初始化项目，由ModelHub XC社区提供模型

Model: MoxoffSrL/AzzurroQuantized
Source: Original Platform

README.md

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+---
+license: mit
+language:
+- it
+- en
+library_name: transformers
+tags:
+- sft
+- it
+- mistral
+- chatml
+---
+
+# Model Information
+
+AzzurroQuantized is a compact iteration of the model [Azzurro](https://huggingface.co/MoxoffSpA/Azzurro), optimized for efficiency.
+
+It is offered in two distinct configurations: a 4-bit version and an 8-bit version, each designed to maintain the model's effectiveness while significantly reducing its size
+and computational requirements.
+
+- It's trained both on publicly available datasets, like [SQUAD-it](https://huggingface.co/datasets/squad_it), and datasets we've created in-house.
+- it's designed to understand and maintain context, making it ideal for Retrieval Augmented Generation (RAG) tasks and applications requiring contextual awareness.
+- It is quantized in a 4-bit version and an 8-bit version following the procedure [here](https://github.com/ggerganov/llama.cpp).
+
+# Evaluation
+
+We evaluated the model using the same test sets as used for the [Open Ita LLM Leaderboard](https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard):
+
+| hellaswag_it acc_norm | arc_it acc_norm | m_mmlu_it 5-shot acc | Average |
+|:----------------------| :--------------- | :-------------------- | :------- |
+| 0.6067 | 0.4405 | 0.5112 | 0,52 |
+
+## Usage
+
+You need to download the .gguf model first 
+
+If you want to use the cpu install these dependencies:
+
+```python
+pip install llama-cpp-python huggingface_hub
+```
+
+If you want to use the gpu instead:
+
+```python
+CMAKE_ARGS="-DLLAMA_CUBLAS=on" pip install huggingface_hub llama-cpp-python --force-reinstall --upgrade --no-cache-dir
+```
+
+And then use this code to see a response to the prompt. 
+
+```python
+from huggingface_hub import hf_hub_download
+from llama_cpp import Llama
+
+model_path = hf_hub_download(
+    repo_id="MoxoffSpA/AzzurroQuantized",
+    filename="Azzurro-ggml-Q4_K_M.gguf"
+)
+
+# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
+llm = Llama(
+  model_path=model_path,
+  n_ctx=2048,  # The max sequence length to use - note that longer sequence lengths require much more resources
+  n_threads=8,            # The number of CPU threads to use, tailor to your system and the resulting performance
+  n_gpu_layers=0         # The number of layers to offload to GPU, if you have GPU acceleration available
+)
+
+# Simple inference example
+question = """Quanto è alta la torre di Pisa?"""
+context = """
+La Torre di Pisa è un campanile del XII secolo, famoso per la sua inclinazione. Alta circa 56 metri.
+"""
+
+prompt = f"Domanda: {question}, contesto: {context}"
+
+output = llm(
+  f"[INST] {prompt} [/INST]", # Prompt
+  max_tokens=128,
+  stop=["\n"],   
+  echo=True,
+  temperature=0.1,
+  top_p=0.95
+)
+
+# Chat Completion API
+
+print(output['choices'][0]['text'])
+```
+
+## Bias, Risks and Limitations
+
+AzzurroQuantized and its original model [Azzurro](https://huggingface.co/MoxoffSpA/Azzurro) have not been aligned to human preferences for safety within the RLHF phase or deployed with in-the-loop filtering of 
+responses like ChatGPT, so the model can produce problematic outputs (especially when prompted to do so). It is also unknown what the size and composition 
+of the corpus were used to train the base model [mistralai/Mistral-7B-v0.2](https://huggingface.co/mistralai/Mistral-7B-v0.2), however, it is likely to have included a mix of Web data and technical sources 
+like books and code.
+
+## Links to resources
+
+- SQUAD-it dataset: https://huggingface.co/datasets/squad_it
+- Mistral_7B_v0.2 original weights: https://models.mistralcdn.com/mistral-7b-v0-2/mistral-7B-v0.2.tar
+- Mistral_7B_v0.2 model: https://huggingface.co/alpindale/Mistral-7B-v0.2-hf
+- Open Ita LLM Leaderbord: https://huggingface.co/spaces/FinancialSupport/open_ita_llm_leaderboard
+
+## Base version
+
+We have the not quantized version here:
+https://huggingface.co/MoxoffSpA/Azzurro
+
+## The Moxoff Team
+
+Jacopo Abate, Marco D'Ambra, Luigi Simeone, Gianpaolo Francesco Trotta