improve readme

README.md

@@ -51,38 +51,23 @@ gemma-2b-orpo performs well on Nous' benchmark suite (evaluation performed using
 is a simplified version of [`argilla/dpo-mix-7k`](https://huggingface.co/datasets/argilla/dpo-mix-7k).
 You can find more information [here](https://huggingface.co/alvarobartt/Mistral-7B-v0.1-ORPO#about-the-dataset).
 
-## 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: 2
-- eval_batch_size: 8
-- seed: 42
-- gradient_accumulation_steps: 2
-- total_train_batch_size: 4
-- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
-- lr_scheduler_type: cosine
-- lr_scheduler_warmup_ratio: 0.1
-- lr_scheduler_warmup_steps: 100
-- num_epochs: 3
-
-### Training results
-
-
+## 🎮 Model in action
+### [📓 Examples: Chat and RAG using Haystack](./notebooks/usage.ipynb)
+### Simple text generation with Transformers
+The model is small, so runs smoothly on Colab. *It is also fine to load the model using quantization*.
+```python
+# pip install transformers accelerate
+import torch
+from transformers import pipeline
+pipe = pipeline("text-generation", model="anakin87/gemma-2b-orpo", torch_dtype=torch.bfloat16, device_map="auto")
+messages = [{"role": "user", "content": "Write a rap song on Vim vs VSCode."}]
+prompt = pipe.tokenizer.apply_chat_template(messages, tokenize=False)
+outputs = pipe(prompt, max_new_tokens=500, do_sample=True, temperature=0.7,  top_k=50, top_p=0.95)
+print(outputs[0]["generated_text"])
+```
+## Training
+The model was trained using HF TRL.
+[📓 Training notebook](./notebooks/training.ipynb)
 
 ### Framework versions