Instructions to use FabricAI/Fabric1.6 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FabricAI/Fabric1.6 with Transformers:

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

pipe = pipeline("image-text-to-text", model="FabricAI/Fabric1.6")
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("FabricAI/Fabric1.6")
model = AutoModelForMultimodalLM.from_pretrained("FabricAI/Fabric1.6", 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 FabricAI/Fabric1.6 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "FabricAI/Fabric1.6"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "FabricAI/Fabric1.6",
		"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/FabricAI/Fabric1.6

SGLang

How to use FabricAI/Fabric1.6 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 "FabricAI/Fabric1.6" \
    --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": "FabricAI/Fabric1.6",
		"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 "FabricAI/Fabric1.6" \
        --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": "FabricAI/Fabric1.6",
		"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 FabricAI/Fabric1.6 with Docker Model Runner:
```
docker model run hf.co/FabricAI/Fabric1.6
```

tudor-iustin22 commited on 6 days ago

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@@ -257,14 +257,13 @@ model-index:
   </picture>
 </div>
-<div align="center" style="line-height: 1;">
-  <a href="https://huggingface.co/FabricAI" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/HuggingFace-FabricAI-e0a800?logo=huggingface&logoColor=black"/></a>
-  <a href="https://fabricai.co.uk" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-Fabric_AI-white?style=social&logo=globe&logoColor=black"/></a>
-  <a href="https://x.com/fabricai_uk" target="_blank"><img alt="X" src="https://img.shields.io/badge/X-%40fabricai_uk-white?style=social&logo=x&logoColor=black"/></a>
 </div>
----
 # Fabric 1.6
 **Fabric 1.6** is a 35-billion-parameter Mixture-of-Experts (MoE) reasoning model developed by **Fabric AI**, with approximately **3 billion parameters activated per token**. It is a native multimodal, agentic model built on a hybrid **Gated DeltaNet + Gated Attention** architecture, with explicit chain-of-thought reasoning, a native 262,144-token context window, and built-in Multi-Token Prediction (MTP) for up to 50% faster generation.

   </picture>
 </div>
+<hr>
+<div align="center" style="line-height:1">
+  <a href="https://huggingface.co/FabricAI" target="_blank"><img alt="Hugging Face" src="https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-FabricAI-ffc107?color=e0a800&logoColor=white"/></a>
+  <a href="https://fabricai.co.uk" target="_blank"><img alt="Homepage" src="https://img.shields.io/badge/Homepage-Fabric%20AI-white?logo=globe&logoColor=white"/></a>
+  <a href="https://x.com/fabricai_uk" target="_blank"><img alt="X" src="https://img.shields.io/badge/X-%40fabricai_uk-white?logo=x&logoColor=black"/></a>
 </div>
 # Fabric 1.6
 **Fabric 1.6** is a 35-billion-parameter Mixture-of-Experts (MoE) reasoning model developed by **Fabric AI**, with approximately **3 billion parameters activated per token**. It is a native multimodal, agentic model built on a hybrid **Gated DeltaNet + Gated Attention** architecture, with explicit chain-of-thought reasoning, a native 262,144-token context window, and built-in Multi-Token Prediction (MTP) for up to 50% faster generation.