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

Libraries

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

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

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

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "tawkeed-sa/tawkeed-gpt"
# 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-gpt",
		"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-gpt

SGLang

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

Tawkeed GPT — توكيد جي بي تي

Tawkeed GPT is Tawkeed's sovereign Arabic-first general assistant. It always produces the final answer locally and has no advisor tool or external retrieval path. Arabic questions receive Arabic answers while accurate Arabic and English technical terminology is preserved.

The weights are an unchanged server-side copy of unsloth/Qwen3.6-35B-A3B-NVFP4. The Tawkeed GPT role is supplied by serving/tawkeed_gpt.py; it is not a fine-tune embedded in the checkpoint.

Privacy and data flow

The default endpoint is the local DGX Spark runtime. The wrapper defines no tools, never invokes Tawkeed Advisor, and does not claim external knowledge or fresh sources unless results are explicitly supplied in the conversation. Operators remain responsible for keeping the configured inference endpoint inside their trusted boundary.

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_gpt.py --query "ما هي عاصمة المملكة العربية السعودية؟"

Validation status

Offline local-answer contracts: passing.
Exact DGX Spark NVFP4 role suite: 3/3 Arabic/general-answer cases passed.
Warm exact-runtime latency: 27.219 s mean, 29.400 s p95 with explicit Qwen reasoning enabled.
The wrapper reserves enough reasoning budget and retries in direct-answer mode if a provider returns no visible content.
Fresh-downloaded Hugging Face package test: 1/1 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 Nex-N2 / Qwen3.5-lineage checkpoint is preserved separately as an archive and is not the model represented by this repository.

Downloads last month: 12

Safetensors

Model size

25B params

Tensor type

F32

BF16

F8_E4M3

Model tree for tawkeed-sa/tawkeed-gpt

Base model

Qwen/Qwen3.6-35B-A3B

Quantized

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

Quantized

(6)

this model