Instructions to use FirstPrinciplesLabs/quark with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use FirstPrinciplesLabs/quark with Transformers:

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

pipe = pipeline("image-text-to-text", model="FirstPrinciplesLabs/quark")
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)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("FirstPrinciplesLabs/quark")
model = AutoModelForMultimodalLM.from_pretrained("FirstPrinciplesLabs/quark", 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=256)
print(processor.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use FirstPrinciplesLabs/quark with vLLM:

Install from pip and serve model

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

SGLang

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

chengfp-eng commited on 9 days ago

Commit

8907ae7

verified ·

1 Parent(s): 2720954

README: remove cut-off columns

Browse files

Files changed (1) hide show

README.md +9 -10

README.md CHANGED Viewed

@@ -33,21 +33,20 @@ up to 131,072 output tokens, and following the Qwen3.5 model card's sampling set
 AIME 2025 is averaged over 16 samples per problem and GPQA Diamond over 8. HLE (text-only, 2,158 questions)
 is graded by Qwen3.5-27B as judge.
-| Benchmark | Qwen3.5-9B | **Quark** | Cut off at 131K: Qwen3.5-9B | Cut off at 131K: Quark |
-|---|---|---|---|---|
-| GPQA Diamond | 81.6 | **87.4** | 0% | 0.3% |
-| AIME 2025 | 86.9 | **91.7** | 0% | 3.3% |
-| MMLU-Pro Physics | 90.7 | 91.0 | 0% | 0% |
-| Humanity's Last Exam (HLE), text | 15.9 | **22.7** | 3.7% | 5.9% |
-| HLE, physics subset | 8.4 | **15.8** | 1.0% | 1.5% |
-Both models were measured by us with the same setup. "Cut off" is the share of answers that reached the
-131,072-token limit before finishing; those count as wrong.
 **Compared with other open models** (scores as reported by each developer):
 | Model | Size | GPQA Diamond | AIME 2025 | HLE |
-|---|---|---|---|---|
 | **Quark** | 9B | **87.4** | **91.7** | **22.7** |
 | Qwen3-8B | 8B | 59.6 | 69.3 | 4.4 |
 | NVIDIA Nemotron Nano 9B v2 | 9B | 64.0 | 72.1 | 6.5 |

 AIME 2025 is averaged over 16 samples per problem and GPQA Diamond over 8. HLE (text-only, 2,158 questions)
 is graded by Qwen3.5-27B as judge.
+| Benchmark | Qwen3.5-9B | **Quark** |
+|---|---|---|
+| GPQA Diamond | 81.6 | **87.4** |
+| AIME 2025 | 86.9 | **91.7** |
+| MMLU-Pro Physics | 90.7 | 91.0 |
+| Humanity's Last Exam (HLE), text | 15.9 | **22.7** |
+| HLE, physics subset | 8.4 | **15.8** |
+Both models were measured by us with the same setup.
 **Compared with other open models** (scores as reported by each developer):
 | Model | Size | GPQA Diamond | AIME 2025 | HLE |
+|---|---|---|
 | **Quark** | 9B | **87.4** | **91.7** | **22.7** |
 | Qwen3-8B | 8B | 59.6 | 69.3 | 4.4 |
 | NVIDIA Nemotron Nano 9B v2 | 9B | 64.0 | 72.1 | 6.5 |