Instructions to use QuantFactory/Qwen2-1.5B-Ita-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Qwen2-1.5B-Ita-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Qwen2-1.5B-Ita-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Qwen2-1.5B-Ita-GGUF",
	filename="Qwen2-1.5B-Ita.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Qwen2-1.5B-Ita-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2-1.5B-Ita-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Qwen2-1.5B-Ita-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2-1.5B-Ita-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Qwen2-1.5B-Ita-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Qwen2-1.5B-Ita-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-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/Qwen2-1.5B-Ita-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Qwen2-1.5B-Ita-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Qwen2-1.5B-Ita-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2-1.5B-Ita-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Qwen2-1.5B-Ita-GGUF

This is quantized version of DeepMount00/Qwen2-1.5B-Ita created using llama.cpp

Model Description

Qwen2 1.5B: Almost the Same Performance as ITALIA (iGenius) but 6 Times Smaller 🚀

Model Overview

Model Name: Qwen2 1.5B Fine-tuned for Italian Language
Version: 1.5b
Model Type: Language Model
Parameter Count: 1.5 billion
Language: Italian
Comparable Model: ITALIA by iGenius (9 billion parameters)

Model Description

Qwen2 1.5B is a compact language model specifically fine-tuned for the Italian language. Despite its relatively small size of 1.5 billion parameters, Qwen2 1.5B demonstrates strong performance, nearly matching the capabilities of larger models, such as the 9 billion parameter ITALIA model by iGenius. The fine-tuning process focused on optimizing the model for various language tasks in Italian, making it highly efficient and effective for Italian language applications.

Performance Evaluation

The performance of Qwen2 1.5B was evaluated on several benchmarks and compared against the ITALIA model. The results are as follows: Sure, here are the results in a markdown table:

Performance Evaluation

Model	Parameters	Average	MMLU	ARC	HELLASWAG
ITALIA	9B	43.5	35.22	38.49	56.79
Qwen2 1.5B	1.5B	43.18	49.04	33.06	47.13

Conclusion

Qwen2 1.5B demonstrates that a smaller, more efficient model can achieve performance levels comparable to much larger models. It excels in the MMLU benchmark, showing its strength in multitask language understanding. While it scores slightly lower in the ARC and HELLASWAG benchmarks, its overall performance makes it a viable option for Italian language tasks, offering a balance between efficiency and capability.

Downloads last month: 134

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Qwen2-1.5B-Ita-GGUF

Base model

DeepMount00/Qwen2-1.5B-Ita

Quantized

(9)

this model