Instructions to use CaptchaKraken/Sunlight-v1.2-AWQ-4bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use CaptchaKraken/Sunlight-v1.2-AWQ-4bit with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("image-text-to-text", model="CaptchaKraken/Sunlight-v1.2-AWQ-4bit")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
pipe(text=messages)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("CaptchaKraken/Sunlight-v1.2-AWQ-4bit")
model = AutoModelForMultimodalLM.from_pretrained("CaptchaKraken/Sunlight-v1.2-AWQ-4bit", device_map="auto")
messages = [
    {
        "role": "user",
        "content": [
            {"type": "image", "url": "https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/p-blog/candy.JPG"},
            {"type": "text", "text": "What animal is on the candy?"}
        ]
    },
]
inputs = processor.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use CaptchaKraken/Sunlight-v1.2-AWQ-4bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "CaptchaKraken/Sunlight-v1.2-AWQ-4bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CaptchaKraken/Sunlight-v1.2-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker

docker model run hf.co/CaptchaKraken/Sunlight-v1.2-AWQ-4bit

SGLang

How to use CaptchaKraken/Sunlight-v1.2-AWQ-4bit with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "CaptchaKraken/Sunlight-v1.2-AWQ-4bit" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CaptchaKraken/Sunlight-v1.2-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "CaptchaKraken/Sunlight-v1.2-AWQ-4bit" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "CaptchaKraken/Sunlight-v1.2-AWQ-4bit",
		"messages": [
			{
				"role": "user",
				"content": [
					{
						"type": "text",
						"text": "Describe this image in one sentence."
					},
					{
						"type": "image_url",
						"image_url": {
							"url": "https://cdn.britannica.com/61/93061-050-99147DCE/Statue-of-Liberty-Island-New-York-Bay.jpg"
						}
					}
				]
			}
		]
	}'

Docker Model Runner
How to use CaptchaKraken/Sunlight-v1.2-AWQ-4bit with Docker Model Runner:
```
docker model run hf.co/CaptchaKraken/Sunlight-v1.2-AWQ-4bit
```

CaptchaKraken Sunlight v1.2 (AWQ 4-bit)

A standalone captcha-solving model: the CaptchaKraken-Lora-v1.2 adapter merged into Qwen/Qwen3.5-9B and quantized to AWQ 4-bit. One download, no adapter to wire up.

Sunlight is the lighter of two hardware tiers. Twilight v1.2 is the same merge at FP8 — 2 GB larger, and the one our hosted API runs.

Note this is larger than v1.1's Sunlight (9.1 GB) despite the same 4-bit scheme: only the language-model linears are quantized, and the vision tower this generation carries stays bf16 on purpose. Quantizing the half that reads the picture is not a size win worth making.

Weights: 11 GB (measured) · Min VRAM: ~14 GB
Quantized: language-model linears at 4-bit (group 128, asymmetric). The vision tower, the linear-attention projections and the multi-token-prediction head stay bf16 — the task is reading a small picture accurately, and quantizing the half that does the reading costs more than it saves. Calibrated on 256 synthetic in-domain captchas spread across puzzle types, none from the held-out eval.

What changed since v1.1

v1.1 solved reCAPTCHA and hCaptcha. v1.2 solves everything the generators cover — 44 puzzle types across 10 vendors, and it is the first generation to handle animated challenges and typed text captchas at all.

	v1.1	v1.2
Vendors	reCAPTCHA, hCaptcha	+ GeeTest, Yidun, BotDetect, MTCaptcha, Yandex, Tencent, Lemin, Prosopo
Animated / video challenges	✗	✓
Typed text captchas	✗	✓
Prompt generation	1	2

Requires captchakraken >= 2.5.0

This model answers generation-2 prompts. Every v1.1 model is generation 1, and sending this one a generation-1 prompt does not error — it silently degrades on every puzzle. Clients before 2.5.0 hardcode generation-1 text and have no way to know otherwise.

pip install "captchakraken>=2.5.0"      # or: npm install captchakraken@^2.5.0

Serving (vLLM)

vllm serve CaptchaKraken/Sunlight-v1.2-AWQ-4bit \
  --max-model-len 8192 --gpu-memory-utilization 0.85 --trust-remote-code --port 8000

export VLLM_BASE_URL=http://localhost:8000/v1
export CAPTCHA_KRAKEN_API_KEY=EMPTY
export CAPTCHA_LORA_NAME=CaptchaKraken/Sunlight-v1.2-AWQ-4bit   # must match the served name

No --enable-lora and no adapter flags — the adapter is already merged in.

Three things to get right, or it scores far below what it can

Send the prompts it was trained on. prompts.json in this repo carries them plus the puzzle-type → template mapping. A mismatched prompt does not error, it collapses accuracy.
Disable thinking — chat_template_kwargs: {"enable_thinking": false}. With thinking on and a qwen3 reasoning parser, the answer goes to reasoning and content comes back empty.
Serve it at a flat 720² pixel budget — MIN_PIXELS=518400, MAX_PIXELS=518400. Qwen clamps each image's AREA into that band, and the band decides how many visual tokens the model reads a puzzle from. This one was measured, not guessed: see below.

Coordinates come back normalized 0–1000, top-left (0,0), bottom-right (1000,1000) — not pixels. Grid screenshots must be sent with cell numbers drawn on them; the model reads those labels and was never trained to invent a numbering. The shipped client does all of this for you.

Evaluation

Scored with the project's soft-tolerance grader on the full 1458-sample held-out real eval — every hand-labelled real capture we hold, none of it trained on. Grid puzzles are exact tile-set match with per-error decay; click/drag receive partial credit by normalized distance, so these are graded scores, not "percent solved".

Vendor	Puzzle types	Held-out samples	Score
reCAPTCHA	2	629	0.600
hCaptcha	26	539	0.543
GeeTest	7	172	0.780
NetEase Yidun	3	37	0.775
BotDetect	1	28	0.929
Tencent	1	11	0.829
Prosopo	1	11	0.727
Yandex	1	11	0.364
Lemin	1	10	0.874
MTCaptcha	1	10	0.900
Overall	44	1458	0.6155

Training run 20260812-005302, verdict ready-to-deploy — passed all three gates uncapped.

These figures are the unquantized adapter's, and 4-bit will not match them. At v1.1 the AWQ build scored 62.72% against the FP8 build's 66.87% on the eval current then — about 4 points, concentrated in the coordinate-precision puzzles (drag, path) rather than in tile selection. Expect a gap of that order here. The v1.2 delta has not been measured on its own yet, and this note stays until it has. If you have the 22 GB, take Twilight instead.

v1.1's numbers are not comparable to these. They were taken on a 156-sample eval that predates the 2026-08-05 split fix, when part of the real capture set was still in training.

Why 720², and why not the ViT's native grid

Sweeping the serving floor over 448/576/640/704/720/736/768/896/1024² (full 1458-sample eval per arm, run-to-run noise ±0.001) put the plateau at 704–736²:

floor	patch grid on a 400×580 capture	overall	4×4 exact
448²	23×33	0.6115	0.390
640²	33×48	0.6357	0.425
720²	37×53	0.6412	0.406
768²	39×57	0.6368	0.449
1024²	53×77	0.6243	0.370

+3.0 points overall and reCAPTCHA 3×3 exact 0.648 → 0.715. num_position_embeddings: 2304 is 48×48 patches = 768² for a square image, but real captures are 400×580 portrait, so 768² of area already puts the long side at 57 patches. The area that saturates the grid exactly is 640² — and it scores worse. Mild position-embedding extrapolation plus real pixels beats staying inside the grid; it falls apart past ~67 patches. Floor == ceiling, so every image arrives at one size and the band cannot drift.

License

CaptchaKraken Source-Available License v1.1 — see LICENSE.

Use it, modify it, build commercially on it where captcha solving is an internal component of a product that delivers value beyond the solve — scrapers, anti-detection browsers, automation frameworks, QA and accessibility tooling.

You may not sell the solve: no reselling or offering for a fee a captcha solving service or API whose primary value is solving captchas, no thin wrappers exposing this model's solving capability, and no relaying its outputs through a paid or public captcha-solving API. Those require a separate written commercial agreement.