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, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("MikeTeng/git-base-naruto")
model = AutoModelForMultimodalLM.from_pretrained("MikeTeng/git-base-naruto", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

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 / README.md

MikeTeng

End of training

93252d3 verified about 2 years ago

preview code

Raw

History Blame Contribute Delete

2.22 kB

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

	# git-base-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: 0.0529
	- 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