Instructions to use EditScore/EditScore-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EditScore/EditScore-7B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("/share/project/luoxin/huggingface/hub/models--Qwen--Qwen2.5-VL-7B-Instruct/snapshots/cc594898137f460bfe9f0759e9844b3ce807cfb5")
model = PeftModel.from_pretrained(base_model, "EditScore/EditScore-7B")

Transformers

How to use EditScore/EditScore-7B with Transformers:

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

pipe = pipeline("text-generation", model="EditScore/EditScore-7B")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EditScore/EditScore-7B", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EditScore/EditScore-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EditScore/EditScore-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EditScore/EditScore-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EditScore/EditScore-7B

SGLang

How to use EditScore/EditScore-7B 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 "EditScore/EditScore-7B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EditScore/EditScore-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "EditScore/EditScore-7B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EditScore/EditScore-7B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EditScore/EditScore-7B with Docker Model Runner:
```
docker model run hf.co/EditScore/EditScore-7B
```

Update pipeline tag, add paper ID, abstract, and GitHub link

by nielsr HF Staff - opened Oct 1, 2025

base: refs/heads/main

←

from: refs/pr/1

Discussion Files changed

+13

-2

nielsr

Oct 1, 2025

This PR updates the model card for the EditScore model to improve its discoverability and provide more comprehensive information for users.

Key changes include:

Updated pipeline_tag: Changed from text-generation to image-text-to-text to accurately reflect the model's functionality as a reward model for instruction-guided image editing, which takes both images and text as input to produce a textual score.
Added paper metadata: Included the Hugging Face paper ID 2509.23909 in the metadata for better integration with the Hugging Face Hub.
Added Paper Abstract: Incorporated the paper's abstract into a dedicated section to give users a quick overview of the model's purpose and methodology.
Added Code Repository Link: Provided a direct link to the official GitHub repository for easy access to the source code and further resources.

These changes enhance the model card's clarity and ensure it meets best practices for documentation on the Hugging Face Hub.

Update pipeline tag, add paper ID, abstract, and GitHub link5a3ea835

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