Create README.md

README.md

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+---
+language:
+- it
+license: apache-2.0
+tags:
+- text-generation-inference
+- text generation
+datasets:
+- DeepMount00/llm_ita_ultra
+pipeline_tag: text-generation
+base_model: DeepMount00/Mistral-Ita-7b
+---
+
+# QuantFactory/Mistral-Ita-7b-GGUF
+This is quantized version of [DeepMount00/Mistral-Ita-7b](https://huggingface.co/DeepMount00/Mistral-Ita-7b) created using llama.cpp
+
+# Model Description
+## Mistral-7B-v0.1 for Italian Language Text Generation
+
+## Model Architecture
+- **Base Model:** [Mistral-7B-v0.1](https://huggingface.co/mistralai/Mistral-7B-v0.1)
+- **Specialization:** Italian Language
+
+## 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**     | 0.6731                | 0.5502          | 0.5364               | 0.5866  |
+
+---
+
+
+**Quantized 4-Bit Version Available**
+
+A quantized 4-bit version of the model is available for use. This version offers a more efficient processing capability by reducing the precision of the model's computations to 4 bits, which can lead to faster performance and decreased memory usage. This might be particularly useful for deploying the model on devices with limited computational power or memory resources.
+
+For more details and to access the model, visit the following link: [Mistral-Ita-7b-GGUF 4-bit version](https://huggingface.co/DeepMount00/Mistral-Ita-7b-GGUF).
+
+---
+
+## How to Use
+How to utilize my Mistral for Italian text generation
+
+```python
+from transformers import AutoModelForCausalLM, AutoTokenizer
+import torch
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+MODEL_NAME = "DeepMount00/Mistral-Ita-7b"
+
+model = AutoModelForCausalLM.from_pretrained(MODEL_NAME, torch_dtype=torch.bfloat16).eval()
+model.to(device)
+tokenizer = AutoTokenizer.from_pretrained(MODEL_NAME)
+
+def generate_answer(prompt):
+    messages = [
+        {"role": "user", "content": prompt},
+    ]
+    model_inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(device)
+    generated_ids = model.generate(model_inputs, max_new_tokens=200, do_sample=True,
+                                          temperature=0.001, eos_token_id=tokenizer.eos_token_id)
+    decoded = tokenizer.batch_decode(generated_ids, skip_special_tokens=True)
+    return decoded[0]
+
+prompt = "Come si apre un file json in python?"
+answer = generate_answer(prompt)
+print(answer)
+```
+---
+## Developer
+[Michele Montebovi]