Instructions to use CaptchaKraken/CaptchaKrakenV1_Lora with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use CaptchaKraken/CaptchaKrakenV1_Lora with PEFT:
from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3.5-9B") model = PeftModel.from_pretrained(base_model, "CaptchaKraken/CaptchaKrakenV1_Lora") - Notebooks
- Google Colab
- Kaggle
CaptchaKrakenV1_Lora
A multimodal LoRA adapter for Qwen/Qwen3.5-9B that solves image captchas.
It is the model behind CaptchaKraken
โ a self-hosted captcha solver for browser automation (npm captcha-kraken-js,
PyPI captchakraken).
This adapter supersedes the earlier CaptchaKraken_v1 and CaptchaKraken_v1.1
repositories, which have been withdrawn. It is the same trained adapter as
v1.1, republished under the CaptchaKraken Source-Available License and under a
stable name.
Coverage
The first release covered grid captchas only ("select all squares withโฆ"). This adapter also handles the non-grid puzzle families โ drag-to-slot, click-the-object, line-tracing, tile-fitting, and the other hCaptcha interaction types โ while keeping the grid performance. It is trained with per-object content supervision (each click/drag target is labelled by what it is, not just where), which is what recovered the drag/click puzzles.
Details
- Base model:
Qwen/Qwen3.5-9B(natively multimodal), revisionc202236235762e1c871ad0ccb60c8ee5ba337b9aโ the revision this adapter was trained against. - Adapter: PEFT LoRA,
r=32,lora_alpha=64, applied to both the language model and the vision tower, excluding the vision merger. Serving the adapter therefore requires vLLM's--enable-tower-connector-lora. - Task: image captcha solving. Grid tile selection (reCAPTCHA 3ร3 / 4ร4, hCaptcha 3ร3 image grids) plus non-grid puzzles (drag / click / line / fit). For grids it returns the cell numbers to click; for interaction puzzles it returns the action (click points or drag vectors).
Serving (vLLM)
vllm serve Qwen/Qwen3.5-9B \
--reasoning-parser qwen3 \
--enable-lora --enable-tower-connector-lora \
--max-lora-rank 32 --max-model-len 8192 \
--trust-remote-code --port 8000 \
--lora-modules captcha=CaptchaKraken/CaptchaKrakenV1_Lora
--enable-tower-connector-lora is required โ without it the vision half of
the adapter is dropped and grid accuracy collapses. On limited-VRAM GPUs, serve
a quantized base (AWQ/FP8) instead of the bf16 base above, or use one of the
pre-merged single-file models below.
The CaptchaKraken project wires
this up hands-off: setup.sh downloads the weights and the server auto-starts on
the first solve.
Pre-merged models
If you would rather not wire up an adapter, this adapter merged into the base is published as two standalone models โ no adapter flags, one download:
| Model | Precision | Weights | Min VRAM |
|---|---|---|---|
CaptchaKraken/Sunlight-AWQ-4bit |
4-bit (AWQ) | ~6 GB | 11 GB |
CaptchaKraken/Twilight-FP8 |
8-bit (FP8) | ~14 GB | 22 GB |
License
CaptchaKraken Source-Available License v1.0 โ see LICENSE.
You may use, modify, and distribute this adapter, including commercially, where captcha solving is an internal component of a product that delivers value beyond the solve itself โ scrapers, anti-detection browsers, automation frameworks, QA and accessibility tooling.
You may not sell the solve: no reselling or offering for a fee any captcha solving service or API whose primary value is solving captchas, no thin wrappers that just expose this model's solving capability, and no relaying this model's outputs through a paid or public captcha-solving API. Those uses require a separate written commercial agreement from the copyright holder.
Copyright (c) 2026 CaptchaKraken LLC. All rights reserved.
Framework versions
- PEFT 0.18.1
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