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

Libraries

How to use CaptchaKraken/Sunlight-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-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-AWQ-4bit")
model = AutoModelForMultimodalLM.from_pretrained("CaptchaKraken/Sunlight-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-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-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-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-AWQ-4bit

SGLang

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

CaptchaKraken Sunlight (AWQ 4-bit)

A standalone captcha-solving model: the CaptchaKrakenV1_Lora 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 is the same adapter and base at FP8, ~5 GB larger and ~4 points stronger.

Weights: ~9.1 GB · Min VRAM: ~11 GB
Base revision: c202236235762e1c871ad0ccb60c8ee5ba337b9a
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 — quantizing those costs accuracy out of proportion to the space saved. Calibrated on 256 in-domain captchas (hCaptcha + reCAPTCHA, none from the held-out set).
Solves: reCAPTCHA and hCaptcha grids, plus hCaptcha drag / click / path / fit puzzles.

Serving (vLLM)

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

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

Prompts — read this before integrating

This model only performs as measured when it is sent the prompts it was trained on. prompts.json in this repo carries them, along with the puzzle-type → template mapping and a prompt_version. Resolve prompts from that file rather than hardcoding a copy: a mismatched prompt does not error, it silently collapses accuracy.

Two integration requirements:

Disable thinking. Send chat_template_kwargs: {"enable_thinking": false}. With thinking on and a qwen3 reasoning parser, the answer is routed into reasoning and content comes back empty.
Coordinates are normalized 0–1000, top-left (0,0), bottom-right (1000,1000) — not pixels.

Evaluation

Scored with the project's soft-tolerance grader on 156 human-labelled held-out captchas, through an OpenAI-compatible vLLM endpoint. Samples promoted into the labelled-train pool are excluded, so nothing here was 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".

model	score
Twilight (FP8)	66.87%
production `captcha` (FP8 base + LoRA at serve time)	66.30%
Sunlight (AWQ 4-bit)	62.72%

Merging costs nothing: Twilight matches the served base+adapter setup within noise. The 4-bit gap concentrates in coordinate-precision puzzles (drag, path) rather than tile selection, where the two are identical.

License

CaptchaKraken Source-Available License v1.0 — 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.

Downloads last month: 8

Safetensors

Model size

10B params

Tensor type

I32

BF16

Model tree for CaptchaKraken/Sunlight-AWQ-4bit

Base model

Qwen/Qwen3.5-9B-Base

Finetuned

Qwen/Qwen3.5-9B

Quantized

(421)

this model