Update README.md

README.md

@@ -1,54 +1,43 @@
 ---
-license: apache-2.0
+license: mit
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 tags:
-- trl
-- sft
-- generated_from_trainer
+  - trl
+  - sft
 model-index:
-- name: mayo
-  results: []
+  - name: mayo
+    results: []
+datasets:
+  - nroggendorff/mayo
+language:
+  - en
 ---
 
-<!-- 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. -->
+# Mayonnaise LLM
 
-# mayo
+Mayo is a language model fine-tuned on the [Mayo dataset](https://huggingface.co/datasets/nroggendorff/mayo) using Supervised Fine-Tuning (SFT) and Teacher Reinforced Learning (TRL) techniques. It is based on the [TinyLlama/TinyLlama-1.1B-Chat-v1.0 model](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0).
 
-This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-Chat-v1.0](https://huggingface.co/TinyLlama/TinyLlama-1.1B-Chat-v1.0) on an unknown dataset.
+## Features
 
-## Model description
+- Utilizes SFT and TRL techniques for improved performance
+- Supports English language
 
-More information needed
+## Usage
 
-## Intended uses & limitations
+To use the Mayo LLM, you can load the model using the Hugging Face Transformers library:
 
-More information needed
+```python
+from transformers import pipeline
 
-## Training and evaluation data
+pipe = pipeline("text-generation", model="nroggendorff/mayo")
 
-More information needed
+question = "What color is the sky?"
+conv = [{"role": "system", "content": "You are a very bored real human named Noa Roggendorff."}, {"role": "user", "content": question}]
 
-## Training procedure
+response = pipe(conv, max_new_tokens=2048)[0]['generated_text'][-1]['content']
+print(response)
+```
 
-### Training hyperparameters
+## License
 
-The following hyperparameters were used during training:
-- learning_rate: 0.0001
-- train_batch_size: 4
-- eval_batch_size: 16
-- seed: 42
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: linear
-- training_steps: 112
-
-### Training results
-
-
-
-### Framework versions
-
-- Transformers 4.39.3
-- Pytorch 2.1.2
-- Datasets 2.18.0
-- Tokenizers 0.15.2
+This project is licensed under the MIT License.