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 4 days ago

Commit

935908a

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1 Parent(s): 10fa12c

Update README.md

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README.md +3 -20

README.md CHANGED Viewed

@@ -44,8 +44,7 @@ questions) is graded by Qwen3.5-27B as judge.
 | Humanity's Last Exam (HLE), text | 15.9 | **22.7** | 22.3 |
 | HLE, physics subset | 8.4 | 15.8 | **16.3** |
-All were measured by us with the same setup. The Q8_0 version matches Quark: the differences are within
-run-to-run variation.
 **Compared with other open models** (scores as reported by each developer):
@@ -63,7 +62,7 @@ run-to-run variation.
 | Kimi K2 Thinking | 1T | 84.5 | **94.5** | 23.9 |
 — = not reported. Sources: each model's official model card; Quark (bf16) and its Q8_0 version measured by us
-(see above), with the Q8_0 version's differences from Quark within run-to-run variation.
 ## Q8_0 version
@@ -78,9 +77,8 @@ Tokens per second on an NVIDIA H200 and a MacBook Pro, for the original bf16 wei
 ### llama.cpp
-Throughput for a single user with llama.cpp, by how much context is already filled.
-Output generation, tokens per second:
 | Hardware | Context | Q8_0 | bf16 |
 | --- | --- | --- | --- |
@@ -90,21 +88,6 @@ Output generation, tokens per second:
 | MacBook Pro, M4, 16 GB | empty | **11.2** | does not fit (18.4 GB) |
 | MacBook Pro, M4, 16 GB | 32K | **8.7** | does not fit |
-### vLLM
-vLLM targets NVIDIA and AMD GPUs; on Macs, use llama.cpp or tools built on it.
-With vLLM on one NVIDIA H200 (bf16 weights), 512-token prompts and 4,096-token answers in thinking mode,
-throughput of each request, tokens per second:
-| Concurrent requests | Throughput per request |
-| --- | --- |
-| 1 | 184 |
-| 8 | 162 |
-| 24 | 145 |
-| 64 | 104 |
-| 128 | 71 |
 ## Training data
 Quark was fine-tuned on our curated mix of about 200,000 physics, science, maths and coding problems,

 | Humanity's Last Exam (HLE), text | 15.9 | **22.7** | 22.3 |
 | HLE, physics subset | 8.4 | 15.8 | **16.3** |
+All were measured by us with the same setup.
 **Compared with other open models** (scores as reported by each developer):
 | Kimi K2 Thinking | 1T | 84.5 | **94.5** | 23.9 |
 — = not reported. Sources: each model's official model card; Quark (bf16) and its Q8_0 version measured by us
+(see above)
 ## Q8_0 version
 ### llama.cpp
+Throughput (token/s) for a single user with llama.cpp, by how much context is already filled.
 | Hardware | Context | Q8_0 | bf16 |
 | --- | --- | --- | --- |
 | MacBook Pro, M4, 16 GB | empty | **11.2** | does not fit (18.4 GB) |
 | MacBook Pro, M4, 16 GB | 32K | **8.7** | does not fit |
 ## Training data
 Quark was fine-tuned on our curated mix of about 200,000 physics, science, maths and coding problems,