Update README.md

README.md

@@ -1,3 +1,51 @@
 ---
-license: cc-by-nc-4.0
+license: cc-by-4.0
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+# Piccolo-8x7b
+
+
+  **In loving memory of my dog Klaus (Piccolo)**
+    
+  _~ Piccolo (Italian): the little one ~_
+
+ ![piccolo.png](piccolo.png)
+
+
+# Code Example
+
+Inference and Evaluation colab available [here](https://colab.research.google.com/drive/1ZqLNvVvtFHC_4v2CgcMVh7pP9Fvx0SbI?usp=sharing)
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+def generate_response(prompt):
+    """
+    Generate a response from the model based on the input prompt.
+    Args:
+    prompt (str): Prompt for the model.
+
+    Returns:
+    str: The generated response from the model.
+    """
+    inputs = tokenizer(prompt, return_tensors="pt")
+    outputs = model.generate(**inputs, max_new_tokens=256, eos_token_id=tokenizer.eos_token_id, pad_token_id=tokenizer.pad_token_id)
+
+    response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+
+    return response
+
+model_id = "macadeliccc/Piccolo-8x7b"
+tokenizer = AutoTokenizer.from_pretrained(model_id)
+model = AutoModelForCausalLM.from_pretrained(model_id,load_in_4bit=True)
+
+prompt = "What is the best way to train Cane Corsos?"
+
+print("Response:")
+print(generate_response(prompt), "\n")
+```
+
+The model is capable of quality code, math, and logical reasoning. Try whatever questions you think of.
+
+# Evaluations
+
+TODO