Instructions to use treadon/prompt-fungineer-355M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use treadon/prompt-fungineer-355M with Transformers:

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

pipe = pipeline("text-generation", model="treadon/prompt-fungineer-355M")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("treadon/prompt-fungineer-355M")
model = AutoModelForCausalLM.from_pretrained("treadon/prompt-fungineer-355M", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use treadon/prompt-fungineer-355M with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "treadon/prompt-fungineer-355M"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "treadon/prompt-fungineer-355M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/treadon/prompt-fungineer-355M

SGLang

How to use treadon/prompt-fungineer-355M 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 "treadon/prompt-fungineer-355M" \
    --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": "treadon/prompt-fungineer-355M",
		"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 "treadon/prompt-fungineer-355M" \
        --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": "treadon/prompt-fungineer-355M",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use treadon/prompt-fungineer-355M with Docker Model Runner:
```
docker model run hf.co/treadon/prompt-fungineer-355M
```

Prompt Fungineer 355M

Follow @treadon on X and treadon on Hugging Face for more AI experiments, evals, and projects.

A GPT-2 based model (355M parameters) fine-tuned to expand short image prompts into detailed, creative prompts for AI image generators like Midjourney, DALL-E, Stable Diffusion, and others.

What It Does

Give it a simple prompt like "a cat on a mountain" and it generates detailed, production-ready prompts with lighting, composition, camera settings, and artistic style suggestions.

Input:

a sunset over the ocean

Output:

A breathtaking sunset over a calm ocean, golden hour lighting casting
warm amber reflections across gentle waves, wispy cirrus clouds painted
in shades of coral and violet, shot with a Canon EOS R5, 24-70mm f/2.8
lens, long exposure capturing silky smooth water, dramatic sky gradient
from deep blue to fiery orange, cinematic composition with rule of thirds

How It Works

The model was fine-tuned on a curated dataset of high-quality image prompts (treadon/image_prompts). It learned the structure and vocabulary that image generation models respond to best.

The input format uses structured tags:

BRF: — Brief/base prompt (your input)
POS: — Positive prompt (detailed expansion)
ENH: — Enhancers (camera settings, lighting, technical details)
INS: — Inspiration (photographer/artist references)
NEG: — Negative prompt (what to avoid)

Usage

With the Demo

Try it directly: Prompt Fungineer Space

With Transformers

from transformers import AutoModelForCausalLM, AutoTokenizer

model = AutoModelForCausalLM.from_pretrained("treadon/prompt-fungineer-355M")
tokenizer = AutoTokenizer.from_pretrained("gpt2")

prompt = "BRF: a castle in the clouds POS:"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(
    **inputs,
    max_length=256,
    do_sample=True,
    top_k=100,
    top_p=0.95,
    temperature=0.85,
    num_return_sequences=4,
    pad_token_id=tokenizer.eos_token_id,
)

for output in outputs:
    print(tokenizer.decode(output, skip_special_tokens=True))

Model Details

Attribute	Value
Base model	GPT-2 Medium (355M)
Fine-tuning	Causal language modeling on image prompt dataset
Tokenizer	GPT-2 tokenizer
Max length	256 tokens
Language	English
License	MIT

Recommended Settings

Parameter	Value	Notes
Temperature	0.85	Higher = more creative
Top K	100
Top P	0.95
Max length	256

Options

Enhancers: Adds camera/lens/lighting metadata (recommended ON)
Wild Imagination: Lets the model go beyond the base prompt
Inspiration: Adds photographer references (experimental)
Negative Prompt: Generates a negative prompt for Stable Diffusion

Limitations

355M parameters — produces good but sometimes generic results
Trained on English prompts only
Occasionally generates nonsensical photographer names in "Inspiration" mode
Best for photographic/realistic styles; less effective for abstract art prompts

Author

Built by Ritesh Khanna (@treadon)

treadon
/

prompt-fungineer-355M