README.md

---
library_name: transformers
license: apache-2.0
datasets:
- Gustavosta/Stable-Diffusion-Prompts
language:
- en
tags:
- completion
widget:
  - text: A picture of
  - text: photo of
  - text: a drawing of
inference:
  parameters:
    max_new_tokens: 20
    do_sample: True
    early_stopping: True
    temperature: 1.2
    num_beams: 5
    no_repeat_ngram_size: 2
    repetition_penalty: 1.35
    top_k: 50
    top_p: 0.75
---
# MagicPrompt TinyStories-33M (Merged)

## Info

Magic prompt completion model trained on a dataset of 80k Stable Diffusion prompts. Base model: TinyStories-33M. Inspired by [MagicPrompt-Stable-Diffusion](https://huggingface.co/Gustavosta/MagicPrompt-Stable-Diffusion).

Model seems to be pretty decent for 33M params due to the TinyStories base, but it clearly lacks much of an understanding of pretty much anything. Still, considering the size, I think it's decent. Whether you would use this over a small GPT-2 based model is up to you.

## Examples

Best generation settings I found: `max_new_tokens=40, do_sample=True, temperature=1.2, num_beams=10, no_repeat_ngram_size=2, early_stopping=True, repetition_penalty=1.35, top_k=50, top_p=0.55, eos_token_id=tokenizer.eos_token_id, pad_token_id=0` (there may be better settings).
`no_repeat_ngram_size` is important for making sure the model doesn't repeat phrases (as it is quite small).

(Bold text is generated by the model)

"found footage of a ufo **in the forest, by lusax, wlop, greg rutkowski, stanley artgerm, highly detailed, intricate, digital painting, artstation, concept art, smooth**"

"A close shot of a bird in a jungle, **with two legs, with long hair on a tall, long brown body, long white skin, sharp teeth, high bones, digital painting, artstation, concept art, illustration by wlop,**"

"Camera shot of **a strange young girl wearing a cloak, wearing a mask in clothes, with long curly hair, long hair, black eyes, dark skin, white teeth, long brown eyes eyes, big eyes, sharp**"

"An illustration of a house, stormy weather, **sun, moonlight, night, concept art, 4 k, wlop, by wlop, by jose stanley, ilya kuvshinov, sprig**"

"A field of flowers, camera shot, 70mm lens, **fantasy, intricate, highly detailed, artstation, concept art, sharp focus, illustration, illustration, artgerm jake daggaws, artgerm and jaggodieie brad**"

## Next steps

- Larger dataset ie [neuralworm/stable-diffusion-discord-prompts](https://huggingface.co/datasets/neuralworm/stable-diffusion-discord-prompts) or [daspartho/stable-diffusion-prompts](https://huggingface.co/datasets/daspartho/stable-diffusion-prompts)
- More epochs
- Instead of going smaller than GPT-2 137M, fine tune a 1-7B param model

## Training config

- Rank 16 LoRA
- Trained on Gustavosta/Stable-Diffusion-Prompts for 10 epochs
- Batch size of 64

## Training procedure


The following `bitsandbytes` quantization config was used during training:
- load_in_8bit: False
- load_in_4bit: True
- llm_int8_threshold: 6.0
- llm_int8_skip_modules: None
- llm_int8_enable_fp32_cpu_offload: False
- llm_int8_has_fp16_weight: False
- bnb_4bit_quant_type: fp4
- bnb_4bit_use_double_quant: False
- bnb_4bit_compute_dtype: float32
### Framework versions


- PEFT 0.5.0.dev0