Update README.md

README.md

@@ -1,204 +1,41 @@
 ---
-library_name: peft
-base_model: mlabonne/AlphaMonarch-7B
+license: mit
+datasets:
+- daswer123/sentence-danbooru-tags-dataset
 ---
 
-# Model Card for Model ID
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-<!-- Provide a quick summary of what the model is/does. -->
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-## Model Details
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-### Model Description
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-- **Developed by:** [More Information Needed]
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-### Model Sources [optional]
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-## Uses
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-### Direct Use
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-### Downstream Use [optional]
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-### Out-of-Scope Use
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-## Bias, Risks, and Limitations
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-### Recommendations
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-Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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-## How to Get Started with the Model
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-Use the code below to get started with the model.
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-## Training Details
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-### Training Data
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-### Training Procedure 
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-#### Preprocessing [optional]
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-#### Training Hyperparameters
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-#### Speeds, Sizes, Times [optional]
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-## Evaluation
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-### Testing Data, Factors & Metrics
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-#### Testing Data
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-#### Factors
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-#### Metrics
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-### Results
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-#### Summary
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-## Model Examination [optional]
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-## Environmental Impact
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-Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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-## Technical Specifications [optional]
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-### Model Architecture and Objective
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-#### Hardware
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-## Citation [optional]
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-**BibTeX:**
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-## Glossary [optional]
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-## More Information [optional]
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-## Model Card Authors [optional]
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-## Model Card Contact
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-
-### Framework versions
-
-- PEFT 0.8.2
+Qlora for AlphaMonarch-7B which allows you to convert a given model sentence into a set of danbooru tags
+format: Alpaca 
+train format: Alpaca
+
+This is the first time I have had this lora done and I am quite happy with the result. This lora was trained on full model AlphaMonarch-7B. with rank of 8 for 3 epochs
+There were 15k instructions in the dataset that had examples of how to convert from sentences to tags.
+
+I use following prompt
+```
+Here are examples following these rules:
+
+Input: A boy with glasses is drawing in his sketchbook at a park during autumn.
+Output: 1boy, solo, glasses, drawing, sketchbook, park, autumn
+
+Input: Two girls are jogging together around a lake at sunrise; one has red hair and the other has brown.
+Output: 2girls, jogging together, lake, sunrise, red hair girl, brown hair girl
+
+Input: A girl with cat ears, wearing a hoodie and with long white hair and red eyes walks arm in arm with a girl in a black bikini with brown hair and green eyes
+Output: 2girls, walking, holding hands\n1girl, cat ears, hoodie, long hair, white hair, red eyes\n1girl, black bikini, brown hair, green eyes
+
+Input: 3 girls walking in the park, one in a hoodie with green hair, one with red hair and a dress, 3 in shorts and a top with white hair
+Output:  3girls, walking in park\n1girl, green hair, hoodie\n1girl, red hair, dress\n1girl, white hair, shorts, top
+
+Important note:
+- Always start with the number of characters and their gender (e.g., "1girl," "3boys").
+- If a character is alone, add the "solo" tag.
+- Describe physical attributes such as hair color or clothing (e.g., "pink hair," "gothic lolita fashion").
+- Describe actions of characters if any (e.g., “playing violin,” “studying”).
+- Mention setting details like location and background objects (e.g.,”music room”, “classical instruments”).
+- Keep each tag separated by a comma.
+- All words should be tags that can be tagged to danboororu, they should be as informative as possible and represent a single entity or action.
+- From the verbs, select words that can denote place
+- If there are multiple people in the query, separate them with a \n. The first line is the number of people and location. And each following line is a description of one person.
+- Answer only on English
+```