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README.md

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+Quantization made by Richard Erkhov.
+
+[Github](https://github.com/RichardErkhov)
+
+[Discord](https://discord.gg/pvy7H8DZMG)
+
+[Request more models](https://github.com/RichardErkhov/quant_request)
+
+
+MagicPrompt-tinystories-33M-epoch10-merged - bnb 8bits
+- Model creator: https://huggingface.co/Technotech/
+- Original model: https://huggingface.co/Technotech/MagicPrompt-tinystories-33M-epoch10-merged/
+
+
+
+
+Original model description:
+---
+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
+