Instructions to use EricChan1122/florence2-parasitic-egg-detection-version3 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use EricChan1122/florence2-parasitic-egg-detection-version3 with PEFT:

from peft import PeftModel
from transformers import AutoModelForCausalLM

base_model = AutoModelForCausalLM.from_pretrained("microsoft/Florence-2-large")
model = PeftModel.from_pretrained(base_model, "EricChan1122/florence2-parasitic-egg-detection-version3")

Transformers

How to use EricChan1122/florence2-parasitic-egg-detection-version3 with Transformers:

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

pipe = pipeline("text-generation", model="EricChan1122/florence2-parasitic-egg-detection-version3")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("EricChan1122/florence2-parasitic-egg-detection-version3", dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use EricChan1122/florence2-parasitic-egg-detection-version3 with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "EricChan1122/florence2-parasitic-egg-detection-version3"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "EricChan1122/florence2-parasitic-egg-detection-version3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/EricChan1122/florence2-parasitic-egg-detection-version3

SGLang

How to use EricChan1122/florence2-parasitic-egg-detection-version3 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 "EricChan1122/florence2-parasitic-egg-detection-version3" \
    --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": "EricChan1122/florence2-parasitic-egg-detection-version3",
		"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 "EricChan1122/florence2-parasitic-egg-detection-version3" \
        --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": "EricChan1122/florence2-parasitic-egg-detection-version3",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use EricChan1122/florence2-parasitic-egg-detection-version3 with Docker Model Runner:
```
docker model run hf.co/EricChan1122/florence2-parasitic-egg-detection-version3
```

florence2-parasitic-egg-detection-version3

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

Loss: 0.8939

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: 2e-05
train_batch_size: 1
eval_batch_size: 8
seed: 42
gradient_accumulation_steps: 8
total_train_batch_size: 8
optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
num_epochs: 3
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss
0.7932	0.2424	200	0.9498
0.8312	0.4848	400	0.9952
0.7408	0.7273	600	0.9016
0.6968	0.9697	800	1.0748
0.8033	1.2121	1000	1.0826
0.7996	1.4545	1200	0.8847
0.7384	1.6970	1400	0.9686
0.7812	1.9394	1600	0.8919
0.6949	2.1818	1800	0.9751
0.6564	2.4242	2000	0.9029
0.6975	2.6667	2200	0.8928
0.6555	2.9091	2400	0.8939

Framework versions

PEFT 0.18.1
Transformers 4.45.2
Pytorch 2.6.0+cu124
Datasets 4.5.0
Tokenizers 0.20.3

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Model tree for EricChan1122/florence2-parasitic-egg-detection-version3

Base model

microsoft/Florence-2-large

Adapter

(9)

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