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

Libraries

How to use MikeTeng/git-base-naruto with Transformers:

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

pipe = pipeline("image-text-to-text", model="MikeTeng/git-base-naruto")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("MikeTeng/git-base-naruto")
model = AutoModelForImageTextToText.from_pretrained("MikeTeng/git-base-naruto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use MikeTeng/git-base-naruto with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/MikeTeng/git-base-naruto

SGLang

How to use MikeTeng/git-base-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 "MikeTeng/git-base-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": "MikeTeng/git-base-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 "MikeTeng/git-base-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": "MikeTeng/git-base-naruto",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use MikeTeng/git-base-naruto with Docker Model Runner:
```
docker model run hf.co/MikeTeng/git-base-naruto
```

git-base-naruto

This model is a fine-tuned version of microsoft/git-base on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.0529
Wer Score: 1.4091

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: 50
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
7.3586	3.7037	50	4.5383	8.8030
2.3507	7.4074	100	0.4544	0.4697
0.1281	11.1111	150	0.0543	0.5152
0.0161	14.8148	200	0.0491	0.4545
0.0115	18.5185	250	0.0501	0.4394
0.0099	22.2222	300	0.0528	0.4697
0.0085	25.9259	350	0.0536	0.4697
0.0075	29.6296	400	0.0532	0.4848
0.0068	33.3333	450	0.0520	0.4697
0.0061	37.0370	500	0.0528	0.6818
0.0054	40.7407	550	0.0530	0.8030
0.0044	44.4444	600	0.0535	1.2121
0.0038	48.1481	650	0.0529	1.4091

Framework versions

Transformers 4.40.2
Pytorch 2.3.0+cu121
Datasets 2.19.1
Tokenizers 0.19.1

Downloads last month: 2

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

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Model tree for MikeTeng/git-base-naruto

Base model

microsoft/git-base

Finetuned

(117)

this model