初始化项目，由ModelHub XC社区提供模型

Model: walid-iguider/Phi-3-mini-4k-instruct-bnb-4bit-Ita
Source: Original Platform

README.md

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+---
+language:
+- it
+license: cc-by-nc-sa-4.0
+tags:
+- text-generation-inference
+- transformers
+- unsloth
+- trl
+- sft
+- phi-3
+- phi-3-mini
+- italian
+base_model: unsloth/Phi-3-mini-4k-instruct-bnb-4bit
+datasets:
+- mchl-labs/stambecco_data_it
+---
+
+# Uploaded  model
+
+- **Developed by:** walid-iguider
+- **License:** cc-by-nc-sa-4.0
+- **Finetuned from model :** unsloth/Phi-3-mini-4k-instruct-bnb-4bit
+
+
+## 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**     | 0.5841                | 0.4414        | 0.5389              | 0.5214  |
+
+---
+
+## 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)
+```
+---
+
+This model was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth) and Huggingface's TRL library.
+
+[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)