Instructions to use jesusgs01/results_final_fold_4 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use jesusgs01/results_final_fold_4 with Transformers:

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

pipe = pipeline("image-text-to-text", model="jesusgs01/results_final_fold_4")

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("jesusgs01/results_final_fold_4")
model = AutoModelForMultimodalLM.from_pretrained("jesusgs01/results_final_fold_4")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use jesusgs01/results_final_fold_4 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "jesusgs01/results_final_fold_4"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "jesusgs01/results_final_fold_4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/jesusgs01/results_final_fold_4

SGLang

How to use jesusgs01/results_final_fold_4 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 "jesusgs01/results_final_fold_4" \
    --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": "jesusgs01/results_final_fold_4",
		"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 "jesusgs01/results_final_fold_4" \
        --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": "jesusgs01/results_final_fold_4",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use jesusgs01/results_final_fold_4 with Docker Model Runner:
```
docker model run hf.co/jesusgs01/results_final_fold_4
```

results_final_fold_4

This model is a fine-tuned version of google/paligemma-3b-pt-224 on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 0.1638

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: 1e-05
train_batch_size: 8
eval_batch_size: 8
seed: 42
optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
lr_scheduler_type: linear
num_epochs: 15

Training results

Training Loss	Epoch	Step	Validation Loss
0.2131	1.0	2091	0.1682
0.2028	2.0	4182	0.1802
0.2011	3.0	6273	0.1676
0.1997	4.0	8364	0.1743
0.1912	5.0	10455	0.1655
0.1934	6.0	12546	0.1650
0.1947	7.0	14637	0.1658
0.1916	8.0	16728	0.1656
0.1934	9.0	18819	0.1647
0.1818	10.0	20910	0.1645
0.2004	11.0	23001	0.1638
0.194	12.0	25092	0.1646
0.1805	13.0	27183	0.1645
0.1805	14.0	29274	0.1643
0.188	15.0	31365	0.1646

Framework versions

Transformers 4.51.0.dev0
Pytorch 2.1.2+cu121
Datasets 3.5.0
Tokenizers 0.21.1

Downloads last month: 1

Safetensors

Model size

3B params

Tensor type

BF16

Inference Providers NEW

Image-Text-to-Text

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Model tree for jesusgs01/results_final_fold_4

Base model

google/paligemma-3b-pt-224

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