Instructions to use John6666/gokaygokay-Florence-2-SD3-Captioner-8bit with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use John6666/gokaygokay-Florence-2-SD3-Captioner-8bit with Transformers:

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

pipe = pipeline("image-text-to-text", model="John6666/gokaygokay-Florence-2-SD3-Captioner-8bit", trust_remote_code=True)

# Load model directly
from transformers import AutoProcessor, AutoModelForMultimodalLM

processor = AutoProcessor.from_pretrained("John6666/gokaygokay-Florence-2-SD3-Captioner-8bit", trust_remote_code=True)
model = AutoModelForMultimodalLM.from_pretrained("John6666/gokaygokay-Florence-2-SD3-Captioner-8bit", trust_remote_code=True)

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use John6666/gokaygokay-Florence-2-SD3-Captioner-8bit with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/John6666/gokaygokay-Florence-2-SD3-Captioner-8bit

SGLang

How to use John6666/gokaygokay-Florence-2-SD3-Captioner-8bit 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 "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit" \
    --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": "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit",
		"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 "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit" \
        --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": "John6666/gokaygokay-Florence-2-SD3-Captioner-8bit",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use John6666/gokaygokay-Florence-2-SD3-Captioner-8bit with Docker Model Runner:
```
docker model run hf.co/John6666/gokaygokay-Florence-2-SD3-Captioner-8bit
```

Original model is here. Tagger for local environment is here.

# recipe
from transformers import AutoModelForCausalLM, AutoProcessor, BitsAndBytesConfig
import transformers
import torch
import json

model_id = 'gokaygokay/Florence-2-SD3-Captioner'
save_path = 'gokaygokay-Florence-2-SD3-Captioner-8bit'

processor = AutoProcessor.from_pretrained(model_id, trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    model_id, 
    trust_remote_code=True,
    torch_dtype=torch.float32,
    low_cpu_mem_usage=True,
    quantization_config=BitsAndBytesConfig(
        load_in_8bit=True,
        llm_int8_threshold=6.0,
        llm_int8_enable_fp32_cpu_offload=True,
        llm_int8_skip_modules=['lm_head'],
    ),
)

processor.save_pretrained(save_path)
model.save_pretrained(save_path, safe_serialization=True)

config = {}
with open(f'{save_path}/config.json') as f:
    config = json.load(f)
config['vision_config']['model_type'] = 'davit'
with open(f'{save_path}/config.json', 'w') as f:
    json.dump(config, f, indent=2)

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Safetensors

Model size

0.3B params

Tensor type

F32

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Image-Text-to-Text

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