End of training

README.md

@@ -1,44 +1,54 @@
 ---
-license: mit
+license: apache-2.0
 base_model: TinyLlama/TinyLlama-1.1B-Chat-v1.0
 tags:
-  - trl
-  - sft
-  - generated_from_trainer
+- trl
+- sft
+- generated_from_trainer
 model-index:
-  - name: mayo
-    results: []
-datasets:
-  - nroggendorff/mayo
-language:
-  - en
+- name: mayo
+  results: []
 ---
 
-# Mayonnaise LLM
+<!-- 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. -->
 
-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).
+# mayo
 
-## Features
+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.
 
-- Utilizes SFT and TRL techniques for improved performance
-- Supports English language
+## Model description
 
-## Usage
+More information needed
 
-To use the Mayo LLM, you can load the model using the Hugging Face Transformers library:
+## Intended uses & limitations
 
-```python
-from transformers import pipeline
+More information needed
 
-pipe = pipeline("text-generation", model="nroggendorff/mayo")
+## Training and evaluation data
 
-question = "What color is the sky?"
-conv = [{"role": "system", "content": "You are a very bored real human named Noa Roggendorff."}, {"role": "user", "content": question}]
+More information needed
 
-response = pipe(conv, max_new_tokens=2048)[0]['generated_text'][-1]['content']
-print(response)
-```
+## Training procedure
 
-## License
+### Training hyperparameters
 
-This project is licensed under the MIT 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

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