Instructions to use ChiJuiChen/GenerativeImage2Text-naruto with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ChiJuiChen/GenerativeImage2Text-naruto with Transformers:

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

pipe = pipeline("image-text-to-text", model="ChiJuiChen/GenerativeImage2Text-naruto")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("ChiJuiChen/GenerativeImage2Text-naruto")
model = AutoModelForMultimodalLM.from_pretrained("ChiJuiChen/GenerativeImage2Text-naruto", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ChiJuiChen/GenerativeImage2Text-naruto with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ChiJuiChen/GenerativeImage2Text-naruto"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ChiJuiChen/GenerativeImage2Text-naruto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/ChiJuiChen/GenerativeImage2Text-naruto

SGLang

How to use ChiJuiChen/GenerativeImage2Text-naruto 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 "ChiJuiChen/GenerativeImage2Text-naruto" \
    --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": "ChiJuiChen/GenerativeImage2Text-naruto",
		"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 "ChiJuiChen/GenerativeImage2Text-naruto" \
        --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": "ChiJuiChen/GenerativeImage2Text-naruto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use ChiJuiChen/GenerativeImage2Text-naruto with Docker Model Runner:
```
docker model run hf.co/ChiJuiChen/GenerativeImage2Text-naruto
```

GenerativeImage2Text-naruto / README.md

ChiJuiChen

End of training

f090ea6 verified about 2 years ago

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	---
	license: mit
	base_model: microsoft/git-base
	tags:
	- generated_from_trainer
	model-index:
	- name: GenerativeImage2Text-naruto
	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. -->

	# GenerativeImage2Text-naruto

	This model is a fine-tuned version of [microsoft/git-base](https://huggingface.co/microsoft/git-base) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.0544
	- Wer Score: 2.6810

	## 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: 2
	- eval_batch_size: 2
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 4
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- num_epochs: 30
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer Score \|
	\|:-------------:\|:-------:\|:----:\|:---------------:\|:---------:\|
	\| 7.2829 \| 1.8182 \| 50 \| 4.4333 \| 11.5086 \|
	\| 2.2118 \| 3.6364 \| 100 \| 0.3655 \| 1.1034 \|
	\| 0.1089 \| 5.4545 \| 150 \| 0.0428 \| 1.0259 \|
	\| 0.0223 \| 7.2727 \| 200 \| 0.0421 \| 0.4655 \|
	\| 0.0162 \| 9.0909 \| 250 \| 0.0430 \| 0.4224 \|
	\| 0.0139 \| 10.9091 \| 300 \| 0.0434 \| 0.9569 \|
	\| 0.0126 \| 12.7273 \| 350 \| 0.0455 \| 0.8534 \|
	\| 0.0115 \| 14.5455 \| 400 \| 0.0457 \| 3.2845 \|
	\| 0.0106 \| 16.3636 \| 450 \| 0.0490 \| 2.3190 \|
	\| 0.0096 \| 18.1818 \| 500 \| 0.0515 \| 2.2241 \|
	\| 0.0094 \| 20.0 \| 550 \| 0.0520 \| 1.9569 \|
	\| 0.0087 \| 21.8182 \| 600 \| 0.0540 \| 4.2328 \|
	\| 0.0084 \| 23.6364 \| 650 \| 0.0539 \| 1.8448 \|
	\| 0.008 \| 25.4545 \| 700 \| 0.0546 \| 2.5431 \|
	\| 0.0077 \| 27.2727 \| 750 \| 0.0542 \| 2.7672 \|
	\| 0.0072 \| 29.0909 \| 800 \| 0.0544 \| 2.6810 \|


	### Framework versions

	- Transformers 4.40.2
	- Pytorch 2.3.0+cu121
	- Datasets 2.19.1
	- Tokenizers 0.19.1