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treadon/prompt-fungineer-355M
prompt-fungineer-355M is a text generation model from treadon. Use it when you need the model to write or continue text. It is set up for transformers. The card lists the license as mit.
Follow @treadon on X and treadon on Hugging Face for more AI experiments, evals, and projects.
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From the Hugging Face model README
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.
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
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)Try it directly: Prompt Fungineer Space
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))
| 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 |
| Parameter | Value | Notes |
|---|---|---|
| Temperature | 0.85 | Higher = more creative |
| Top K | 100 | |
| Top P | 0.95 | |
| Max length | 256 |
Built by Ritesh Khanna (@treadon)
For other projects and writeups, see riteshkhanna.com, follow @treadon on X, or treadon on Hugging Face.