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
```

sienna223 commited on Sep 30, 2025

Commit

7d551a4

verified ·

1 Parent(s): ab9018c

Upload 4 files

Browse files

Files changed (5) hide show

.gitattributes +3 -0
assets/.DS_Store +0 -0
assets/figure_edit_results.png +3 -0
assets/logo.png +3 -0
assets/table_reward_model_results.png +3 -0

.gitattributes CHANGED Viewed

@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text

 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+assets/figure_edit_results.png filter=lfs diff=lfs merge=lfs -text
+assets/logo.png filter=lfs diff=lfs merge=lfs -text
+assets/table_reward_model_results.png filter=lfs diff=lfs merge=lfs -text

assets/.DS_Store ADDED Viewed

Binary file (6.15 kB). View file

assets/figure_edit_results.png ADDED Viewed

Git LFS Details

SHA256: 43382f61cb4875a077f38817d2126bb13a6967f8352d27d36d74662d38dc2b90
Pointer size: 131 Bytes
Size of remote file: 305 kB

assets/logo.png ADDED Viewed

Git LFS Details

SHA256: 170b16d70efc7058027b8c019779202950a48c500b191956d0fd829639adf5e0
Pointer size: 131 Bytes
Size of remote file: 195 kB

assets/table_reward_model_results.png ADDED Viewed

Git LFS Details

SHA256: 52c18933854a5635607cbd0797be91e8c29365cda07ee9c0afae69651c4f7d14
Pointer size: 131 Bytes
Size of remote file: 227 kB