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

Libraries

How to use lenashamseldin/git-base-floors with Transformers:

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

pipe = pipeline("image-text-to-text", model="lenashamseldin/git-base-floors")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("lenashamseldin/git-base-floors")
model = AutoModelForImageTextToText.from_pretrained("lenashamseldin/git-base-floors")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use lenashamseldin/git-base-floors with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/lenashamseldin/git-base-floors

SGLang

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

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

git-base-floors

This model is a fine-tuned version of microsoft/git-base on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.0322
Wer Score: 1.8463

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: 8
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 2
total_train_batch_size: 16
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 50

Training results

Training Loss	Epoch	Step	Validation Loss	Wer Score
6.8447	4.76	50	4.1895	2.2204
2.1937	9.52	100	0.4359	3.1889
0.1385	14.29	150	0.0360	0.1426
0.0184	19.05	200	0.0307	0.6185
0.0117	23.81	250	0.0287	0.1741
0.0095	28.57	300	0.0293	1.1463
0.0081	33.33	350	0.0292	0.8463
0.0061	38.1	400	0.0312	2.0333
0.0044	42.86	450	0.0322	1.9204
0.0032	47.62	500	0.0322	1.8463

Framework versions

Transformers 4.38.2
Pytorch 2.0.1
Datasets 2.18.0
Tokenizers 0.15.2

Downloads last month: 4

Safetensors

Model size

0.2B params

Tensor type

F32

Inference Providers NEW

Image-Text-to-Text

This model isn't deployed by any Inference Provider. 🙋 Ask for provider support

Model tree for lenashamseldin/git-base-floors

Base model

microsoft/git-base

Finetuned

(117)

this model