Instructions to use workwithHasnain/image-caption-generator with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use workwithHasnain/image-caption-generator with Transformers:

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

pipe = pipeline("image-text-to-text", model="workwithHasnain/image-caption-generator")

# Load model directly
from transformers import AutoTokenizer, AutoModelForMultimodalLM

tokenizer = AutoTokenizer.from_pretrained("workwithHasnain/image-caption-generator")
model = AutoModelForMultimodalLM.from_pretrained("workwithHasnain/image-caption-generator", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use workwithHasnain/image-caption-generator with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/workwithHasnain/image-caption-generator

SGLang

How to use workwithHasnain/image-caption-generator 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 "workwithHasnain/image-caption-generator" \
    --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": "workwithHasnain/image-caption-generator",
		"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 "workwithHasnain/image-caption-generator" \
        --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": "workwithHasnain/image-caption-generator",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use workwithHasnain/image-caption-generator with Docker Model Runner:
```
docker model run hf.co/workwithHasnain/image-caption-generator
```

image-caption-generator / README.md

workwithHasnain

Image-to-recipe

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	---
	tags:
	- generated_from_trainer
	model-index:
	- name: image-caption-generator
	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-caption-generator

	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: 0.2180

	## 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: 32
	- eval_batch_size: 32
	- seed: 42
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 18

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|
	\| No log \| 1.0 \| 140 \| 0.9561 \|
	\| No log \| 2.0 \| 280 \| 0.8228 \|
	\| No log \| 3.0 \| 420 \| 0.7362 \|
	\| 1.0135 \| 4.0 \| 560 \| 0.6639 \|
	\| 1.0135 \| 5.0 \| 700 \| 0.5998 \|
	\| 1.0135 \| 6.0 \| 840 \| 0.5435 \|
	\| 1.0135 \| 7.0 \| 980 \| 0.4893 \|
	\| 0.6775 \| 8.0 \| 1120 \| 0.4445 \|
	\| 0.6775 \| 9.0 \| 1260 \| 0.4032 \|
	\| 0.6775 \| 10.0 \| 1400 \| 0.3637 \|
	\| 0.5059 \| 11.0 \| 1540 \| 0.3269 \|
	\| 0.5059 \| 12.0 \| 1680 \| 0.3013 \|
	\| 0.5059 \| 13.0 \| 1820 \| 0.2749 \|
	\| 0.5059 \| 14.0 \| 1960 \| 0.2553 \|
	\| 0.389 \| 15.0 \| 2100 \| 0.2395 \|
	\| 0.389 \| 16.0 \| 2240 \| 0.2280 \|
	\| 0.389 \| 17.0 \| 2380 \| 0.2214 \|
	\| 0.3218 \| 18.0 \| 2520 \| 0.2180 \|


	### Framework versions

	- Transformers 4.39.3
	- Pytorch 2.1.2
	- Datasets 2.18.0
	- Tokenizers 0.15.2