README.md

---
license: apache-2.0
datasets:
- google/docci
- google/imageinwords
- ProGamerGov/synthetic-dataset-1m-dalle3-high-quality-captions
language:
- en
library_name: transformers
pipeline_tag: image-text-to-text
tags:
- art
base_model: gokaygokay/Florence-2-SD3-Captioner
inference: false
---

Original model is [here](https://huggingface.co/gokaygokay/Florence-2-SD3-Captioner).
Tagger for local environment is [here](https://huggingface.co/John6666/local_gokaygokay_Florence-2-SD3-Captioner_Tagger).

```python
# 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)
```