Instructions to use zhaohungtan1209/created_model with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use zhaohungtan1209/created_model with Transformers:

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

pipe = pipeline("image-text-to-text", model="zhaohungtan1209/created_model", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("zhaohungtan1209/created_model", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("zhaohungtan1209/created_model", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use zhaohungtan1209/created_model with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "zhaohungtan1209/created_model"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "zhaohungtan1209/created_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/zhaohungtan1209/created_model

SGLang

How to use zhaohungtan1209/created_model 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 "zhaohungtan1209/created_model" \
    --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": "zhaohungtan1209/created_model",
		"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 "zhaohungtan1209/created_model" \
        --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": "zhaohungtan1209/created_model",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use zhaohungtan1209/created_model with Docker Model Runner:
```
docker model run hf.co/zhaohungtan1209/created_model
```

created_model

This model is a fine-tuned version of microsoft/Florence-2-large-ft on an unknown dataset. It achieves the following results on the evaluation set:

Loss: 1.7657

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: 0.0001
train_batch_size: 2
eval_batch_size: 1
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: 10

Training results

Training Loss	Epoch	Step	Validation Loss
2.5758	1.0	22	1.7627
1.1636	2.0	44	2.3089
0.9265	3.0	66	1.7357
0.7011	4.0	88	1.6376
0.6003	5.0	110	1.5972
0.4465	6.0	132	1.9380
0.2891	7.0	154	2.3485
0.2896	8.0	176	1.7245
0.2192	9.0	198	1.7667
0.1263	10.0	220	1.7657

Framework versions

Transformers 4.46.0
Pytorch 2.3.1+cu121
Tokenizers 0.20.2

Downloads last month: 2

Safetensors

Model size

0.8B 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 zhaohungtan1209/created_model

Base model

microsoft/Florence-2-large-ft

Finetuned

(9)

this model