Then add the
Title
product to your target. The
MLX
trait is required: without it the module compiles as a stub.
Get a title and a one or two sentence description for any passage of text, on device.
Fine-tuned on transcript clips, but it works on any prose. The register is deliberately plain, with no emoji, no hashtags and no clickbait,
and a description is meant to identify
this
passage rather than its topic.
Files
An MLX model directory. Load the folder, not a single file.
File
Contents
model.safetensors
6-bit quantized weights
model.safetensors.index.json
shard index; present even for one shard, because the loader reads it
config.json
architecture and quantization config
generation_config.json
decode defaults
tokenizer.json
,
tokenizer_config.json
byte-level BPE with merges
chat_template.jinja
the chat template the fine-tune was trained against
The chat template is not incidental. A different template is a different task to this model.
The prompt
The model was fine-tuned against one specific instruction, and a paraphrase is a different
task to it. It lives in
Titles.prompt
in the SDK; use that wording. The reply is two labelled
lines:
TITLE: <3-8 words, no final punctuation>
DESC: <1-2 sentences>
Parse tolerantly. A card model that drifts off format should degrade to a usable title rather
than throw.
Apple only
MLX runs on Apple silicon and nowhere else, so there is no Android, Linux or Windows artifact
here and no manifest promising one. A Core ML export exists in the training repository and is
kept as evidence rather than as a candidate: on short autoregressive decode the Neural Engine
is bandwidth-bound, and the Core ML arm lost on first token, throughput, load time and resident
memory.
Status
Internal testing, and less settled than that phrase usually implies. This card carries no
quality figures: no independent review has been completed, and a known open issue is that the
model sometimes opens a description with a stock phrase its own instruction forbids. Treat the
output as needing a read before it reaches a user.
@software{title_2026,
title = {Title: On-device titles and descriptions: a short factual title and a one- to two-sentence description for any passage of text},
author = {Desert Ant Labs},
year = {2026},
url = {https://huggingface.co/desert-ant-labs/title},
}
title huggingface.co is an AI model on huggingface.co that provides title's model effect (), which can be used instantly with this desert-ant-labs title model. huggingface.co supports a free trial of the title model, and also provides paid use of the title. Support call title model through api, including Node.js, Python, http.
title huggingface.co is an online trial and call api platform, which integrates title's modeling effects, including api services, and provides a free online trial of title, you can try title online for free by clicking the link below.
desert-ant-labs title online free url in huggingface.co:
title is an open source model from GitHub that offers a free installation service, and any user can find title on GitHub to install. At the same time, huggingface.co provides the effect of title install, users can directly use title installed effect in huggingface.co for debugging and trial. It also supports api for free installation.