Instructions to use HuggingFaceM4/VLM_WebSight_finetuned with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use HuggingFaceM4/VLM_WebSight_finetuned with Transformers:

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

pipe = pipeline("text-generation", model="HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("HuggingFaceM4/VLM_WebSight_finetuned", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use HuggingFaceM4/VLM_WebSight_finetuned with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "HuggingFaceM4/VLM_WebSight_finetuned"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "HuggingFaceM4/VLM_WebSight_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned

SGLang

How to use HuggingFaceM4/VLM_WebSight_finetuned 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 "HuggingFaceM4/VLM_WebSight_finetuned" \
    --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": "HuggingFaceM4/VLM_WebSight_finetuned",
		"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 "HuggingFaceM4/VLM_WebSight_finetuned" \
        --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": "HuggingFaceM4/VLM_WebSight_finetuned",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use HuggingFaceM4/VLM_WebSight_finetuned with Docker Model Runner:
```
docker model run hf.co/HuggingFaceM4/VLM_WebSight_finetuned
```

VictorSanh commited on Jan 10, 2024

Commit

f653a92

1 Parent(s): b240ec1

ditch some unecessary parts ?

Browse files

Files changed (1) hide show

preprocessor_config.json +2 -2

preprocessor_config.json CHANGED Viewed

@@ -9,12 +9,12 @@
     0.5,
     0.5
   ],
-  "image_processor_class": "image_processing_img2html.Img2HTMLImageProcessor",
   "image_size": 960,
   "image_std": [
     0.5,
     0.5,
     0.5
   ],
-  "processor_class": "processing_img2html.Img2HTMLProcessor"
 }

     0.5,
     0.5
   ],
+  "image_processor_type": "Img2HTMLImageProcessor",
   "image_size": 960,
   "image_std": [
     0.5,
     0.5,
     0.5
   ],
+  "processor_class": "Img2HTMLProcessor"
 }