Instructions to use Image-Captioning-ML/image-captioning-output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Image-Captioning-ML/image-captioning-output with Transformers:

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

pipe = pipeline("image-text-to-text", model="Image-Captioning-ML/image-captioning-output")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Image-Captioning-ML/image-captioning-output")
model = AutoModelForMultimodalLM.from_pretrained("Image-Captioning-ML/image-captioning-output", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Image-Captioning-ML/image-captioning-output with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Image-Captioning-ML/image-captioning-output

SGLang

How to use Image-Captioning-ML/image-captioning-output 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 "Image-Captioning-ML/image-captioning-output" \
    --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": "Image-Captioning-ML/image-captioning-output",
		"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 "Image-Captioning-ML/image-captioning-output" \
        --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": "Image-Captioning-ML/image-captioning-output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Image-Captioning-ML/image-captioning-output with Docker Model Runner:
```
docker model run hf.co/Image-Captioning-ML/image-captioning-output
```

NourFakih commited on Apr 25, 2024

Commit

54502f4

verified ·

1 Parent(s): d75724c

Training in progress, step 2500

Browse files

Files changed (2) hide show

model.safetensors +1 -1
runs/Apr25_21-18-03_2310d6ab4b0d/events.out.tfevents.1714079921.2310d6ab4b0d.34.3 +2 -2

model.safetensors CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:49f64f20064b875e137f42754fe2ff5dcc41376e90d823200f612319e8100a9d
 size 956835520

 version https://git-lfs.github.com/spec/v1
+oid sha256:8d09a8a193fa0a1f0105c5ced3204d3c9947d7be7f7cfc0ce2e5450b617e08b5
 size 956835520

runs/Apr25_21-18-03_2310d6ab4b0d/events.out.tfevents.1714079921.2310d6ab4b0d.34.3 CHANGED Viewed

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:6868d5affcaf6a08b39eb2307b92d076d7b3f2b1cc7be55016dd81f9548bee53
-size 12086

 version https://git-lfs.github.com/spec/v1
+oid sha256:04a15dc899fbab4122ce6e9c15e8a656bc0bab9ea79b0edea60143fbbec460f2
+size 12822