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---
language:
- it
license: apache-2.0
tags:
- text-generation-inference
- text generation
---
# Mistral-7B-v0.1 for Italian Language Text Generation
## Overview
`Mistral-7B-v0.1` is a state-of-the-art Large Language Model (LLM) specifically pre-trained for generating text. With its 7 billion parameters, it's built to excel in benchmarks and outperforms even some larger models like the Llama 2 13B.
## Model Architecture
The Mistral-7B-v0.1 model is a transformer-based model that can handle a variety of tasks including but not limited to translation, summarization, and text completion. It's particularly designed for the Italian language and can be fine-tuned for specific tasks.
## Quantized version
[DeepMount00/Mistral-Ita-7b-GGUF](https://huggingface.co/DeepMount00/Mistral-Ita-7b-GGUF)
## Unique Features for Italian
- **Tailored Vocabulary**: The model's vocabulary is fine-tuned to encompass the nuances and diversity of the Italian language.
- **Enhanced Understanding**: Mistral-7B is specifically trained to grasp and generate Italian text, ensuring high linguistic and contextual accuracy.
## Capabilities
- **Vocabulary Size**: 32,000 tokens, allowing for a broad range of inputs and outputs.
- **Hidden Size**: 4,096 dimensions, providing rich internal representations.
- **Intermediate Size**: 14,336 dimensions, which contributes to the model's ability to process and generate complex sentences.
## How to Use
How to utilize my Mistral for Italian text generation
```python
import transformers
from transformers import TextStreamer
import torch
model_name = "DeepMount00/Mistral-Ita-7b"
tokenizer = transformers.AutoTokenizer.from_pretrained(model_name)
model = transformers.LlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto").eval()
def stream(user_prompt):
runtimeFlag = "cuda:0"
system_prompt = ''
B_INST, E_INST = "<s> [INST]", "[/INST]"
prompt = f"{system_prompt}{B_INST}{user_prompt.strip()}\n{E_INST}"
inputs = tokenizer([prompt], return_tensors="pt").to(runtimeFlag)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
_ = model.generate(**inputs, streamer=streamer, max_new_tokens=300, temperature=0.0001,
repetition_penalty=1.2, eos_token_id=2, do_sample=True, num_return_sequences=1)
domanda = """Scrivi una funzione python che moltiplica per 2 tutti i valori della lista:"""
contesto = """
[-5, 10, 15, 20, 25, 30, 35]
"""
prompt = domanda + "\n" + contesto
stream(prompt)
```
---
## Developer
[Michele Montebovi] |