Instructions to use thefertilityplan/ivf-bench-qwen9b-vlm-orpo with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use thefertilityplan/ivf-bench-qwen9b-vlm-orpo with Transformers:

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

pipe = pipeline("image-text-to-text", model="thefertilityplan/ivf-bench-qwen9b-vlm-orpo")
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("thefertilityplan/ivf-bench-qwen9b-vlm-orpo")
model = AutoModelForMultimodalLM.from_pretrained("thefertilityplan/ivf-bench-qwen9b-vlm-orpo", 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 thefertilityplan/ivf-bench-qwen9b-vlm-orpo with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "thefertilityplan/ivf-bench-qwen9b-vlm-orpo"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "thefertilityplan/ivf-bench-qwen9b-vlm-orpo",
		"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/thefertilityplan/ivf-bench-qwen9b-vlm-orpo

SGLang

How to use thefertilityplan/ivf-bench-qwen9b-vlm-orpo 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 "thefertilityplan/ivf-bench-qwen9b-vlm-orpo" \
    --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": "thefertilityplan/ivf-bench-qwen9b-vlm-orpo",
		"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 "thefertilityplan/ivf-bench-qwen9b-vlm-orpo" \
        --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": "thefertilityplan/ivf-bench-qwen9b-vlm-orpo",
		"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 thefertilityplan/ivf-bench-qwen9b-vlm-orpo with Docker Model Runner:
```
docker model run hf.co/thefertilityplan/ivf-bench-qwen9b-vlm-orpo
```

Stop bolding a Brier score that is not the best

by andrewmonostate - opened Aug 3

base: refs/heads/main

←

from: refs/pr/3

Discussion Files changed

-2

andrewmonostate

The Fertility Plan org Aug 3

Hugging Face CLI

The results table bolded this model's Brier of 0.245 while Sonnet 4.6's 0.242 sat unbolded three lines above prose that correctly says we are second. Bold was doing two jobs, marking our row on the name and reading as best-in-column on the numbers. Numbers in our row are now unbolded and the caption states that bold identifies our row only. Audited: no bolded number in the table is anything other than the best in its column.

Stop bolding a Brier score that is not the best1d334434

andrewmonostate changed pull request status to merged Aug 3

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