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

Libraries

How to use Winmodel/image-captioning-output with Transformers:

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

pipe = pipeline("image-text-to-text", model="Winmodel/image-captioning-output")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("Winmodel/image-captioning-output")
model = AutoModelForMultimodalLM.from_pretrained("Winmodel/image-captioning-output")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use Winmodel/image-captioning-output with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Winmodel/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": "Winmodel/image-captioning-output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Winmodel/image-captioning-output

SGLang

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

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

image-captioning-output / README.md

Winmodel

End of training

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	---
	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 [](https://huggingface.co/) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.2689
	- Rouge1: 0.0
	- Rouge2: 0.0
	- Rougel: 0.0
	- Rougelsum: 0.0
	- Gen Len: 9.707

	## 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: 16
	- eval_batch_size: 32
	- 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|:------:\|:------:\|:---------:\|:-------:\|
	\| No log \| 1.0 \| 375 \| 0.3010 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 10.073 \|
	\| 0.391 \| 2.0 \| 750 \| 0.2773 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 12.193 \|
	\| 0.2852 \| 3.0 \| 1125 \| 0.2689 \| 0.0 \| 0.0 \| 0.0 \| 0.0 \| 9.707 \|


	### Framework versions

	- Transformers 4.38.2
	- Pytorch 2.2.1+cu121
	- Datasets 2.19.0
	- Tokenizers 0.15.2