_desks":{"hiring":0,"forks":6,"releases":12,"talking":12,"repos":30},"data_radar_lanes":null,"data_radar_matches":null,"stored_analysis_evidence":null,"stored_analysis_web":null,"stored_analysis_signal_desks":null,"stored_analysis_data_radar_lanes":null,"stored_analysis_data_radar_matches":null},"stored_web_provenance":null,"evidence":[{"ref":"P1","kind":"page","title":"replicate/nydus-snapshotter repository metadata","date":"2026-06-11T04:19:18.240111+00:00","date_source":null,"source_url":"https://github.com/replicate/nydus-snapshotter","signal_url":null,"signal_json_url":null,"text":"# replicate/nydus-snapshotter\n\nDescription: A containerd snapshotter with data deduplication and lazy loading in P2P fashion\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-07-24T17:52:34Z\n\nPushed: 2023-08-10T13:52:01Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: containerd/nydus-snapshotter\n\nArchived: no\n\nREADME:\n[**[⬇️ Download]**](https://github.com/containerd/nydus-snapshotter/releases)\n[**[📖 Website]**](https://nydus.dev/)\n[**[☸ Quick Start (Kubernetes)**]](https://github.com/containerd/nydus-snapshotter/blob/main/docs/run_nydus_in_kubernetes.md)\n[**[🤓 Quick Start (nerdctl)**]](https://github.com/containerd/nerdctl/blob/master/docs/nydus.md)\n[**[❓ FAQs & Troubleshooting]**](https://github.com/dragonflyoss/image-service/wiki/FAQ)\n\n# Nydus Snapshotter\n\n<p><img src=\"https://github.com/dragonflyoss/image-service/blob/master/misc/logo.svg\" width=\"170\"></p>\n\n[![Release Version](https://img.shields.io/github/v/release/containerd/nydus-snapshotter?style=flat)](https://github.com/containerd/nydus-snapshotter/releases)\n[![LICENSE](https://img.shields.io/github/license/containerd/nydus-snapshotter.svg?style=flat)](https://github.com/containerd/nydus-snapshotter/blob/main/LICENSE)\n![CI](https://github.com/containerd/nydus-snapshotter/actions/workflows/ci.yml/badge.svg?event=push)\n[![Go Report Card](https://goreportcard.com/badge/github.com/containerd/nydus-snapshotter?style=flat)](https://goreportcard.com/report/github.com/containerd/nydus-snapshotter)\n[![Twitter](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2Fdragonfly_oss)](https://twitter.com/dragonfly_oss)\n[![Nydus Stars](https://img.shields.io/github/stars/dragonflyoss/image-service?label=Nydus%20Stars&style=social)](https://github.com/dragonflyoss/image-service)\n\nNydus-snapshotter is a **non-core** sub-project of containerd.\n\nNydus snapshotter is an external plugin of containerd for [Nydus image service](https://nydus.dev) which implements a chunk-based content-addressable filesystem on top of a called `RAFS (Registry Acceleration File System)` format that improves the current OCI image specification, in terms of container launching "},{"ref":"P2","kind":"page","title":"replicate/image-service repository metadata","date":"2026-06-11T04:19:18.170086+00:00","date_source":null,"source_url":"https://github.com/replicate/image-service","signal_url":null,"signal_json_url":null,"text":"# replicate/image-service\n\nDescription: Nydus - the Dragonfly image service, providing fast, secure and easy access to container images.\n\nLanguage: Rust\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-08-02T15:17:13Z\n\nPushed: 2023-08-08T12:39:37Z\n\nDefault branch: stable/v2.2-our-patches\n\nFork: yes\n\nParent repository: dragonflyoss/nydus\n\nArchived: no\n\nREADME:\n# Nydus: Dragonfly Container Image Service\n\n<p><img src=\"misc/logo.svg\" width=\"170\"></p>\n\n[![Release Version](https://img.shields.io/github/v/release/dragonflyoss/image-service?style=flat)](https://github.com/dragonflyoss/image-service/releases)\n[![License](https://img.shields.io/crates/l/nydus-rs)](https://crates.io/crates/nydus-rs)\n\n[![Smoke Test](https://github.com/dragonflyoss/image-service/actions/workflows/smoke.yml/badge.svg?event=schedule)](https://github.com/dragonflyoss/image-service/actions/workflows/ci.yml)\n[![Image Conversion](https://github.com/dragonflyoss/image-service/actions/workflows/convert.yml/badge.svg?event=schedule)](https://github.com/dragonflyoss/image-service/actions/workflows/convert.yml)\n[![Release Test Daily](https://github.com/dragonflyoss/image-service/actions/workflows/release.yml/badge.svg?event=schedule)](https://github.com/dragonflyoss/image-service/actions/workflows/release.yml)\n[![Twitter](https://img.shields.io/twitter/url?style=social&url=https%3A%2F%2Ftwitter.com%2Fdragonfly_oss)](https://twitter.com/dragonfly_oss)\n[![Nydus Stars](https://img.shields.io/github/stars/dragonflyoss/image-service?label=Nydus%20Stars&style=social)](https://github.com/dragonflyoss/image-service)\n\n## Introduction\nThe nydus project implements a content-addressable filesystem on top of a RAFS format that improves the current OCI image specification, in terms of container launching speed, image space, and network bandwidth efficiency, as well as data integrity.\n\nThe following benchmarking result shows the performance improvement compared with the OCI image for the container cold startup elapsed time on containerd. As the OCI image size increases, the container startup time of using Nydus image remains very short.\n\n![Container Cold Startup](./misc/perf.jpg)\n\nNydus' key fe"},{"ref":"P3","kind":"page","title":"replicate/musicgen-chord repository metadata","date":"2026-06-11T04:19:17.548547+00:00","date_source":null,"source_url":"https://github.com/replicate/musicgen-chord","signal_url":null,"signal_json_url":null,"text":"# replicate/musicgen-chord\n\nDescription: MusicGen conditioned with chord progression.\n\nLanguage: Jupyter Notebook\n\nLicense: MIT\n\nStars: 11\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-09-03T09:41:04Z\n\nPushed: 2023-10-07T17:13:39Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: facebookresearch/audiocraft\n\nArchived: no\n\nREADME:\n# AudioCraft\n![docs badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_docs/badge.svg)\n![linter badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_linter/badge.svg)\n![tests badge](https://github.com/facebookresearch/audiocraft/workflows/audiocraft_tests/badge.svg)\n\nAudioCraft is a PyTorch library for deep learning research on audio generation. AudioCraft contains inference and training code\nfor two state-of-the-art AI generative models producing high-quality audio: AudioGen and MusicGen.\n\n## Installation\nAudioCraft requires Python 3.9, PyTorch 2.0.0. To install AudioCraft, you can run the following:\n\n```shell\n# Best to make sure you have torch installed first, in particular before installing xformers.\n# Don't run this if you already have PyTorch installed.\npip install 'torch>=2.0'\n# Then proceed to one of the following\npip install -U audiocraft # stable release\npip install -U git+https://git@github.com/facebookresearch/audiocraft#egg=audiocraft # bleeding edge\npip install -e . # or if you cloned the repo locally (mandatory if you want to train).\n```\n\nWe also recommend having `ffmpeg` installed, either through your system or Anaconda:\n```bash\nsudo apt-get install ffmpeg\n# Or if you are using Anaconda or Miniconda\nconda install \"ffmpeg<5\" -c conda-forge\n```\n\n## Models\n\nAt the moment, AudioCraft contains the training code and inference code for:\n* [MusicGen](./docs/MUSICGEN.md): A state-of-the-art controllable text-to-music model.\n* [AudioGen](./docs/AUDIOGEN.md): A state-of-the-art text-to-sound model.\n* [EnCodec](./docs/ENCODEC.md): A state-of-the-art high fidelity neural audio codec.\n* [Multi Band Diffusion](./docs/MBD.md): An EnCodec compatible decoder using diffusion.\n\n## Training code\n\nAudioCraft contains PyTorch components for deep learning research in audio and training pipelines for th"},{"ref":"P4","kind":"page","title":"replicate/cog-lcm repository metadata","date":"2026-06-11T04:19:17.347412+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-lcm","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-lcm\n\nLicense: MIT\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-11-16T23:20:00Z\n\nPushed: 2024-01-13T23:50:19Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: fofr/cog-lcm\n\nArchived: no\n\nREADME:\n# latent-consistency-model"},{"ref":"P5","kind":"page","title":"replicate/gpt-fast repository metadata","date":"2026-06-11T04:19:17.319149+00:00","date_source":null,"source_url":"https://github.com/replicate/gpt-fast","signal_url":null,"signal_json_url":null,"text":"# replicate/gpt-fast\n\nDescription: Simple and efficient pytorch-native transformer text generation in <1000 LOC of python.\n\nLicense: BSD-3-Clause\n\nStars: 0\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-12-13T21:56:55Z\n\nPushed: 2023-12-13T10:31:54Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: meta-pytorch/gpt-fast\n\nArchived: no\n\nREADME:\n# gpt-fast\nSimple and efficient pytorch-native transformer text generation.\n\nFeaturing:\n1. Very low latency\n2. <1000 lines of python\n3. No dependencies other than PyTorch and sentencepiece\n4. int8/int4 quantization\n5. Speculative decoding\n6. Tensor parallelism\n7. Supports Nvidia and AMD GPUs\n\nThis is *NOT* intended to be a \"framework\" or \"library\" - it is intended to show off what kind of performance you can get with native PyTorch :) Please copy-paste and fork as you desire.\n\nFor an in-depth walkthrough of what's in this codebase, see this [blog post](https://pytorch.org/blog/accelerating-generative-ai-2/).\n\n## Installation\n[Download PyTorch nightly](https://pytorch.org/get-started/locally/)\nInstall sentencepiece and huggingface_hub\n```bash\npip install sentencepiece huggingface_hub\n```\n\nTo download llama models, go to https://huggingface.co/meta-llama/Llama-2-7b and go through steps to obtain access.\nThen login with `huggingface-cli login`\n\n## Downloading Weights\nModels tested/supported\n```text\nopenlm-research/open_llama_7b\nmeta-llama/Llama-2-7b-chat-hf\nmeta-llama/Llama-2-13b-chat-hf\nmeta-llama/Llama-2-70b-chat-hf\ncodellama/CodeLlama-7b-Python-hf\ncodellama/CodeLlama-34b-Python-hf\n```\n\nFor example, to convert Llama-2-7b-chat-hf\n```bash\nexport MODEL_REPO=meta-llama/Llama-2-7b-chat-hf\n./scripts/prepare.sh $MODEL_REPO\n```\n\n## Benchmarks\nBenchmarks run on an A100-80GB, power limited to 330W.\n\n| Model | Technique | Tokens/Second | Memory Bandwidth (GB/s) |\n| -------- | ------- | ------ | ------ |\n| Llama-2-7B | Base | 104.9 | 1397.31 |\n| | 8-bit | 155.58 | 1069.20 |\n| | 4-bit (G=32) | 196.80 | 862.69 |\n| Llama-2-70B | Base | OOM ||\n| | 8-bit | 19.13 | 1322.58 |\n| | 4-bit (G=32) | 25.25 | 1097.66 |\n\n### Speculative Sampling\n[Verifier: Llama-70B (int4), Draft: Llama-7B (int4)](./scripts/speculate_70B_int4.sh): 48.4 tok/s\n\n### Tensor P"},{"ref":"P6","kind":"page","title":"replicate/vllm-with-loras repository metadata","date":"2026-06-11T04:19:17.317491+00:00","date_source":null,"source_url":"https://github.com/replicate/vllm-with-loras","signal_url":null,"signal_json_url":null,"text":"# replicate/vllm-with-loras\n\nDescription: A high-throughput and memory-efficient inference and serving engine for LLMs\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 6\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-09-07T16:04:08Z\n\nPushed: 2023-11-17T01:35:39Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: vllm-project/vllm\n\nArchived: no\n\nREADME:\n<p align=\"center\">\n<picture>\n<source media=\"(prefers-color-scheme: dark)\" srcset=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-dark.png\">\n<img alt=\"vLLM\" src=\"https://raw.githubusercontent.com/vllm-project/vllm/main/docs/source/assets/logos/vllm-logo-text-light.png\" width=55%>\n</picture>\n</p>\n\n<h3 align=\"center\">\nEasy, fast, and cheap LLM serving for everyone\n</h3>\n\n<p align=\"center\">\n| <a href=\"https://vllm.readthedocs.io/en/latest/\"><b>Documentation</b></a> | <a href=\"https://vllm.ai\"><b>Blog</b></a> | <a href=\"https://arxiv.org/abs/2309.06180\"><b>Paper</b></a> | <a href=\"https://discord.gg/jz7wjKhh6g\"><b>Discord</b></a> |\n\n</p>\n\n---\n\n**The First vLLM Bay Area Meetup (Oct 5th 6pm-8pm PT)**\n\nWe are excited to invite you to the first vLLM meetup!\nThe vLLM team will share recent updates and roadmap.\nWe will also have vLLM users and contributors coming up to the stage to share their experiences.\nPlease register [here](https://lu.ma/first-vllm-meetup) and join us!\n\n---\n\n*Latest News* 🔥\n- [2023/09] We created our [Discord server](https://discord.gg/jz7wjKhh6g)! Join us to discuss vLLM and LLM serving! We will also post the latest announcements and updates there.\n- [2023/09] We released our [PagedAttention paper](https://arxiv.org/abs/2309.06180) on arXiv!\n- [2023/08] We would like to express our sincere gratitude to [Andreessen Horowitz](https://a16z.com/2023/08/30/supporting-the-open-source-ai-community/) (a16z) for providing a generous grant to support the open-source development and research of vLLM.\n- [2023/07] Added support for LLaMA-2! You can run and serve 7B/13B/70B LLaMA-2s on vLLM with a single command!\n- [2023/06] Serving vLLM On any Cloud with SkyPilot. Check out a 1-click [example](https://github.com/skypilot-org/skypilot/blob/master/llm/vllm) to start the vL"},{"ref":"P7","kind":"page","title":"replicate/getting-started-nextjs-typescript repository metadata","date":"2026-06-11T04:19:17.266742+00:00","date_source":null,"source_url":"https://github.com/replicate/getting-started-nextjs-typescript","signal_url":null,"signal_json_url":null,"text":"# replicate/getting-started-nextjs-typescript\n\nDescription: A Next.js + Typescript starter app using Replicate\n\nLanguage: TypeScript\n\nLicense: Apache-2.0\n\nStars: 31\n\nForks: 12\n\nOpen issues: 0\n\nCreated: 2023-12-03T14:32:57Z\n\nPushed: 2023-12-03T16:05:47Z\n\nDefault branch: main\n\nFork: yes\n\nParent repository: replicate/getting-started-nextjs\n\nArchived: no\n\nREADME:\n## Getting started with Next.js and Replicate\n\nThis branch uses Typescript, Tailwind and the Next.js App Router\n\nThis is a [Next.js](https://nextjs.org/) template project that's preconfigured to work with Replicate's API.\n\nYou can use this as a quick jumping-off point to build a web app using Replicate's API, or you can recreate this codebase from scratch by following the guide at [replicate.com/docs/get-started/nextjs](https://replicate.com/docs/get-started/nextjs)\n\n## Noteworthy files\n\n- [src/app/page.tsx](src/app/page.tsx) - The React frontend that renders the home page in the browser\n- [src/app/api/predictions/route.ts](src/app/api/predictions/route.ts) - The backend API endpoint that calls Replicate's API to create a prediction\n- [src/app/api/predictions/[id]/route.ts](src/app/api/predictions/[id]/route.ts) - The backend API endpoint that calls Replicate's API to get the prediction result\n\n## Usage\n\nGet a copy of this repo:\n```console\nnpx create-next-app --example https://github.com/replicate/getting-started-nextjs-typescript your-project-name\ncd your-project-name\n```\n\nInstall dependencies:\n\n```console\nnpm install\n```\n\nAdd your [Replicate API token](https://replicate.com/account#token) to `.env.local`:\n\n```\nREPLICATE_API_TOKEN=<your-token-here>\n```\n\nRun the development server:\n\n```console\nnpm run dev\n```\n\nOpen [http://localhost:3000](http://localhost:3000) with your browser.\n\nFor detailed instructions on how to create and use this template, see [replicate.com/docs/get-started/nextjs](https://replicate.com/docs/get-started/nextjs)\n\n<img width=\"698\" alt=\"iguana\" src=\"https://github.com/replicate/getting-started-nextjs-typescript/assets/14337872/f40cf84f-f309-44d5-8429-9a1cda911d6d\">"},{"ref":"P8","kind":"page","title":"replicate/GFPGAN repository metadata","date":"2026-06-11T04:19:16.564996+00:00","date_source":null,"source_url":"https://github.com/replicate/GFPGAN","signal_url":null,"signal_json_url":null,"text":"# replicate/GFPGAN\n\nDescription: Patches for GFPGAN\n\nLanguage: Python\n\nLicense: NOASSERTION\n\nStars: 13\n\nForks: 25\n\nOpen issues: 1\n\nCreated: 2024-03-14T00:33:02Z\n\nPushed: 2024-04-02T16:39:32Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: TencentARC/GFPGAN\n\nArchived: no\n\nREADME:\n<p align=\"center\">\n<img src=\"assets/gfpgan_logo.png\" height=130>\n</p>\n\n## <div align=\"center\"><b><a href=\"README.md\">English</a> | <a href=\"README_CN.md\">简体中文</a></b></div>\n\n<div align=\"center\">\n<!-- <a href=\"https://twitter.com/_Xintao_\" style=\"text-decoration:none;\">\n<img src=\"https://user-images.githubusercontent.com/17445847/187162058-c764ced6-952f-404b-ac85-ba95cce18e7b.png\" width=\"4%\" alt=\"\" />\n</a> -->\n\n[![download](https://img.shields.io/github/downloads/TencentARC/GFPGAN/total.svg)](https://github.com/TencentARC/GFPGAN/releases)\n[![PyPI](https://img.shields.io/pypi/v/gfpgan)](https://pypi.org/project/gfpgan/)\n[![Open issue](https://img.shields.io/github/issues/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues)\n[![Closed issue](https://img.shields.io/github/issues-closed/TencentARC/GFPGAN)](https://github.com/TencentARC/GFPGAN/issues)\n[![LICENSE](https://img.shields.io/badge/License-Apache%202.0-blue.svg)](https://github.com/TencentARC/GFPGAN/blob/master/LICENSE)\n[![python lint](https://github.com/TencentARC/GFPGAN/actions/workflows/pylint.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/pylint.yml)\n[![Publish-pip](https://github.com/TencentARC/GFPGAN/actions/workflows/publish-pip.yml/badge.svg)](https://github.com/TencentARC/GFPGAN/blob/master/.github/workflows/publish-pip.yml)\n</div>\n\n1. :boom: **Updated** online demo: [![Replicate](https://img.shields.io/static/v1?label=Demo&message=Replicate&color=blue)](https://replicate.com/tencentarc/gfpgan). Here is the [backup](https://replicate.com/xinntao/gfpgan).\n1. :boom: **Updated** online demo: [![Huggingface Gradio](https://img.shields.io/static/v1?label=Demo&message=Huggingface%20Gradio&color=orange)](https://huggingface.co/spaces/Xintao/GFPGAN)\n1. [Colab Demo](https://colab.research.google.com/drive/1sVsoBd9AjckIXThgtZhGrHRfFI6UUYOo) for GFPGAN <a href=\"https://colab.researc"},{"ref":"P9","kind":"page","title":"replicate/sse repository metadata","date":"2026-06-11T04:19:15.876247+00:00","date_source":null,"source_url":"https://github.com/replicate/sse","signal_url":null,"signal_json_url":null,"text":"# replicate/sse\n\nDescription: Server Sent Events server and client for Golang\n\nLicense: MPL-2.0\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2024-03-14T09:44:29Z\n\nPushed: 2024-03-14T09:51:47Z\n\nDefault branch: master\n\nFork: yes\n\nParent repository: r3labs/sse\n\nArchived: no\n\nREADME:\n# SSE - Server Sent Events Client/Server Library for Go\n\n## Synopsis\n\nSSE is a client/server implementation for Server Sent Events for Golang.\n\n## Build status\n\n* Master: [![CircleCI Master](https://circleci.com/gh/r3labs/sse.svg?style=svg)](https://circleci.com/gh/r3labs/sse)\n\n## Quick start\n\nTo install:\n```\ngo get github.com/r3labs/sse/v2\n```\n\nTo Test:\n\n```sh\n$ make deps\n$ make test\n```\n\n#### Example Server\n\nThere are two parts of the server. It is comprised of the message scheduler and a http handler function.\nThe messaging system is started when running:\n\n```go\nfunc main() {\nserver := sse.New()\n}\n```\n\nTo add a stream to this handler:\n\n```go\nfunc main() {\nserver := sse.New()\nserver.CreateStream(\"messages\")\n}\n```\n\nThis creates a new stream inside of the scheduler. Seeing as there are no consumers, publishing a message to this channel will do nothing.\nClients can connect to this stream once the http handler is started by specifying _stream_ as a url parameter, like so:\n\n```\nhttp://server/events?stream=messages\n```\n\nIn order to start the http server:\n\n```go\nfunc main() {\nserver := sse.New()\n\n// Create a new Mux and set the handler\nmux := http.NewServeMux()\nmux.HandleFunc(\"/events\", server.ServeHTTP)\n\nhttp.ListenAndServe(\":8080\", mux)\n}\n```\n\nTo publish messages to a stream:\n\n```go\nfunc main() {\nserver := sse.New()\n\n// Publish a payload to the stream\nserver.Publish(\"messages\", &sse.Event{\nData: []byte(\"ping\"),\n})\n}\n```\n\nPlease note there must be a stream with the name you specify and there must be subscribers to that stream\n\nA way to detect disconnected clients:\n\n```go\nfunc main() {\nserver := sse.New()\n\nmux := http.NewServeMux()\nmux.HandleFunc(\"/events\", func(w http.ResponseWriter, r *http.Request) {\ngo func() {\n// Received Browser Disconnection\n<-r.Context().Done()\nprintln(\"The client is disconnected here\")\nreturn\n}()\n\nserver.ServeHTTP(w, r)\n})\n\nhttp.ListenAndServe(\":8080\", mux)\n}\n```\n\n"},{"ref":"P10","kind":"page","title":"replicate/cog-vllm repository metadata","date":"2026-06-11T04:09:52.156386+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-vllm","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-vllm\n\nDescription: Run LLMs on Replicate with vLLM\n\nLanguage: Python\n\nStars: 27\n\nForks: 5\n\nOpen issues: 3\n\nCreated: 2023-07-20T21:09:07Z\n\nPushed: 2025-07-19T12:17:58Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-vLLM: Run vLLM on Replicate\n\n[Cog](https://github.com/replicate/cog) \nis an open-source tool that lets you package machine learning models\nin a standard, production-ready container. \nvLLM is a fast and easy-to-use library for LLM inference and serving.\n\nYou can deploy your packaged model to your own infrastructure, \nor to [Replicate].\n\n## Highlights\n\n* 🚀 **Run vLLM in the cloud with an API**.\nDeploy any [vLLM-supported language model] at scale on Replicate.\n\n* 🏭 **Support multiple concurrent requests**.\nContinuous batching works out of the box.\n\n* 🐢 **Open Source, all the way down**.\nLook inside, take it apart, make it do exactly what you need.\n\n## Quickstart\n\nGo to [replicate.com/replicate/vllm](https://replicate.com/replicate/vllm)\nand create a new vLLM model from a [supported Hugging Face repo][vLLM-supported language model],\nsuch as [google/gemma-2b](https://huggingface.co/google/gemma-2b)\n\n> [!IMPORTANT] \n> Gated models require a [Hugging Face API token](https://huggingface.co/settings/tokens),\n> which you can set in the `hf_token` field of the model creation form.\n\n<img width=\"1055\" alt=\"Create a new vLLM model on Replicate\" src=\"https://github.com/replicate/cog-vllm/assets/7659/a8f31837-0ed3-40f7-974c-d0a16ae48350\">\n\nReplicate downloads the model files, packages them into a `.tar` archive,\nand pushes a new version of your model that's ready to use.\n\n<img width=\"1322\" alt=\"Trained vLLM model on Replicate\" src=\"https://github.com/replicate/cog-vllm/assets/7659/ebb84e12-9173-4fb0-8749-7293a105cf13\">\n\nFrom here, you can either use your model as-is,\nor customize it and push up your changes.\n\n## Local Development\n\nIf you're on a machine or VM with a GPU,\nyou can try out changes before pushing them to Replicate.\n\nStart by [installing or upgrading Cog](https://cog.run/#install).\nYou'll need Cog [v0.10.0-alpha11](https://github.com/replicate/cog/releases/tag/v0.10.0-alpha11):\n\n```console\n$ sudo curl -o /usr/local/bin/cog "},{"ref":"P11","kind":"page","title":"replicate/cog-sdxl repository metadata","date":"2026-06-11T04:09:51.864504+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-sdxl","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-sdxl\n\nDescription: Stable Diffusion XL training and inference as a cog model\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 234\n\nForks: 105\n\nOpen issues: 34\n\nCreated: 2023-08-01T18:50:46Z\n\nPushed: 2024-11-08T22:25:25Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-SDXL\n\n[![Replicate demo and cloud API](https://replicate.com/stability-ai/sdxl/badge)](https://replicate.com/stability-ai/sdxl)\n\nThis is an implementation of Stability AI's [SDXL](https://github.com/Stability-AI/generative-models) as a [Cog](https://github.com/replicate/cog) model.\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of SDXL to [Replicate](https://replicate.com).\n\n## Basic Usage\n\nfor prediction,\n\n```bash\ncog predict -i prompt=\"a photo of TOK\"\n```\n\n```bash\ncog train -i input_images=@example_datasets/__data.zip -i use_face_detection_instead=True\n```\n\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## Update notes\n\n**2023-08-17**\n* ROI problem is fixed.\n* Now BLIP caption_prefix does not interfere with BLIP captioner.\n\n**2023-08-12**\n* Input types are inferred from input name extensions, or from the `input_images_filetype` argument\n* Preprocssing are now done with fp16, and if no mask is found, the model will use the whole image\n\n**2023-08-11**\n* Default to 768x768 resolution training\n* Rank as argument now, default to 32\n* Now uses Swin2SR `caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr` as default, and will upscale + downscale to 768x768"},{"ref":"P12","kind":"page","title":"replicate/homebrew-tap repository metadata","date":"2026-06-11T04:09:51.619707+00:00","date_source":null,"source_url":"https://github.com/replicate/homebrew-tap","signal_url":null,"signal_json_url":null,"text":"# replicate/homebrew-tap\n\nLanguage: Ruby\n\nStars: 1\n\nForks: 1\n\nOpen issues: 2\n\nCreated: 2023-08-11T09:53:31Z\n\nPushed: 2026-05-19T23:03:16Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Replicate Tap\n\n## How do I install these formulae?\n\n`brew install replicate/tap/<formula>`\n\nOr `brew tap replicate/tap` and then `brew install <formula>`.\n\n## Documentation\n\n`brew help`, `man brew` or check [Homebrew's documentation](https://docs.brew.sh)."},{"ref":"P13","kind":"page","title":"replicate/cli repository metadata","date":"2026-06-11T04:09:51.617615+00:00","date_source":null,"source_url":"https://github.com/replicate/cli","signal_url":null,"signal_json_url":null,"text":"# replicate/cli\n\nDescription: CLI for Replicate\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 94\n\nForks: 12\n\nOpen issues: 10\n\nCreated: 2023-08-11T12:07:07Z\n\nPushed: 2024-09-16T18:01:48Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Replicate CLI\n\n![demo](demo.gif)\n\n## Install\n\nIf you're using macOS, you can install the Replicate CLI using Homebrew:\n\n```console\nbrew tap replicate/tap\nbrew install replicate\n```\n\nOr you can build from source and install it with these commands\n(requires Go 1.20 or later):\n\n```console\nmake\nsudo make install\n```\n\n## Upgrade\n\nIf you previously installed the CLI with Homebrew,\nyou can upgrade to the latest version by running the following command:\n\n```console\nbrew upgrade replicate\n```\n\n## Usage\n\nGrab your API token from [replicate.com/account](https://replicate.com/account)\nand set the `REPLICATE_API_TOKEN` environment variable.\n\n```console\n$ export REPLICATE_API_TOKEN=<your token here>\n```\n\n---\n\n```console\nUsage:\nreplicate [command]\n\nCore commands:\nhardware Interact with hardware\nmodel Interact with models\nprediction Interact with predictions\nscaffold Create a new local development environment from a prediction\ntraining Interact with trainings\n\nAlias commands:\nrun Alias for \"prediction create\"\nstream Alias for \"prediction create --stream\"\ntrain Alias for \"training create\"\n\nAdditional Commands:\ncompletion Generate the autocompletion script for the specified shell\nhelp Help about any command\n\nFlags:\n-h, --help help for replicate\n-v, --version version for replicate\n\nUse \"replicate [command] --help\" for more information about a command.```\n```\n\n---\n\n### Create a prediction\n\nGenerate an image with [SDXL].\n\n```console\n$ replicate run stability-ai/sdxl \\\nprompt=\"a studio photo of a rainbow colored corgi\"\nPrediction created: https://replicate.com/p/jpgp263bdekvxileu2ppsy46v4\n```\n\n### Stream prediction output\n\nRun [LLaMA 2] and stream output tokens to your terminal.\n\n```console\n$ replicate run meta/llama-2-70b-chat --stream \\\nprompt=\"Tell me a joke about llamas\"\nSure, here's a joke about llamas for you:\n\nWhy did the llama refuse to play poker?\n\nBecause he always got fleeced!\n```\n\n### Create a local development environment from a predic"},{"ref":"P14","kind":"page","title":"replicate/cog-sdxl-lora repository metadata","date":"2026-06-11T04:09:51.616559+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-sdxl-lora","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-sdxl-lora\n\nLanguage: Python\n\nStars: 23\n\nForks: 8\n\nOpen issues: 3\n\nCreated: 2023-08-20T16:21:20Z\n\nPushed: 2023-08-20T16:34:44Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-SDXL\n\n[![Replicate demo and cloud API](https://replicate.com/stability-ai/sdxl/badge)](https://replicate.com/stability-ai/sdxl)\n\nThis is an implementation of the [SDXL](https://github.com/Stability-AI/generative-models) as a Cog model. [Cog packages machine learning models as standard containers](https://github.com/replicate/cog).\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of SDXL to [Replicate](https://replicate.com).\n\n## Basic Usage\n\nfor prediction,\n\n```bash\ncog predict -i prompt=\"a photo of TOK\"\n```\n\n```bash\ncog train -i input_images=@example_datasets/__data.zip -i use_face_detection_instead=True\n```\n\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## Update notes\n\n**2023-08-17**\n* ROI problem is fixed.\n* Now BLIP caption_prefix does not interfere with BLIP captioner.\n* Now lora_url can be used to accept lora models from the web.\n\n**2023-08-12**\n* Input types are inferred from input name extensions, or from the `input_images_filetype` argument\n* Preprocssing are now done with fp16, and if no mask is found, the model will use the whole image\n\n**2023-08-11**\n* Default to 768x768 resolution training\n* Rank as argument now, default to 32\n* Now uses Swin2SR `caidas/swin2SR-realworld-sr-x4-64-bsrgan-psnr` as default, and will upscale + downscale to 768x768"},{"ref":"P15","kind":"page","title":"replicate/cog-lightweight-openpose repository metadata","date":"2026-06-11T04:09:51.350047+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-lightweight-openpose","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-lightweight-openpose\n\nDescription: Cog wrapper for Lightweight Openpose\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 3\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-09-08T16:02:33Z\n\nPushed: 2023-09-17T13:24:43Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-Lightweight-OpenPose\nCog wrapper for [Lightweight OpenPose](https://arxiv.org/abs/1811.12004). This is an implementation of the original work's [GitHub repository](https://github.com/Daniil-Osokin/lightweight-human-pose-estimation.pytorch), see Replicate [model page](https://replicate.com/alaradirik/lightweight-openpose) for the API and demo.\n\n## Basic Usage\n\nTo run a prediction:\n\n```bash\ncog predict -i image=@sample.png -i image_size=256\n```\n\nTo start your own server:\n\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## References\n```\n@inproceedings{osokin2018lightweight_openpose,\nauthor={Osokin, Daniil},\ntitle={Real-time 2D Multi-Person Pose Estimation on CPU: Lightweight OpenPose},\nbooktitle = {arXiv preprint arXiv:1811.12004},\nyear = {2018}\n}\n```"},{"ref":"P16","kind":"page","title":"replicate/cog-t2i-adapter-sdxl repository metadata","date":"2026-06-11T04:09:51.082544+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-t2i-adapter-sdxl","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-t2i-adapter-sdxl\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 18\n\nForks: 10\n\nOpen issues: 0\n\nCreated: 2023-09-14T12:19:38Z\n\nPushed: 2023-10-20T10:47:30Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-T2I-Adapter-SDXL\n\n[![Replicate demo and cloud API](https://replicate.com/stability-ai/sdxl/badge)](https://replicate.com/stability-ai/sdxl)\n\nThis is an implementation of TencentARC and the diffuser team's [T2I-Adapter-SDXL](https://github.com/TencentARC/T2I-Adapter) as a [Cog](https://github.com/replicate/cog) model.\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of T2I-Adapter-SDXL to [Replicate](https://replicate.com).\n\n## Basic Usage\n\nTo run a prediction:\n\n```bash\ncog predict -i prompt=\"Ice dragon roar, 4k photo\" -i adapter_name=\"lineart\"\n```\n\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## References\n```\n@article{mou2023t2i,\ntitle={T2i-adapter: Learning adapters to dig out more controllable ability for text-to-image diffusion models},\nauthor={Mou, Chong and Wang, Xintao and Xie, Liangbin and Wu, Yanze and Zhang, Jian and Qi, Zhongang and Shan, Ying and Qie, Xiaohu},\njournal={arXiv preprint arXiv:2302.08453},\nyear={2023}\n}\n```"},{"ref":"P17","kind":"page","title":"replicate/cog-codellama-cpp repository metadata","date":"2026-06-11T04:09:50.80973+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-codellama-cpp","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-codellama-cpp\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 2\n\nForks: 0\n\nOpen issues: 3\n\nCreated: 2023-09-28T16:38:51Z\n\nPushed: 2024-01-30T18:56:41Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-CodeLlama-cpp\n\n[![Run on Replicate](https://replicate.com/meta/codellama-34b-instruct/badge)](https://replicate.com/meta/codellama-34b-instruct\n)\n\nCog predictors for CodeLlama powered by llama.cpp."},{"ref":"P18","kind":"page","title":"replicate/dreambooth-batch repository metadata","date":"2026-06-11T04:09:50.808668+00:00","date_source":null,"source_url":"https://github.com/replicate/dreambooth-batch","signal_url":null,"signal_json_url":null,"text":"# replicate/dreambooth-batch\n\nDescription: batch inference (image generation) from dreambooth trainings\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-09-22T20:37:38Z\n\nPushed: 2023-09-23T00:19:18Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Stable Diffusion Dreambooth model template\n\nThis is the template used by [replicate.com dreambooth api](https://replicate.com/blog/dreambooth-api/) to build custom dreambooth models.\n\nThis template is based on [cog-stable-diffusion](https://github.com/replicate/cog-stable-diffusion) which uses diffusers.\n\n## Usage\n\nThis template is primarily intended for use by [Replicate's DreamBooth API](https://replicate.com/blog/dreambooth-api), which is what you probably want to use to train and publish your own model.\n\nIf you really want to use this template locally, you can do so by following these steps:\n\n1. Generate some weights, put them in `weights/` (use our trainer or your own)\n2. Download NSFW safety_checker weights using `script/download-weights`\n3. Install [cog](https://github.com/replicate/cog) & docker\n4. Build `cog build`\n5. Predict `cog predict -i prompt=\"photo of zzz\" -i seed=42` ..."},{"ref":"P19","kind":"page","title":"replicate/all-the-public-replicate-models repository metadata","date":"2026-06-11T04:09:50.775087+00:00","date_source":null,"source_url":"https://github.com/replicate/all-the-public-replicate-models","signal_url":null,"signal_json_url":null,"text":"# replicate/all-the-public-replicate-models\n\nDescription: 📦 Metadata for all the public models on Replicate, bundled up into an npm package.\n\nLanguage: JavaScript\n\nStars: 48\n\nForks: 5\n\nOpen issues: 3\n\nCreated: 2023-10-04T04:08:12Z\n\nPushed: 2026-04-21T00:21:25Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# all-the-public-replicate-models\n\nMetadata for all[^1] the public models on Replicate, bundled up into an npm package.\n\nThis package also includes [historical daily run counts](#stats) for each model, which are updated daily.\n\n## Installation\n\n```sh\nnpm install all-the-public-replicate-models\n```\n\n## Usage (as a library)\n\nFull-bodied usage (all the metadata, ~17MB)\n\n```js\nimport models from 'all-the-public-replicate-models'\n\nconsole.log(models)\n```\n\nLite usage (just the basic metadata, ~375K):\n\n```js\nimport models from 'all-the-public-replicate-models/lite'\n\nconsole.log(models)\n```\n\nFind the top 10 models by run count:\n\n```js\nimport models from 'all-the-public-replicate-models'\nimport {chain} from 'lodash-es'\n\nconst mostRun = chain(models).orderBy('run_count', 'desc').take(10).value()\nconsole.log({mostRun})\n```\n\n## Stats\n\nThis package also includes historical daily run counts for each model, which are updated daily.\n\n```js\nimport stats from 'all-the-public-replicate-models/stats'\n\nconsole.log(stats[\"black-forest-labs/flux-schnell\"].slice(-5))\n\n/*\n[\n{ date: '2025-01-03', totalRuns: 176951005, dailyRuns: 1071498 },\n{ date: '2025-01-04', totalRuns: 178025758, dailyRuns: 1074753 },\n{ date: '2025-01-05', totalRuns: 179119496, dailyRuns: 1093738 },\n{ date: '2025-01-06', totalRuns: 180272877, dailyRuns: 1153381 },\n{ date: '2025-01-07', totalRuns: 181445133, dailyRuns: 1172256 }\n]\n*/\n```\n\nSee [example.js](example.js) for a code snippet that uses the stats.\n\n## Usage (as a CLI)\n\nThe CLI dumps the model metadata to standard output as a big JSON object:\n\n```command\n$ npx all-the-public-replicate-models\n```\n\nThe output will be:\n\n```\n[\n{...},\n{...},\n{...},\n]\n```\n\nYou can use [jq](https://stedolan.github.io/jq/) to filter the output. Here's an example that finds all the whisper models and sorts them by run count:\n\n```command\nnpx all-the-public-replicate-models | j"},{"ref":"P20","kind":"page","title":"replicate/yolo repository metadata","date":"2026-06-11T04:09:50.309661+00:00","date_source":null,"source_url":"https://github.com/replicate/yolo","signal_url":null,"signal_json_url":null,"text":"# replicate/yolo\n\nDescription: EXPERIMENT - exploring ideas to improve dx for models\n\nLanguage: Go\n\nLicense: Apache-2.0\n\nStars: 12\n\nForks: 0\n\nOpen issues: 6\n\nCreated: 2023-10-04T11:21:20Z\n\nPushed: 2024-06-25T13:54:33Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# yolo\n\nAn experimental CLI for tweaking existing [Cog](https://github.com/replicate/cog) models and deploying them to Replicate really fast.\n\n- No Docker required\n- No Cog required\n- No GPU required\n\n## DISCLAIMER\n\n⚠️ This is a tool for power users that has many rough edges. ⚠️\n\n## Usage\n\n### Install on mac/linux\n\nFetch the precompiled Go binary from GitHub Releases:\n\nsudo curl -o /usr/local/bin/yolo -L \"https://github.com/replicate/yolo/releases/latest/download/yolo_$(uname -s)_$(uname -m)\"\nsudo chmod +x /usr/local/bin/yolo\n\nAlternatively can build from source (Golang required):\n\ngo build && sudo cp yolo /usr/local/bin\n\n### Get your Replicate token\n\nYou can use **either** your REPLICATE_API_TOKEN or your COG_TOKEN.\n\nBy using your REPLICATE_API_TOKEN, we can access the API and PUSH models to your account.\n\n#### Replicate API Token\n\nVisit https://replicate.com/account/api-tokens and copy your token.\n\nexport REPLICATE_API_TOKEN=r8_...\n\n#### Cog Token\n\nVisit https://replicate.com/auth/token and copy your token.\n\nexport COG_TOKEN=4b212....\n\n### Modify a model (e.g. SDXL)\n\nGrab the code by cloning the repo\n\ngit clone https://github.com/replicate/cog-sdxl.git\n\n### Find an existing version to modify\n\nVisit https://replicate.com/stability-ai/sdxl/api and find the docker image name:\n\nr8.im/stability-ai/sdxl@sha256:1bfb924045802467cf8869d96b231a12e6aa994abfe37e337c63a4e49a8c6c41\n\nThis is going to be your \"base\" for your tweaked model. You can think \nof the process as adding your changes on top of this model, as that is\nwhat happens under the hood. A new layer is added with whatever files\nyou specify.\n\n### Create a model\n\nThere is no Replicate API for model creation, so you must create a model on the website:\n\nhttps://replicate.com/create\n\n### Make and push your changes\n\nIf you are NOT changing the schema (inputs/outputs), you run this:\n\nyolo push \\\n--base r8.im/stability-ai/sdxl@sha256:1bfb924045802467c"},{"ref":"P21","kind":"page","title":"replicate/cog-inst-inpaint repository metadata","date":"2026-06-11T04:09:50.295849+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-inst-inpaint","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-inst-inpaint\n\nDescription: Cog wrapper for Inst-Inpaint\n\nLanguage: Python\n\nStars: 1\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-10-04T10:39:20Z\n\nPushed: 2023-10-04T10:49:32Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-Inst-Inpaint\n\nThis is an implementation of [Inst-Inpaint](https://github.com/abyildirim/inst-inpaint/) as a [Cog](https://github.com/replicate/cog) model. Inst-Inpaint is a diffusion based model that performs text-guided object removal from images. For more details, see this [Replicate model](https://replicate.com/alaradirik/inst-inpaint), [paper](https://arxiv.org/abs/2304.03246) and [project website](https://instinpaint.abyildirim.com/).\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your fork or other models to [Replicate](https://replicate.com).\n\n## Basic Usage\n\nTo run a prediction:\n\n```bash\ncog predict -i image=@cups.webp -i instruction=\"remove the cup on the left\"\n```\n\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## References\n```\n@misc{yildirim2023instinpaint,\ntitle={Inst-Inpaint: Instructing to Remove Objects with Diffusion Models}, \nauthor={Ahmet Burak Yildirim and Vedat Baday and Erkut Erdem and Aykut Erdem and Aysegul Dundar},\nyear={2023},\neprint={2304.03246},\narchivePrefix={arXiv},\nprimaryClass={cs.CV}\n}\n```"},{"ref":"P22","kind":"page","title":"replicate/blog-example-rag-chromadb-mistral7b repository metadata","date":"2026-06-11T04:09:50.048915+00:00","date_source":null,"source_url":"https://github.com/replicate/blog-example-rag-chromadb-mistral7b","signal_url":null,"signal_json_url":null,"text":"# replicate/blog-example-rag-chromadb-mistral7b\n\nLanguage: Python\n\nStars: 4\n\nForks: 0\n\nOpen issues: 1\n\nCreated: 2023-10-12T22:09:30Z\n\nPushed: 2023-10-17T15:54:44Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Retrieval Augmented Generation with Mistral-7b-Instruct and Chromadb\n\nThis git repo contains example scripts for building a small example retrieval augmented generation app.\n\nFor more information, check out the full blog post here: https://replicate.com/blog/how-to-use-rag-with-chromadb-and-mistral-7b-instruct"},{"ref":"P23","kind":"page","title":"replicate/latent-consistency-model repository metadata","date":"2026-06-11T04:09:49.977107+00:00","date_source":null,"source_url":"https://github.com/replicate/latent-consistency-model","signal_url":null,"signal_json_url":null,"text":"# replicate/latent-consistency-model\n\nDescription: Run Latent Consistency Models on your Mac\n\nLanguage: Python\n\nLicense: MIT\n\nStars: 196\n\nForks: 13\n\nOpen issues: 9\n\nCreated: 2023-10-24T10:45:42Z\n\nPushed: 2023-11-10T22:28:49Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Run latent consistency models on your Mac\n\nLatent consistency models (LCMs) are based on Stable Diffusion, but they can generate images much faster, needing only 4 to 8 steps for a good image (compared to 25 to 50 steps). [Simian Luo et al](https://arxiv.org/abs/2310.04378) released the first Stable Diffusion distilled model. It’s distilled from the Dreamshaper fine-tune by incorporating classifier-free guidance into the model’s input.\n\nYou can [run Latent Consistency Models in the cloud on Replicate](https://replicate.com/luosiallen/latent-consistency-model), but it's also possible to run it locally.\n\n## Prerequisites\n\nYou’ll need:\n\n- a Mac with an M1 or M2 chip\n- 16GB RAM or more\n- macOS 13.0 or higher\n- Python 3.10 or above\n\n## Install\n\nRun this to clone the repo:\n\ngit clone https://github.com/replicate/latent-consistency-model.git\ncd latent-consistency-model\n\nSet up a virtualenv to install the dependencies:\n\npython3 -m pip install virtualenv\npython3 -m virtualenv venv\n\nActivate the virtualenv:\n\nsource venv/bin/activate\n\n(You'll need to run this command again any time you want to run the script.)\n\nThen, install the dependencies:\n\npip install -r requirements.txt\n\n## Run\n\nThe script will automatically download the [`SimianLuo/LCM_Dreamshaper_v7`](https://huggingface.co/SimianLuo/LCM_Dreamshaper_v7) (3.44 GB) and [safety checker](https://huggingface.co/CompVis/stable-diffusion-safety-checker) (1.22 GB) models from HuggingFace.\n\n```sh\npython main.py \\\n\"a beautiful apple floating in outer space, like a planet\" \\\n--steps 4 --width 512 --height 512\n```\n\nYou’ll see an output like this:\n\n```sh\nOutput image saved to: output/out-20231026-144506.png\nUsing seed: 48404\n100%|███████████████████████████| 4/4 [00:00<00:00, 5.54it/s]\n```\n\n## Options\n\n| Parameter | Type | Default | Description |\n|---------------|-------|---------|---------------------------------------------------------------|\n| promp"},{"ref":"P24","kind":"page","title":"replicate/cog-musicgen-chord repository metadata","date":"2026-06-11T04:09:49.929618+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-musicgen-chord","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-musicgen-chord\n\nDescription: Chord conditioning implementation model of MusicGen\n\nLanguage: Python\n\nStars: 0\n\nForks: 1\n\nOpen issues: 1\n\nCreated: 2023-10-06T12:16:43Z\n\nPushed: 2023-10-09T09:51:36Z\n\nDefault branch: master\n\nFork: no\n\nArchived: no\n\nREADME: none published or not readable through the GitHub API."},{"ref":"P25","kind":"page","title":"replicate/cog-mvdream-multiview repository metadata","date":"2026-06-11T04:09:49.485259+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-mvdream-multiview","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-mvdream-multiview\n\nDescription: Cog wrapper for Multi-View Image Generation with MVDream\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 5\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-10-30T17:04:05Z\n\nPushed: 2023-10-30T17:25:52Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog MVDream Multi-View \nThis is an implementation of MVDream's text-to-multi-view image generation module as a [Cog](https://github.com/replicate/cog) model. See the [paper](https://arxiv.org/abs/2308.16512), [original repository](https://github.com/bytedance/MVDream) and this [Replicate model](https://replicate.com/adirik/mvdream-multi-view).\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of MVDream to [Replicate](https://replicate.com).\n\n## API Usage\nYou will need to have Cog and Docker installed on your local to run predictions. To use MVDream, simply describe the scene in natural language, and set stable diffusion generation parameters, camera elevation and/or azimuth angle span if you wish. The model will generate a consistent set of images from different views. API has the following inputs:\n\n- prompt: What you want to generate expressed in natural language\n- image_size: Width and height of the generated images. allowed values are 128, 256, 512, 1024. Note, larger is better, but slower.\n- num_frames: Number of views to generate.\n- num_inference_steps: Number of diffusion steps. Higher values will lead to better quality, but slower generation.\n- guidance_scale: How much to guide the generation process with the prompt. Higher values will lead to generation that is closer to the prompt, but less diverse or maybe of lower quality.\n- camera_elevation: Elevation angle of the camera.\n- camera_azimuth: Azimuth angle of the camera in the first view.\n- camera_azimuth_span: Total span of the azimuth angle. For example if the span is kept as 360 degrees and num_frames is set to 5 then in each view azimuth angle will be incremented by 360/5=72 degrees.\n- seed: Random seed for the generation process. If not specified, a random seed will be used.\n\nTo run a prediction:\n```bash\ncog predict -i prompt=\"an astron"},{"ref":"P26","kind":"page","title":"replicate/cog-owlvit repository metadata","date":"2026-06-11T04:09:49.483435+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-owlvit","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-owlvit\n\nDescription: Cog wrapper for OWL-ViT\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 0\n\nForks: 1\n\nOpen issues: 0\n\nCreated: 2023-10-25T12:54:07Z\n\nPushed: 2023-10-25T13:04:10Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-OWL-ViT\n\nThis is an implementation of Google's [OWL-ViT (v1)]([https://github.com/facebookresearch/nougat](https://github.com/google-research/scenic/tree/main/scenic/projects/owl_vit)) as a [Cog](https://github.com/replicate/cog) model. OWL-ViT uses a CLIP backbone to perform text-guided and open-vocabulary object detection. To use the model, simply input the image you'd like to query and enter the objects you would like to query as comma-separated text. For more details, see this [Replicate model](https://replicate.com/alaradirik/owlvit-base-patch32).\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of OWL-ViT to [Replicate](https://replicate.com).\n\n## Basic Usage\n\nTo run a prediction:\n```bash\ncog predict -i image=@data/astronaut.png -i query=\"human face, rocket, star-spangled banner, nasa badge\"\n```\n\nTo build the cog image and launch the API on your local:\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## References\n```\n@article{minderer2022simple,\ntitle={Simple Open-Vocabulary Object Detection with Vision Transformers},\nauthor={Matthias Minderer, Alexey Gritsenko, Austin Stone, Maxim Neumann, Dirk Weissenborn, Alexey Dosovitskiy, Aravindh Mahendran, Anurag Arnab, Mostafa Dehghani, Zhuoran Shen, Xiao Wang, Xiaohua Zhai, Thomas Kipf, Neil Houlsby},\njournal={ECCV},\nyear={2022},\n}\n```"},{"ref":"P27","kind":"page","title":"replicate/cog-grounding-dino repository metadata","date":"2026-06-11T04:09:48.336542+00:00","date_source":null,"source_url":"https://github.com/replicate/cog-grounding-dino","signal_url":null,"signal_json_url":null,"text":"# replicate/cog-grounding-dino\n\nDescription: Cog wrapper for Grounding DINO\n\nLanguage: Python\n\nLicense: Apache-2.0\n\nStars: 9\n\nForks: 5\n\nOpen issues: 0\n\nCreated: 2023-10-26T18:25:12Z\n\nPushed: 2023-10-26T18:47:52Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Cog-Grounding-DINO \nThis is an implementation of Grounding DINO by IDEA Reseach as a [Cog](https://github.com/replicate/cog) model. Grounding DINO can detect arbitrary objects with human text inputs such as category names or referring expressions. The model architecture combines Transformer-based detector DINO with grounded pre-training to achieve open-vocabulary / text-guided object detection. See the [paper](https://arxiv.org/abs/2303.05499), [original repository](https://github.com/IDEA-Research/GroundingDINO) and this [Replicate model](https://replicate.com/alaradirik/grounding-dino).\n\n## Development\n\nFollow the [model pushing guide](https://replicate.com/docs/guides/push-a-model) to push your own fork of Grounding DINO to [Replicate](https://replicate.com).\n\n## Basic Usage\nYou will need to have Cog and Docker installed on your local to run predictions. You can use Grounding DINO to query images with text descriptions of any object. To use it, simply upload an image and enter comma separated text descriptions of objects you want to query the image for. Expected input arguments are: \n\n- **image:** your input image\n- **query:** text queries describing objects you want to detect, separate queries with commas\n- **box_threshold:** chooses the boxes whose highest similarities are higher than a box_threshold\n- **text_threshold:** extracts the words whose similarities are higher than the text_threshold as predicted labels\n\nTo run a prediction:\n```bash\ncog predict -i image=@mugs.png -i query=\"a pink mug\" -i box_threshold=0.2 -i text_threshold=0.25 \n```\n\nTo build the cog image and launch the API on your local:\n```bash\ncog run -p 5000 python -m cog.server.http\n```\n\n## References \n```\n@article{liu2023grounding,\ntitle={Grounding dino: Marrying dino with grounded pre-training for open-set object detection},\nauthor={Liu, Shilong and Zeng, Zhaoyang and Ren, Tianhe and Li, Feng and Zhang, Hao and Yang, Jie and "},{"ref":"P28","kind":"page","title":"replicate/node-starter repository metadata","date":"2026-06-11T04:09:48.109312+00:00","date_source":null,"source_url":"https://github.com/replicate/node-starter","signal_url":null,"signal_json_url":null,"text":"# replicate/node-starter\n\nDescription: A starter project for running machine learning models in the cloud with Replicate.\n\nLanguage: JavaScript\n\nStars: 0\n\nForks: 0\n\nOpen issues: 0\n\nCreated: 2023-11-08T13:56:18Z\n\nPushed: 2023-11-22T06:30:01Z\n\nDefault branch: main\n\nFork: no\n\nArchived: no\n\nREADME:\n# Replicate Node Starter\n\nThis is a starter project for users who want to use the [Replicate JavaScript client](https://github.com/replicate/replicate-javascript) to run machine learning models in the cloud.\n\n## Prerequisites\n- You have installed Node.js version 18.\n- You have a Replicate API token.\n\n## Getting started\n1. Clone this repository.\n2. Install the dependencies with `npm install`.\n3. Add your Replicate API token by running `cp .env.example .env` and editing the `.env` file.\n4. Run the script with `npm run predict` or `node index.js`.\n\n## Contributing to This Project\nTo contribute to this project, follow these steps:\n\n- Fork this repository.\n- Create a branch: `git checkout -b <branch_name>`.\n- Make your changes and commit them: `git commit -m '<commit_message>'`\n- Push to the original branch: `git push origin <project_name>/<location>`\n- Create the pull request.\n\nAlternatively see the GitHub documentation on [creating a pull request](https://docs.github.com/en/pull-requests/collaborating-with-pull-requests/proposing-changes-to-your-work-with-pull-requests/creating-a-pull-request)."},{"ref":"E1","kind":"event","title":"Replicate is joining Cloudflare","date":"2025-11-17T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/replicate-cloudflare","signal_url":"https://onlylabs.fyi/signals/c9695709-bc0e-47f7-ba88-416669ca9ace","signal_json_url":"https://onlylabs.fyi/signals/c9695709-bc0e-47f7-ba88-416669ca9ace/signal.json","text":"post_published · Replicate is joining Cloudflare · signal_desk=talking · occurred_at=2025-11-17T00:00:00+00:00 · url=https://replicate.com/blog/replicate-cloudflare · hn=288 points/68 comments"},{"ref":"E2","kind":"event","title":"FLUX is fast and it's open source","date":"2024-10-10T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/flux-is-fast-and-open-source","signal_url":"https://onlylabs.fyi/signals/255fda3f-4fd1-4ba1-8c01-623ba61b30f5","signal_json_url":"https://onlylabs.fyi/signals/255fda3f-4fd1-4ba1-8c01-623ba61b30f5/signal.json","text":"post_published · FLUX is fast and it's open source · signal_desk=talking · occurred_at=2024-10-10T00:00:00+00:00 · url=https://replicate.com/blog/flux-is-fast-and-open-source · hn=258 points/122 comments · raw={\"excerpt\":\"FLUX is now much faster on Replicate, and we’ve made our optimizations open-source so you can see exactly how they work and build upon them.\"}"},{"ref":"E3","kind":"event","title":"replicate/cog v0.21.0-rc.3","date":"2026-06-05T14:49:43+00:00","date_source":"source","source_url":"https://github.com/replicate/cog/releases/tag/v0.21.0-rc.3","signal_url":"https://onlylabs.fyi/signals/a1f92851-6efe-4cd3-a510-179f1b01c5c8","signal_json_url":"https://onlylabs.fyi/signals/a1f92851-6efe-4cd3-a510-179f1b01c5c8/signal.json","text":"release · replicate/cog v0.21.0-rc.3 · signal_desk=releases · occurred_at=2026-06-05T14:49:43+00:00 · url=https://github.com/replicate/cog/releases/tag/v0.21.0-rc.3 · raw={\"repo\":\"replicate/cog\"}"},{"ref":"E4","kind":"event","title":"replicate/cog v0.21.0-rc.2","date":"2026-06-02T19:23:01+00:00","date_source":"source","source_url":"https://github.com/replicate/cog/releases/tag/v0.21.0-rc.2","signal_url":"https://onlylabs.fyi/signals/d5b2b756-e275-4430-8bf9-fedac404599e","signal_json_url":"https://onlylabs.fyi/signals/d5b2b756-e275-4430-8bf9-fedac404599e/signal.json","text":"release · replicate/cog v0.21.0-rc.2 · signal_desk=releases · occurred_at=2026-06-02T19:23:01+00:00 · url=https://github.com/replicate/cog/releases/tag/v0.21.0-rc.2 · raw={\"repo\":\"replicate/cog\"}"},{"ref":"E5","kind":"event","title":"replicate/cog v0.21.0-rc.1","date":"2026-05-29T16:25:07+00:00","date_source":"source","source_url":"https://github.com/replicate/cog/releases/tag/v0.21.0-rc.1","signal_url":"https://onlylabs.fyi/signals/ce05f875-fbe2-43c5-986c-64de8226525a","signal_json_url":"https://onlylabs.fyi/signals/ce05f875-fbe2-43c5-986c-64de8226525a/signal.json","text":"release · replicate/cog v0.21.0-rc.1 · signal_desk=releases · occurred_at=2026-05-29T16:25:07+00:00 · url=https://github.com/replicate/cog/releases/tag/v0.21.0-rc.1 · raw={\"repo\":\"replicate/cog\"}"},{"ref":"E6","kind":"event","title":"FLUX.1 Tools – Control and steerability for FLUX","date":"2024-11-21T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/flux-tools","signal_url":"https://onlylabs.fyi/signals/e4c6e6b5-f94d-4ac2-8565-c7734fd04414","signal_json_url":"https://onlylabs.fyi/signals/e4c6e6b5-f94d-4ac2-8565-c7734fd04414/signal.json","text":"post_published · FLUX.1 Tools – Control and steerability for FLUX · signal_desk=talking · occurred_at=2024-11-21T00:00:00+00:00 · url=https://replicate.com/blog/flux-tools · hn=7 points/0 comments · raw={\"excerpt\":\"A new set of image generation capabilities for FLUX models, including inpainting, outpainting, canny edge detection, and depth maps.\"}"},{"ref":"E7","kind":"event","title":"How to prompt Grok Imagine Video 1.5","date":"2026-05-21T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/grok-imagine","signal_url":"https://onlylabs.fyi/signals/d6e8d53e-0828-4603-8c02-de9ddc8bb0f3","signal_json_url":"https://onlylabs.fyi/signals/d6e8d53e-0828-4603-8c02-de9ddc8bb0f3/signal.json","text":"post_published · How to prompt Grok Imagine Video 1.5 · signal_desk=talking · occurred_at=2026-05-21T00:00:00+00:00 · url=https://replicate.com/blog/grok-imagine · raw={\"excerpt\":\"Grok Imagine Video 1.5 is the most exciting video model release from xAI. You can generate realistic video with synchronized audio in a single pass, capable of juggling complex motion with precise prompt adherence. We pushed it hard across a range of scenes, and came up with the ultimate prompting guide to get the most out of this model.\"}"},{"ref":"E8","kind":"event","title":"replicate/cog v0.20.0","date":"2026-05-19T23:00:03+00:00","date_source":"source","source_url":"https://github.com/replicate/cog/releases/tag/v0.20.0","signal_url":"https://onlylabs.fyi/signals/f4ad7cb2-7191-43cb-8a05-6f4d748b5ef6","signal_json_url":"https://onlylabs.fyi/signals/f4ad7cb2-7191-43cb-8a05-6f4d748b5ef6/signal.json","text":"release · replicate/cog v0.20.0 · signal_desk=releases · occurred_at=2026-05-19T23:00:03+00:00 · url=https://github.com/replicate/cog/releases/tag/v0.20.0 · raw={\"repo\":\"replicate/cog\"}"},{"ref":"E9","kind":"event","title":"How to prompt Nano Banana Pro","date":"2025-11-20T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/how-to-prompt-nano-banana-pro","signal_url":"https://onlylabs.fyi/signals/f0842ae4-5f15-4d7a-8cb5-afede76bdd4a","signal_json_url":"https://onlylabs.fyi/signals/f0842ae4-5f15-4d7a-8cb5-afede76bdd4a/signal.json","text":"post_published · How to prompt Nano Banana Pro · signal_desk=talking · occurred_at=2025-11-20T00:00:00+00:00 · url=https://replicate.com/blog/how-to-prompt-nano-banana-pro · hn=6 points/0 comments · raw={\"excerpt\":\"Nano Banana Pro brings powerful new capabilities in image generation and editing. Here are the main prompt tricks you should know.\"}"},{"ref":"E10","kind":"event","title":"replicate/pget v0.11.1","date":"2026-05-08T22:54:11+00:00","date_source":"source","source_url":"https://github.com/replicate/pget/releases/tag/v0.11.1","signal_url":"https://onlylabs.fyi/signals/e1f65ba4-9409-4e7a-8728-a7257fe48e53","signal_json_url":"https://onlylabs.fyi/signals/e1f65ba4-9409-4e7a-8728-a7257fe48e53/signal.json","text":"release · replicate/pget v0.11.1 · signal_desk=releases · occurred_at=2026-05-08T22:54:11+00:00 · url=https://github.com/replicate/pget/releases/tag/v0.11.1 · raw={\"repo\":\"replicate/pget\"}"},{"ref":"E11","kind":"event","title":"replicate/cog v0.19.3","date":"2026-05-04T22:26:04+00:00","date_source":"source","source_url":"https://github.com/replicate/cog/releases/tag/v0.19.3","signal_url":"https://onlylabs.fyi/signals/bfc414b6-e639-49d1-ad4d-7664c9ba189a","signal_json_url":"https://onlylabs.fyi/signals/bfc414b6-e639-49d1-ad4d-7664c9ba189a/signal.json","text":"release · replicate/cog v0.19.3 · signal_desk=releases · occurred_at=2026-05-04T22:26:04+00:00 · url=https://github.com/replicate/cog/releases/tag/v0.19.3 · raw={\"repo\":\"replicate/cog\"}"},{"ref":"E12","kind":"event","title":"How to make remarkable videos with Seedance 2.0","date":"2026-04-15T00:00:00+00:00","date_source":"rss.item_date","source_url":"https://replicate.com/blog/seedance-2","signal_url":"https://onlylabs.fyi/signals/0e34b741-f8ae-4be3-ad69-15d851d6d977","signal_json_url":"https://onlylabs.fyi/signals/0e34b741-f8ae-4be3-ad69-15d851d6d977/signal.json","text":"post_published · How to make remarkable videos with Seedance 2.0 · signal_desk=talking · 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