Mistral-Ita-7b-GGUF

QuantFactory/Mistral-Ita-7b-GGUF

This is quantized version of 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
• Specialization: Italian Language

Evaluation

For a detailed comparison of model performance, check out the Leaderboard for Italian Language Models.

Here's a breakdown of the performance metrics:

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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.

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How to Use

How to utilize my Mistral for Italian text generation

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)

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Developer

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