Added model card

README.md

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----
-license: apache-2.0
----
+---
+base_model: ministral/Ministral-3b-instruct
+library_name: peft
+---
+
+# Ministral-3b-instruct-PromptEnhancing
+
+Ministral-3b-instruct-PromptEnhancing is a LoRA-finetuned instruction-tuned text-generation model. 
+
+This model was releaszed alongside three other models in the 2-3b parameters range, all trained on the same dataset with the same training arguments.
+
+
+## Model Details
+
+### Model Description
+
+This model is a LoRA fine-tune of [ministral/Ministral-3b-instruct](https://huggingface.co/ministral/Ministral-3b-instruct). 
+The goal of this finetune is to provide a light-weight prompt enhancing model for stable diffusion (or other diffusers sharing the same prompting conventions) to make image generation more accessible to everyone.
+
+
+
+- **Developed by:** [groloch](https://huggingface.co/groloch)
+- **Model type:** LoRA
+- **Language(s) (NLP):** English
+- **License:** [apache 2.0](https://huggingface.co/datasets/choosealicense/licenses/blob/main/markdown/apache-2.0.md)
+- **Finetuned from model:** [ministral/Ministral-3b-instruct](https://huggingface.co/ministral/Ministral-3b-instruct). 
+
+### Model Sources [optional]
+
+- **Paper:** _Coming soon_
+- **Demo:** _Coming soon_
+
+## Uses
+
+This model should be used as a prompt-enhancing model for diffusers. To use it, the simplest is to try out at the official [demo](#) (_coming soon_).
+
+
+### Direct Use
+
+If you want to use it locally, refer to the following code snippet:
+```python
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM
+
+
+base_repo_id = 'ministral/Ministral-3b-instruct'
+adapter_repo_id = 'groloch/Ministral-3b-instruct-PromptEnhancing'
+
+tokenizer = AutoTokenizer.from_pretrained(base_repo_id)
+model = AutoModelForCausalLM.from_pretrained(base_repo_id, torch_dtype=torch.bfloat16).to('cuda')
+model.load_adapter(adapter_repo_id)
+
+prompt_to_enhance = 'Sinister crocodile eating a jolly rabbit'
+
+chat = [
+    {'role' : 'user', 'content': prompt_to_enhance}
+]
+
+prompt = tokenizer.apply_chat_template(chat, 
+                                       tokenize=False, 
+                                       add_generation_prompt=True,
+                                       return_tensors='pt')
+
+encoding = tokenizer(prompt, return_tensors="pt").to('cuda')
+
+generation_config = model.generation_config
+generation_config.do_sample = True
+generation_config.max_new_tokens = 96
+generation_config.temperature = 0.3
+generation_config.top_p = 0.7
+generation_config.num_return_sequences = 1
+generation_config.pad_token_id = tokenizer.eos_token_id
+generation_config.eos_token_id = tokenizer.eos_token_id
+generation_config.repetition_penalty = 2.0
+
+with torch.inference_mode():
+    outputs = model.generate(
+        input_ids=encoding.input_ids,
+        attention_mask=encoding.attention_mask,
+        generation_config=generation_config
+    )
+    
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+
+```
+
+### Out-of-Scope Use
+
+This model is meant to be used as a prompt enhancer. Inputs should be concise and not too detailed (no full prompts). 
+
+Using this model for other purposes may yield unexpected behavior.
+
+## Bias, Risks, and Limitations
+
+This model was trained on a dataset partially generated by AI, which may contain bias.
+
+This is a pretty lightweight model, so it may have significant limitations.
+
+### Recommendations
+
+Use high repetition penalty (> 2.0) and low temperature (< 0.4) for generation. Do not generate more than 128 tokens.
+
+## Training Details
+
+### Training Data
+
+This model was trained for one epoch on [groloch/stable_diffusion_prompts_instruct](https://huggingface.co/datasets/groloch/stable_diffusion_prompts_instruct).
+
+### Training Hyperparameters
+
+_coming soon_
+
+- PEFT 0.13.2