Instructions to use Cloudflare/clef-flash with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cloudflare/clef-flash with Transformers:

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

pipe = pipeline("image-text-to-text", model="Cloudflare/clef-flash")
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("Cloudflare/clef-flash")
model = AutoModelForMultimodalLM.from_pretrained("Cloudflare/clef-flash", 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 Cloudflare/clef-flash with vLLM:

Install from pip and serve model

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

SGLang

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

Support / Roadmap for serving Clef-Flash via vLLM?

by Vishva007 - opened 2 days ago

Discussion

Vishva007

2 days ago

•

edited 2 days ago

Hi @Cloudflare team,

Thanks for releasing Clef-Flash! The joint schema evaluation approach and single forward-pass probability output design are exceptional for high-throughput decision and classification pipelines.

Since standard deployment in production environments relies heavily on vLLM (for PagedAttention, continuous batching, and high concurrency), I wanted to check on vLLM compatibility:

Custom Architecture Support: Since clef-flash relies on custom code (joint_schema_model.py / systemone) and evaluates typed questions via direct hidden-state readouts rather than token-by-token autoregressive generation, is there any current pathway or plan to register ClefForConditionalGeneration / its joint classification heads as an out-of-the-box model architecture in vLLM?
Forward-Pass / Embedding API: In vLLM, classification and embedding models typically use the pooling/forward-pass endpoints rather than /v1/completions. Has the team explored integrating systemone schema scoring on top of vLLM’s forward/pooling engine?
Current Serving Recommendations: Outside of running native Hugging Face Transformers + FastAPI/Triton, what is your recommended high-throughput serving stack for serving Clef-Flash at scale?

Appreciate any guidance or pointers from the team or community!

ironbcc

2 days ago

@Vishva007 If stock vLLM support is the immediate requirement, there is another implementation approach: score the next-token logits restricted to the decision's option keys, rather than serving a custom joint head. I built Seb-9B around that approach: https://huggingface.co/ironbcc/seb-9b

The card includes a vLLM chat API example with constrained choices and option logprobs. It handles one question per request, with up to 20 choice options; that differs from Clef's joint multi-question design. Released-checkpoint GPU throughput measurements are still pending, so I can't claim it solves your high-concurrency requirement yet. It may be useful as a stock-vLLM baseline while the Clef integration question is worked through.

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