Instructions to use ChayanM/Image-Captioning-Output with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChayanM/Image-Captioning-Output with Transformers:

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

pipe = pipeline("image-text-to-text", model="ChayanM/Image-Captioning-Output")

# Load model directly
from transformers import AutoTokenizer, AutoModelForImageTextToText

tokenizer = AutoTokenizer.from_pretrained("ChayanM/Image-Captioning-Output")
model = AutoModelForImageTextToText.from_pretrained("ChayanM/Image-Captioning-Output")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use ChayanM/Image-Captioning-Output with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChayanM/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": "ChayanM/Image-Captioning-Output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChayanM/Image-Captioning-Output

SGLang

How to use ChayanM/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 "ChayanM/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": "ChayanM/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 "ChayanM/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": "ChayanM/Image-Captioning-Output",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChayanM/Image-Captioning-Output with Docker Model Runner:
```
docker model run hf.co/ChayanM/Image-Captioning-Output
```

Image-Captioning-Output / README.md

ChayanM

Image-Captioning-Output

7065295 verified over 2 years ago

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	---
	tags:
	- generated_from_trainer
	datasets:
	- coco_dataset_script
	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 the coco_dataset_script dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3319
	- Rouge1: 21.9307
	- Rouge2: 4.1909
	- Rougel: 20.068
	- Rougelsum: 19.9653
	- Gen Len: 12.0625

	## 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 \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:-------:\|:------:\|:-------:\|:---------:\|:-------:\|
	\| No log \| 1.0 \| 20 \| 0.3359 \| 18.236 \| 0.5556 \| 18.2694 \| 18.255 \| 7.0 \|
	\| No log \| 2.0 \| 40 \| 0.3315 \| 19.2924 \| 3.6258 \| 18.3375 \| 18.3568 \| 14.1875 \|
	\| No log \| 3.0 \| 60 \| 0.3319 \| 21.9307 \| 4.1909 \| 20.068 \| 19.9653 \| 12.0625 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.1.0+cu121
	- Datasets 2.16.1
	- Tokenizers 0.15.0