more eval + refinements to code example

README.md

@@ -31,13 +31,25 @@ Check out the [📖 full walkthrough article](https://huggingface.co/blog/anakin
 
 ## 🏆 Evaluation
 
+*Open ITA LLM Leaderboard*
+
 | Model                                 | Parameters | Average | MMLU_IT | ARC_IT | HELLASWAG_IT |
 | ------------------------------------- | ---------- | ------- | ------- | ------ | ------------ |
 | **anakin87/Phi-3.5-mini-ITA**         | **3.82 B**     |**57.67**   | 59.93   | 51.5  | 61.57        |
 | meta-llama/Meta-Llama-3.1-8B-Instruct | 8.03 B     | 56.97   | 58.43   | 48.42  | 64.07        |
 | microsoft/Phi-3.5-mini-instruct       | 3.82 B     | 56.82   | 60.03   | 49.19  | 61.25        |
 
-For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
+[Details](https://huggingface.co/spaces/mii-llm/open_ita_llm_leaderboard)
+
+*Pinocchio ITA Leaderboard*
+
+| Model                                 | Parameters | Average   | 
+| ------------------------------------- | ---------- | -------   | 
+| **anakin87/Phi-3.5-mini-ITA**         | **3.82 B** | **57.95** |
+| meta-llama/Meta-Llama-3.1-8B-Instruct | 8.03 B     | 56.93     |
+
+[Details](https://huggingface.co/spaces/mii-llm/pinocchio_ita_leaderboard)
+
 
 ## 🎮 Model in action
 ### Demo
@@ -54,7 +66,7 @@ Read [this discussion](https://huggingface.co/microsoft/Phi-3.5-mini-instruct/di
 ```python
 # pip install transformers accelerate
 import torch
-from transformers import pipeline
+from transformers import pipeline, AutoModelForCausalLM, AutoTokenizer
 
 model_id="anakin87/Phi-3.5-mini-ITA"
 
@@ -71,7 +83,7 @@ pipe = pipeline("text-generation", model=model, tokenizer=tokenizer)
 
 user_input = "Puoi spiegarmi brevemente la differenza tra imperfetto e passato prossimo in italiano e quando si usano?"
 messages = [{"role": "user", "content": user_input}]
-outputs = pipe(prompt, max_new_tokens=500, do_sample=True, temperature=0.001)
+outputs = pipe(user_input, max_new_tokens=500, do_sample=True, temperature=0.001)
 print(outputs[0]["generated_text"])
 ```