Drop <|think_on|> or <|think_off|> anywhere in your system or user prompt. The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.
Fast answer, no reasoning:
System: You are a coding assistant. <|think_off|>
User: What's 2+2?
Deep reasoning:
System: You are a coding assistant. <|think_on|>
User: Implement a red-black tree in Rust.
The tag syntax (<|think_on|>, <|think_off|>) uses Qwen's control-token delimiters, so it will never collide with real text. Earlier community templates used /think, which broke legitimate paths like cd /mnt/project/think.
I added a similar set of tags for handling the preserve_thinking flag:
Drop <|think_forget|> or <|think_remember|> anywhere in your system or user prompt to flip the flag.
The template intercepts the tag, removes it from context so the model never sees it, and flips the mode.
The original FINAL-Bench/Darwin-36B-Opus is text only.
This merged model has full vision ability.
It made no sense to have a blind Darwin.
The Quinn character vibes with that Q with a moral spine.
DavidAU came up with the name. It makes sense.
Potato-Potato
-G
Qwen3.6-35B-A3B-Holo3-Qwopus
This is a merge of the following models:
Qwen/Qwen3.6-35B-A3B
samuelcardillo/Qwopus-MoE-35B-A3B
Hcompany/Holo3-35B-A3B
Two excellent models merged into one, ontop of the new Qwen3.6-35B-A3B that gives better context handling and superior.. everything :)
Holo3: Foundational Models for Navigation and Computer Use Agents
Holo3 is our latest generation of large-scale Vision-Language Models (VLMs) specifically optimized for GUI Agents. Like its predecessors, it operates across diverse digital environments—web, desktop, and mobile—by interpreting visual interfaces, reasoning over complex content, and executing precise actions.
Holo3 achieves state-of-the-art performance on OSWorld-Verified, setting a new benchmark for computer use agents. While it retains the world-class web navigation capabilities of Holo2, the new Holo3-35B-A3B architecture is designed to thrive in realistic business environments.
A 36B-parameter MoE model (3B active), tying the 397B dense-equivalent Qwen3.5-397B-A17B and surpassing flagship dense and sparse systems an order of magnitude larger.
What Is Darwin?
Darwin is the evolutionary model breeding engine developed by FINAL-Bench / VIDRAFT_LAB. Rather than allocating further compute to gradient optimization, Darwin treats trained checkpoints as a genetic pool and discovers high-performing descendants through principled recombination of their weight tensors.
Each Darwin generation (v1 through v7+) refines the breeding procedure. Darwin V7 is the current generation and the one used to produce this model. Specific algorithmic details of V7 are proprietary to FINAL-Bench; at a high level, the engine performs:
Per-tensor compatibility analysis of the two parents to identify which components transfer cleanly and which require weighted recombination.
Automated recombination guided by that analysis, producing a single coherent descendant.
Verification via a multi-phase scientific benchmark before release.
