Model reference · open weights

clips

Embeddings desert-ant-labs Reranker 1 build Its own licence terms 600 dl/mo

clips is an open-weight reranker from desert-ant-labs. clips (BF16) weighs 4 MB; the smallest configuration that runs it is RTX 3060 12 GB.

For self-hosting, compare this build's requirements below with the GPU you plan to rent.

  • Clips is a text-ranking model by Desert Ant Labs that identifies short video highlights from transcripts for podcasts, interviews, and meetings.
  • It runs on-device across iOS, macOS, Linux, Windows, and other platforms, supporting 100 languages.
  • The model uses a 284MB int8 Core ML package on Apple devices or two 283MB LiteRT files elsewhere, and it operates under a source-available license.

Summary of the desert-ant-labs/clips model card, 2026-10-12

What it is

Released bydesert-ant-labs
Released2026-07-24
VRAM4 MB for the weights

What it runs on

Memory and cards for clips (BF16)

4 MBweights, file size
1.1 GBruntime overhead
CardRunsMemory
RTX 3060 12 GBfits11.6 GB
RTX 4060 Ti 16 GBfits15.4 GB
RTX 3090 24 GBfits23.4 GB
RTX 4090 24 GBfits23.4 GB
RTX 5090 32 GBfits31.0 GB
L40S 48 GBfits44.0 GB
A100 80 GBfits78.2 GB
H100 80 GBfits78.1 GB
RTX PRO 6000 Blackwell 96 GBfits93.8 GB
DGX Spark (GB10) 128 GB unifiedfits107 GB
H200 141 GBfits138 GB
B200 180 GBfits176 GB

From the model card

What desert-ant-labs says about clips

Read the model card

Create short videos and highlight clips.

Short clips and highlights from talking video and audio: podcasts, interviews, meetings. On-device.

  • SDKs, install and examples: https://github.com/Desert-Ant-Labs/desert-ant-core/blob/main/docs/models/clips.md
PlatformsiOS, macOS, tvOS, visionOS, Linux, Windows
Weightsv0.1.0

Install

Swift (requirements)

.package(url: "https://github.com/Desert-Ant-Labs/desert-ant-core.git", from: "3.6.0")

Then add the Clips product to your target.

What the model does

Point Clips at a transcript and get the moments worth cutting, ranked best first and never overlapping. Nothing is uploaded, so clipping a back catalogue costs the same as clipping one video.

284MB as a single int8 Core ML package on Apple platforms, or two 283MB LiteRT files elsewhere. 100 languages.

Clips carry no title. Pairing them with words is a separate model, desert-ant-labs/title.

Maximum video length

There's no context window. Clips never reads a transcript whole: sentences run through the selector in batches of 16 and each candidate clip is scored on its own, so length is bounded by time rather than by a token limit. The longest we've run is an 835 sentence podcast.

transcriptiPhone 17 ProiPhone 15 Pro
404 sentences, 25 minutes of video, 12 clips9.19s10.22s
57 sentences2.23s
per candidate, encoder only2.78ms3.13ms

Measured on device at batch 16, pinned to .cpuAndNeuralEngine.

Files

FileFormatSizeContents
clips.mlmodelc/Compiled Core ML, int8 per-channel284MBMultifunction package. Function select: ids, mask, disc to saliency, start_p, end_p. Function score: ids, mask to score
clips-selector.tfliteLiteRT, int8 weight-only283MBThe selector, for Android, Linux and Windows
clips-scorer.tfliteLiteRT, int8 weight-only283MBThe scorer, for Android, Linux and Windows
clip_tokenizer.binUnigram tokenizer4MBTokenizer pieces and scores, in the compact binary the runtimes read
clips_meta.jsonJSONtinyGraph widths, input roles and feature order a runtime needs

On Apple platforms both graphs live in one Core ML package with the shared weights stored once. LiteRT has no equivalent packaging, so the other platforms ship two files.

The selector graph is 128 tokens wide, the scorer 256, both at a fixed batch of 16 sentences. A sentence longer than 64 tokens is truncated before it reaches the selector.

Core ML functions

Set MLModelConfiguration.functionName to select or score. A path names the package, not the graph, and Core ML loads the default function silently, so without it both halves of your pipeline run the selector.

Input roles on LiteRT

The LiteRT graphs take int64 ids and mask, and name their inputs positionally, args_0 upward, so clips_meta.json carries the mapping. Feed ids and mask swapped and the graph returns finite, plausibly scaled numbers and a pipeline that quietly builds the wrong pool.

Thresholds

Clip.score runs 1 to 5 and is not calibrated between videos. Threshold on percentile instead. An absolute cut at 0.65 returns nothing on 21% of videos, and at 0.75 on half of them. Per-video 95th percentiles range from 0.33 to 0.95.

percentile cutclips per videoshare of the video covereduse
0.85.017%highlights
0.332.174%auto edit

Limits

  • Clip edges are the weak spot. A clip can open on a pronoun whose referent was in the sentence before it, fuse two topics into one, cut before the payoff lands, or swallow a sponsor read.
  • Short videos return few clips. A two minute transcript comes back with fewer clips than a server-side editor would cut, and a transcript under three sentences returns none.
  • The clip limit is a cap, not a quota. Asking for 10 doesn't mean receiving 10.
  • Selection is sensitive to small score changes. Candidate spans around one moment score very close together, so a different runtime, compute unit or quantization returns a different but comparable set.
  • Non-Latin scripts are under-tested. Our evaluation corpus is overwhelmingly Latin script.
  • Duration is a soft prior. A clip can come back shorter or longer than a typical Short.
  • The LiteRT files don't load through the Desert Ant SDK. The graphs take int64 ids where the SDK builds int32. Drive them from a LiteRT runtime directly.
  • Android needs headroom. The shipped int8 weight-only build peaks at 2136MB resident, measured on 6,395 real candidate spans. That fits a high-end phone and not a mid-range one, which is why there is no Android SDK yet rather than any limit in the artifact.

Requirements

iOS 18, macOS 15, tvOS 18, visionOS 2, watchOS 11. Android, Linux and Windows through LiteRT, driven directly rather than through the SDK.

License

Desert Ant Labs Source-Available License. Free for most apps, and a commercial license is required at scale. Full terms are at the link. Licensing: .

See THIRD_PARTY_NOTICES.md.

Citation

@software{clips_2026,
  title  = {Clips: Short clips and highlights from talking video and audio: podcasts, interviews, meetings. On-device},
  author = {Desert Ant Labs},
  year   = {2026},
  url    = {https://huggingface.co/desert-ant-labs/clips},
}

© 2026 Desert Ant Labs ·

Quoted from the model card on Hugging Face. The full card is behind the Hugging Face link above.

How it works

How rerankers work

Question + passageswhat to orderRerankerreads each pairScoresbest answer firstA reranker reads the question together with each passage and scores how well it answers it, so the best ones come first.
© 2026 AxForge · EU-hosted AI infrastructure Pricing Docs Trust Privacy Terms