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")

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
```

image-captioning-output / README.md

NourFakih

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	---
	license: apache-2.0
	base_model: nlpconnect/vit-gpt2-image-captioning
	tags:
	- generated_from_trainer
	metrics:
	- rouge
	model-index:
	- name: image-captioning-output
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# image-captioning-output

	This model is a fine-tuned version of [nlpconnect/vit-gpt2-image-captioning](https://huggingface.co/nlpconnect/vit-gpt2-image-captioning) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5164
	- Rouge1: 35.5267
	- Rouge2: 12.254
	- Rougel: 32.968
	- Rougelsum: 32.9723
	- Gen Len: 12.395

	## Model description

	More information needed

	## Intended uses & limitations

	More information needed

	## Training and evaluation data

	More information needed

	## Training procedure

	### Training hyperparameters

	The following hyperparameters were used during training:
	- learning_rate: 5e-05
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 3.0

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Rouge1 \| Rouge2 \| Rougel \| Rougelsum \| Gen Len \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:-------:\|:-------:\|:---------:\|:-------:\|
	\| 0.5193 \| 0.25 \| 500 \| 0.5171 \| 33.0319 \| 10.364 \| 30.6939 \| 30.6888 \| 12.1 \|
	\| 0.4842 \| 0.5 \| 1000 \| 0.5102 \| 33.7318 \| 10.8199 \| 31.1842 \| 31.18 \| 11.3 \|
	\| 0.4724 \| 0.75 \| 1500 \| 0.5028 \| 34.6981 \| 11.4074 \| 31.9128 \| 31.9158 \| 12.02 \|
	\| 0.4632 \| 1.0 \| 2000 \| 0.5012 \| 35.9443 \| 12.8742 \| 33.4061 \| 33.377 \| 11.04 \|
	\| 0.377 \| 1.25 \| 2500 \| 0.5026 \| 35.7745 \| 12.2309 \| 33.3234 \| 33.3353 \| 11.735 \|
	\| 0.3819 \| 1.5 \| 3000 \| 0.5018 \| 36.0145 \| 13.0296 \| 33.5985 \| 33.6182 \| 12.285 \|
	\| 0.3788 \| 1.75 \| 3500 \| 0.5030 \| 35.9016 \| 12.5276 \| 33.4995 \| 33.5033 \| 11.305 \|
	\| 0.3654 \| 2.0 \| 4000 \| 0.5020 \| 36.2476 \| 12.945 \| 33.6453 \| 33.6595 \| 11.9 \|
	\| 0.3102 \| 2.25 \| 4500 \| 0.5146 \| 36.1507 \| 13.0072 \| 33.3889 \| 33.3786 \| 12.305 \|
	\| 0.3137 \| 2.5 \| 5000 \| 0.5166 \| 35.7413 \| 12.5693 \| 33.2646 \| 33.2508 \| 12.71 \|
	\| 0.3111 \| 2.75 \| 5500 \| 0.5171 \| 35.5658 \| 12.511 \| 33.0581 \| 33.0518 \| 12.55 \|
	\| 0.3023 \| 3.0 \| 6000 \| 0.5164 \| 35.5267 \| 12.254 \| 32.968 \| 32.9723 \| 12.395 \|


	### Framework versions

	- Transformers 4.40.0
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.0
	- Tokenizers 0.19.1