Instructions to use EditScore/EditScore-72B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EditScore/EditScore-72B with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-VL-72B-Instruct")
model = PeftModel.from_pretrained(base_model, "EditScore/EditScore-72B")

Transformers

How to use EditScore/EditScore-72B with Transformers:

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

pipe = pipeline("text-generation", model="EditScore/EditScore-72B")

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EditScore/EditScore-72B", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use EditScore/EditScore-72B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EditScore/EditScore-72B"
# 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-72B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EditScore/EditScore-72B

SGLang

How to use EditScore/EditScore-72B 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-72B" \
    --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-72B",
		"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-72B" \
        --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-72B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EditScore/EditScore-72B with Docker Model Runner:
```
docker model run hf.co/EditScore/EditScore-72B
```

EditScore-72B / adapter_model.safetensors

sienna223

upload dataset folder to repo

d3de53b verified about 1 year ago

History Blame Contribute Delete

842 MB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: b07ff059ec3d6285a27bf99a4ad3043039732f80222b7bf19bcfec47c100e557
Size of remote file:: 842 MB
SHA256:: 9ffe4e3f9f29a2e942dca4efea670773f7a67f9473dd8c79809b1e3ec585bd68

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.