e-palmisano
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Phi3-ITA-mini-4K-instruct

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 ---
-library_name: transformers
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 ---
-# Model Card for Model ID
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 ---
+language:
+- it
+license: mit
+tags:
+- text-generation-inference
+- transformers
+- trl
+- sft
+- phi-3
+- phi-3-mini
+- italian
+base_model: microsoft/Phi-3-mini-4k-instruct
 ---
+# Uploaded  model
+- **Developed by:** Enzo Palmisano
+- **License:** mit
+- **Finetuned from model :** microsoft/Phi-3-mini-4k-instruct
 ## Evaluation
+For a detailed comparison of model performance, check out the [Leaderboard for Italian Language Models](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard).
+Here's a breakdown of the performance metrics:
+| Metric                      | hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
+|:----------------------------|:----------------------|:----------------|:---------------------|:--------|
+| **Accuracy Normalized**     | 0.6088                | 0.4440          | X                    | X       |
+---
+## How to Use
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+tokenizer = AutoTokenizer.from_pretrained("FairMind/Phi-3-mini-4k-instruct-bnb-4bit-Ita")
+model = AutoModelForCausalLM.from_pretrained("FairMind/Phi-3-mini-4k-instruct-bnb-4bit-Ita")
+model.to(device)
+generation_config = GenerationConfig(
+      penalty_alpha=0.6, # The values balance the model confidence and the degeneration penalty in contrastive search decoding.
+      do_sample = True, # Whether or not to use sampling ; use greedy decoding otherwise.
+      top_k=5, #  The number of highest probability vocabulary tokens to keep for top-k-filtering.
+      temperature=0.001, #  The value used to modulate the next token probabilities.
+      repetition_penalty=1.7, # The parameter for repetition penalty. 1.0 means no penalty.
+      max_new_tokens = 64, # The maximum numbers of tokens to generate, ignoring the number of tokens in the prompt.
+      eos_token_id=tokenizer.eos_token_id, # The id of the *end-of-sequence* token.
+      pad_token_id=tokenizer.eos_token_id, # The id of the *padding* token.
+  )
+def generate_answer(question):
+    messages = [
+        {"role": "user", "content": question},
+    ]
+    model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
+    outputs = model.generate(model_inputs, generation_config=generation_config)
+    result = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0]
+    return result
+question = """Quale è la torre più famosa di Parigi?"""
+answer = generate_answer(question)
+print(answer)
+```
+---