♾️ Thinking

Wop

wop

84 30 453

https://bench-labs-org.github.io

koo1140

AI & ML interests

AI research AGI

Recent Activity

liked a model about 4 hours ago

internlm/Atria-Dawn-Preview

liked a model about 5 hours ago

nex-agi/Nex-N2.5-mini

reacted to RiverRider's post with 🔥 1 day ago

Black Window — a chat model in your browser tab, on your hardware. A memory that stays on the device that opened the page. https://blackwindow.xyz Open the site, pick a model (about 0.6B to 8B), hit Load. The weights run in that tab, on that computer. After they load, the network can drop. The context window is a working set, auto-sized to that device, up to ~32K tokens. Behind the window is the Weave. Every file, picture, recording, link, lookup, and reply is embedded as it arrives. Drop in audio and it is transcribed. Drop in an image and it is described. A question pulls the nearest passages back as notes. A long document is walked once so later questions can use the whole file, not the first pages. Nothing leaves that tab unless you turn on live lookup or connect a rented GPU box, and the chat says so each time. Prompts can go to the box. Files and the Weave stay in the tab. Console on that page: bw.ask, bw.search, bw.digest, bw.notes. A local relay exposes /v1/chat/completions on localhost so other tools on the same computer can talk to the tab. The tab polls the relay. That is the boundary. Not a server with a policy. Your hardware, a window, a Load button. If on mobile add to home-screen for best performance. If you break it lmk. It can serve a few hundred of you at a time before I have to buy a real server.

View all activity

Organizations

Posts 7

Post

2192

bench-labs/GCTokenizer-v1 , a multilingual tokenizer which does not require a training corpus

bench-labs developed **GCTokenizer-v1**, which is a multi-lingual tokenizer
Available in four sizes: 32K, 65K, 131K and 262K tokens "S, M, L, XL"
It utilizes an encoding scheme which allows it to handle characters in any language around the world

General (multi lingual)
Consensus (from multiple model tokenizers consensus)
Tokenizer

We included an implementation script too,
built like BPE- it can encode arbitrary text, most of the time, efficiently