Instructions to use tawkeed-sa/tawkeed-router with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use tawkeed-sa/tawkeed-router with Transformers:

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

pipe = pipeline("image-text-to-text", model="tawkeed-sa/tawkeed-router")
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("tawkeed-sa/tawkeed-router")
model = AutoModelForMultimodalLM.from_pretrained("tawkeed-sa/tawkeed-router")
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 tawkeed-sa/tawkeed-router with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tawkeed-sa/tawkeed-router"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "tawkeed-sa/tawkeed-router",
		"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/tawkeed-sa/tawkeed-router

SGLang

How to use tawkeed-sa/tawkeed-router 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 "tawkeed-sa/tawkeed-router" \
    --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": "tawkeed-sa/tawkeed-router",
		"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 "tawkeed-sa/tawkeed-router" \
        --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": "tawkeed-sa/tawkeed-router",
		"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 tawkeed-sa/tawkeed-router with Docker Model Runner:
```
docker model run hf.co/tawkeed-sa/tawkeed-router
```

Tawkeed Router — موجّه توكيد

Tawkeed Router is the local routing role in the Tawkeed Qwen3.6 family. It selects exactly one downstream family without answering the user's request:

tawkeed-gpt for routine conversation, drafting, translation, extraction, summarization, and straightforward questions.
tawkeed-advisor for genuinely difficult specialist, mathematical, scientific, or multi-step reasoning tasks where generic expert methodology may improve the locally produced answer.

The weights are an unchanged server-side copy of unsloth/Qwen3.6-35B-A3B-NVFP4. The routing behavior is a runtime policy in serving/tawkeed_router.py, not a fine-tune embedded in the checkpoint.

Privacy and data flow

The default runtime is on-prem and makes no advisor or internet call. Its output is strict JSON containing target, confidence, and a short category-level reason. Invalid model output fails safely to Tawkeed Advisor. Sensitive data alone does not trigger Advisor; routing is based on difficulty.

Tawkeed Router, GPT, and Advisor can share one loaded Qwen3.6 NVFP4 server; their system policies provide the different roles. Separate copies of the weights are published so each Hugging Face model identity is complete.

Runtime

The recommended DGX Spark server is MiaAI-Lab/Unsloth-Qwen3.6-35b-NVFP4-DGX-Spark. After its OpenAI-compatible endpoint is available:

pip install -r requirements-tawkeed.txt
python serving/tawkeed_router.py --query "ترجم هذه الجملة"

Validation status

Offline routing contracts: passing.
Exact DGX Spark NVFP4 role suite: 8/8 routing decisions passed.
Warm exact-runtime latency: 5.561 s mean, 8.254 s p95 (sequential, conservative eager-mode validation).
Fresh-downloaded Hugging Face package tests: 2/2 passed.

Validated 2026-07-13 with the MiaAI-Lab image at digest sha256:556dd955082f8aa29779b0bc6d068537e7f2091c5e87ab41f5496b70920a2019. The server loaded the checkpoint through compressed-tensors, FlashInferB12xNvFp4LinearKernel, and the FlashInfer CUTLASS NVFP4 MoE backend.

This role suite is an integration check, not a benchmark claim. The previous compact Qwen3-0.6B/head router is preserved separately as a legacy artifact and is not the model represented by this repository.

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Safetensors

Model size

25B params

Tensor type

F32

BF16

F8_E4M3

Model tree for tawkeed-sa/tawkeed-router

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

unsloth/Qwen3.6-35B-A3B-NVFP4

Quantized

(4)

this model