Instructions to use mohsinshah/git-base-coco-dummy-temp12 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mohsinshah/git-base-coco-dummy-temp12 with Transformers:

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

pipe = pipeline("image-text-to-text", model="mohsinshah/git-base-coco-dummy-temp12")

# Load model directly
from transformers import AutoProcessor, AutoModelForImageTextToText

processor = AutoProcessor.from_pretrained("mohsinshah/git-base-coco-dummy-temp12")
model = AutoModelForImageTextToText.from_pretrained("mohsinshah/git-base-coco-dummy-temp12")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use mohsinshah/git-base-coco-dummy-temp12 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "mohsinshah/git-base-coco-dummy-temp12"
# 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-coco-dummy-temp12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/mohsinshah/git-base-coco-dummy-temp12

SGLang

How to use mohsinshah/git-base-coco-dummy-temp12 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-coco-dummy-temp12" \
    --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-coco-dummy-temp12",
		"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-coco-dummy-temp12" \
        --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-coco-dummy-temp12",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use mohsinshah/git-base-coco-dummy-temp12 with Docker Model Runner:
```
docker model run hf.co/mohsinshah/git-base-coco-dummy-temp12
```

git-base-coco-dummy-temp12

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

Loss: 0.3199
Wer Score: 2.1457

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
7.2365	3.23	50	4.6009	2.7286
2.7457	6.45	100	0.9830	2.0444
0.5898	9.68	150	0.4162	1.9788
0.2845	12.9	200	0.3463	2.3103
0.1764	16.13	250	0.3227	2.3805
0.1162	19.35	300	0.3167	2.3759
0.0856	22.58	350	0.3144	2.2456
0.065	25.81	400	0.3138	2.3201
0.0513	29.03	450	0.3149	2.1917
0.0435	32.26	500	0.3163	2.1561
0.0363	35.48	550	0.3162	2.1466
0.0312	38.71	600	0.3179	2.1793
0.0286	41.94	650	0.3200	2.2413
0.0267	45.16	700	0.3204	2.1061
0.0255	48.39	750	0.3199	2.1457

Framework versions

Transformers 4.30.2
Pytorch 2.0.1+cu118
Datasets 2.14.3
Tokenizers 0.13.3

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