Instructions to use mohsinshah/git-base-dummy-3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohsinshah/git-base-dummy-3 with Transformers:

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

pipe = pipeline("image-text-to-text", model="mohsinshah/git-base-dummy-3")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("mohsinshah/git-base-dummy-3")
model = AutoModelForMultimodalLM.from_pretrained("mohsinshah/git-base-dummy-3")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mohsinshah/git-base-dummy-3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohsinshah/git-base-dummy-3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "mohsinshah/git-base-dummy-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohsinshah/git-base-dummy-3

SGLang

How to use mohsinshah/git-base-dummy-3 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 "mohsinshah/git-base-dummy-3" \
    --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": "mohsinshah/git-base-dummy-3",
		"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 "mohsinshah/git-base-dummy-3" \
        --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": "mohsinshah/git-base-dummy-3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohsinshah/git-base-dummy-3 with Docker Model Runner:
```
docker model run hf.co/mohsinshah/git-base-dummy-3
```

git-base-500img-dataset

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: 0.4161
Wer Score: 2.0379

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: 4
total_train_batch_size: 32
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
7.0698	3.23	50	4.5086	2.5298
2.6252	6.45	100	0.9823	2.2976
0.5497	9.68	150	0.4681	1.6707
0.2558	12.9	200	0.4162	1.7907
0.1551	16.13	250	0.4052	2.0984
0.1041	19.35	300	0.4054	2.0984
0.0764	22.58	350	0.4088	2.0576
0.0581	25.81	400	0.4054	2.0899
0.0462	29.03	450	0.4092	2.0484
0.0382	32.26	500	0.4118	2.1387
0.0329	35.48	550	0.4126	2.1315
0.0275	38.71	600	0.4139	2.0114
0.0255	41.94	650	0.4173	2.0098
0.0234	45.16	700	0.4155	2.0206
0.0226	48.39	750	0.4161	2.0379

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.14.3
Tokenizers 0.13.3

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