Instructions to use gokaygokay/Flux-Prompt-Enhance with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use gokaygokay/Flux-Prompt-Enhance with Transformers:

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

pipe = pipeline("text-generation", model="gokaygokay/Flux-Prompt-Enhance")

# Load model directly
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM

tokenizer = AutoTokenizer.from_pretrained("gokaygokay/Flux-Prompt-Enhance")
model = AutoModelForSeq2SeqLM.from_pretrained("gokaygokay/Flux-Prompt-Enhance", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use gokaygokay/Flux-Prompt-Enhance with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/gokaygokay/Flux-Prompt-Enhance

SGLang

How to use gokaygokay/Flux-Prompt-Enhance 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 "gokaygokay/Flux-Prompt-Enhance" \
    --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": "gokaygokay/Flux-Prompt-Enhance",
		"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 "gokaygokay/Flux-Prompt-Enhance" \
        --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": "gokaygokay/Flux-Prompt-Enhance",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use gokaygokay/Flux-Prompt-Enhance with Docker Model Runner:
```
docker model run hf.co/gokaygokay/Flux-Prompt-Enhance
```

Can you share some experience about how to train a model like this,thanks!

by seasnake - opened Aug 30, 2024

Discussion

seasnake

Aug 30, 2024

Can you share some experience about how to train a model like this,thanks!

gokaygokay

Owner Aug 30, 2024

•

edited Aug 30, 2024

Sure, you can look at this notebook, it is a simple seq2seq model fine tuning.

https://colab.research.google.com/drive/13qTF7Z_Lf-FO-7_j49NTUQWffi7m0_8K?usp=sharing

liu00

Dec 16, 2024

Whether to train with flux？

marduk191

Jul 29, 2025

Sure, you can look at this notebook, it is a simple seq2seq model fine tuning.

https://colab.research.google.com/drive/13qTF7Z_Lf-FO-7_j49NTUQWffi7m0_8K?usp=sharing

step 5 of this script is brroken ---------------------------------------------------------------------------
TypeError Traceback (most recent call last)
/tmp/ipython-input-4-1280422743.py in <cell line: 0>()
3 epochs = 5
4 model_name = f"{model_checkpoint}-prompt-enhancer"
----> 5 args = Seq2SeqTrainingArguments(
6 model_name,
7 evaluation_strategy="epoch",

TypeError: Seq2SeqTrainingArguments.init() got an unexpected keyword argument 'evaluation_strategy'

gokaygokay

Owner Jul 29, 2025

Probably needs old version of transformers

marduk191

Aug 1, 2025

pip install transformers[torch] makes it run

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