Instructions to use QuantFactory/Mistral-Ita-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistral-Ita-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Mistral-Ita-7b-GGUF",
	filename="Mistral-Ita-7b.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Mistral-Ita-7b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Mistral-Ita-7b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Mistral-Ita-7b-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Mistral-Ita-7b-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Mistral-Ita-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Mistral-Ita-7b-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Mistral-Ita-7b-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for QuantFactory/Mistral-Ita-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Mistral-Ita-7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Mistral-Ita-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistral-Ita-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistral-Ita-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Ita-7b-GGUF-Q4_K_M

List all available models

lemonade list

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:

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.

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)

Developer

[Michele Montebovi]

Downloads last month: 160

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Mistral-Ita-7b-GGUF

Base model

DeepMount00/Mistral-Ita-7b

Quantized

(3)

this model

Collection including QuantFactory/Mistral-Ita-7b-GGUF

Mistral-AI

Collection

Quantized versions of models by mistralai • 19 items • Updated Oct 3, 2024 • 6