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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)
+
+
+bloom-560m-finetuned-sd-prompts - bnb 4bits
+- Model creator: https://huggingface.co/mrm8488/
+- Original model: https://huggingface.co/mrm8488/bloom-560m-finetuned-sd-prompts/
+
+
+
+
+Original model description:
+---
+license: bigscience-bloom-rail-1.0
+tags:
+- generated_from_trainer
+- stable-diffusion
+- diffusion
+model-index:
+- name: bloom-560m-finetuned-sd-prompts
+  results: []
+  
+datasets:
+ - Gustavosta/Stable-Diffusion-Prompts
+ 
+widget:
+- text: "<s>Prompt: young, curly haired, redhead Natalie Portman as a"
+- text: "<s>Prompt: a powerful energy woman, by alexander fedosav"
+
+inference:
+  parameters:
+    eos_token_id: 2
+    max_length: 128
+    
+  
+---
+
+<!-- This model card has been generated automatically according to the information the Trainer had access to. You
+should probably proofread and complete it, then remove this comment. -->
+
+# bloom-560m-finetuned-sd-prompts
+
+This model is a fine-tuned version of [bigscience/bloom-560m](https://huggingface.co/bigscience/bloom-560m) on the [Gustavosta/Stable-Diffusion-Prompts](https://huggingface.co/datasets/Gustavosta/Stable-Diffusion-Prompts) dataset.
+It achieves the following results on the evaluation set:
+- Loss: 0.8742
+
+
+## Example of usage
+
+```py
+import torch
+from transformers import BloomTokenizerFast, BloomForCausalLM
+
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+ckpt = 'mrm8488/bloom-560m-finetuned-sd-prompts' 
+
+tokenizer = BloomTokenizerFast.from_pretrained(ckpt)
+model = BloomForCausalLM.from_pretrained(ckpt).to(device)
+
+def generate_prompt(text):
+    inputs = tokenizer(text, return_tensors='pt')
+    input_ids = inputs.input_ids.to(device)
+    attention_mask = inputs.attention_mask.to(device)
+    output = model.generate(input_ids, attention_mask=attention_mask, repetition_penalty=1.05, max_length=2048, eos_token_id=tokenizer.eos_token_id)
+
+    return tokenizer.decode(output[0], skip_special_tokens=False)
+    
+text = "<s>Prompt: pikachu dinning in the eiffel tower"
+
+generate_prompt(text)
+
+# Output: <s>Prompt: pikachu dinning in the eiffel tower, intricate, elegant, highly detailed, digital painting, artstation, concept art, smooth, sharp focus, illustration, art by artgerm and greg rutkowski and alphonse mucha</s>
+```
+
+## Model description
+
+More information needed
+
+## Intended uses & limitations
+
+More information needed
+
+## Training and evaluation data
+
+More information needed
+
+## Training procedure
+
+### Training hyperparameters
+
+The following hyperparameters were used during training:
+- learning_rate: 5e-05
+- train_batch_size: 1
+- eval_batch_size: 1
+- seed: 42
+- gradient_accumulation_steps: 4
+- total_train_batch_size: 4
+- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
+- lr_scheduler_type: linear
+- num_epochs: 2
+- mixed_precision_training: Native AMP
+
+### Training results
+
+| Training Loss | Epoch | Step | Validation Loss |
+|:-------------:|:-----:|:----:|:---------------:|
+| 2.6743        | 0.17  | 100  | 2.0891          |
+| 1.8919        | 0.33  | 200  | 1.7191          |
+| 1.5907        | 0.5   | 300  | 1.4454          |
+| 1.3865        | 0.67  | 400  | 1.3247          |
+| 1.2487        | 0.83  | 500  | 1.2150          |
+| 1.1565        | 1.0   | 600  | 1.1031          |
+| 0.896         | 1.17  | 700  | 1.0612          |
+| 0.8389        | 1.33  | 800  | 0.9994          |
+| 0.8071        | 1.5   | 900  | 0.9530          |
+| 0.7628        | 1.67  | 1000 | 0.9206          |
+| 0.7423        | 1.83  | 1100 | 0.8883          |
+| 0.7155        | 2.0   | 1200 | 0.8742          |
+
+
+### Framework versions
+
+- Transformers 4.22.1
+- Pytorch 1.12.1+cu113
+- Datasets 2.5.1
+- Tokenizers 0.12.1
+
+