Instructions to use fbaldassarri/modello-italia-9B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use fbaldassarri/modello-italia-9B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="fbaldassarri/modello-italia-9B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("fbaldassarri/modello-italia-9B-GGUF")
model = AutoModelForCausalLM.from_pretrained("fbaldassarri/modello-italia-9B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
	messages,
	add_generation_prompt=True,
	tokenize=True,
	return_dict=True,
	return_tensors="pt",
).to(model.device)

outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

llama-cpp-python

How to use fbaldassarri/modello-italia-9B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="fbaldassarri/modello-italia-9B-GGUF",
	filename="modello-italia-9b-ggml-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 fbaldassarri/modello-italia-9B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf fbaldassarri/modello-italia-9B-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 fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf fbaldassarri/modello-italia-9B-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 fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf fbaldassarri/modello-italia-9B-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 fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf fbaldassarri/modello-italia-9B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/fbaldassarri/modello-italia-9B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use fbaldassarri/modello-italia-9B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "fbaldassarri/modello-italia-9B-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": "fbaldassarri/modello-italia-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/fbaldassarri/modello-italia-9B-GGUF:Q4_K_M

SGLang

How to use fbaldassarri/modello-italia-9B-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 "fbaldassarri/modello-italia-9B-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": "fbaldassarri/modello-italia-9B-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 "fbaldassarri/modello-italia-9B-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": "fbaldassarri/modello-italia-9B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use fbaldassarri/modello-italia-9B-GGUF with Ollama:
```
ollama run hf.co/fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
```

Unsloth Studio new

How to use fbaldassarri/modello-italia-9B-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 fbaldassarri/modello-italia-9B-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 fbaldassarri/modello-italia-9B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for fbaldassarri/modello-italia-9B-GGUF to start chatting

Docker Model Runner
How to use fbaldassarri/modello-italia-9B-GGUF with Docker Model Runner:
```
docker model run hf.co/fbaldassarri/modello-italia-9B-GGUF:Q4_K_M
```

Lemonade

How to use fbaldassarri/modello-italia-9B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull fbaldassarri/modello-italia-9B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.modello-italia-9B-GGUF-Q4_K_M

List all available models

lemonade list

Model Card for Modello Italia 9B GGUFs

This an UNOFFICIAL GGUF format model files repository for converted/quantized OFFICIAL model checkpoint of "Modello Italia 9B", Large Language Model (LLM) developed by iGenius in collaboration with CINECA.

More information about Modello Italia: click here.

🚨 Disclaimers

This is an UNOFFICIAL quantization of the OFFICIAL model checkpoint released by iGenius.
This model is based also on the conversion made for HF Transformers by Sapienza NLP, Sapienza University of Rome.
The original model was developed using LitGPT.

🚨 Terms and Conditions

Note: By using this model, you accept the iGenius' terms and conditions.

🚨 About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible.

Here is an incomplate list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
neural-speed. Same interface of llama.cpp, optimized for interefence on CPU.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for story telling.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy to use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.

🚨 Explanation of quantisation methods

The new methods available are:

GGML_TYPE_Q2_K - "type-1" 2-bit quantization in super-blocks containing 16 blocks, each block having 16 weight. Block scales and mins are quantized with 4 bits. This ends up effectively using 2.5625 bits per weight (bpw)
GGML_TYPE_Q3_K - "type-0" 3-bit quantization in super-blocks containing 16 blocks, each block having 16 weights. Scales are quantized with 6 bits. This end up using 3.4375 bpw.
GGML_TYPE_Q4_K - "type-1" 4-bit quantization in super-blocks containing 8 blocks, each block having 32 weights. Scales and mins are quantized with 6 bits. This ends up using 4.5 bpw.
GGML_TYPE_Q5_K - "type-1" 5-bit quantization. Same super-block structure as GGML_TYPE_Q4_K resulting in 5.5 bpw
GGML_TYPE_Q6_K - "type-0" 6-bit quantization. Super-blocks with 16 blocks, each block having 16 weights. Scales are quantized with 8 bits. This ends up using 6.5625 bpw

Refer to the Provided Files table below to see what files use which methods, and how.

🚨 Provided files

Name	Quant method	Bits	Size	Use case
modello-italia-9b-ggml-Q2_K.gguf	Q2_K	2	3.3 GB	smallest, significant quality loss - not recommended for most purposes
modello-italia-9b-ggml-Q3_K_M.gguf	Q3_K_M	3	4.6 GB	very small, high quality loss
modello-italia-9b-ggml-Q3_K_L.gguf	Q3_K_L	3	4.9 GB	small, substantial quality loss
modello-italia-9b-ggml-Q4_0.gguf	Q4_0	4	4.9 GB	legacy; small, very high quality loss - prefer using Q3_K_M
modello-italia-9b-ggml-Q4_K_S.gguf	Q4_K_S	4	4.9 GB	small, greater quality loss
modello-italia-9b-ggml-Q4_K_M.gguf	Q4_K_M	4	5.5 GB	medium, balanced quality - RECOMMENDED
modello-italia-9b-ggml-Q5_0.gguf	Q5_0	5	5.9 GB	legacy; medium, balanced quality - prefer using Q4_K_M
modello-italia-9b-ggml-Q5_K_S.gguf	Q5_K_S	5	5.9 GB	large, low quality loss - recommended
modello-italia-9b-ggml-Q5_K_M.gguf	Q5_K_M	5	6.4 GB	large, very low quality loss - recommended
modello-italia-9b-ggml-Q6_K.gguf	Q6_K	6	7.0 GB	very large, extremely low quality loss
modello-italia-9b-ggml-Q8_0.gguf	Q8_0	8	9.1 GB	very large, extremely low quality loss - not recommended
modello-italia-9b-ggml-f16.gguf	FP16	16	17.1 GB	very large, extremely low quality loss - not recommended
modello-italia-9b-ggml-f32.gguf	FP32	32	34.2 GB	very large, no quality loss - not recommended

🚨 Compatibility

These quantised GGUFv2 files are compatible with llama.cpp from August 27th 2023 onwards, as of commit d0cee0d36d5be95a0d9088b674dbb27354107221

They are also compatible with many third party UIs and libraries - please see the list at the top of this README.

🚨 Reproducibility

This model has been converted/quantized using Intel neural-speed.

🚨 Biases and Risks

From the terms and conditions of iGenius for Modello Italia:

Modello Italia è concepito per essere utilizzato da tutti e per adattarsi a una vasta gamma di casi d'uso. È stato progettato con l'obiettivo di essere accessibile a persone provenienti da background, esperienze e prospettive diverse. Modello Italia si rivolge agli utenti e alle loro esigenze senza inserire giudizi superflui o normative, riconoscendo al contempo che anche contenuti potenzialmente problematici in determinati contesti possono avere scopi validi in altri. Il rispetto per la dignità e l'autonomia di tutti gli utenti, specialmente in termini di libertà di pensiero ed espressione, è un pilastro fondamentale del suo design. Tuttavia, essendo una nuova tecnologia, Modello Italia comporta rischi legati al suo utilizzo. I test condotti finora sono stati eseguiti in italiano e non hanno potuto coprire tutte le possibili situazioni. Pertanto, come per tutti gli LLM, non è possibile prevedere in anticipo gli output di Modello Italia e il modello potrebbe in alcuni casi generare risposte imprecise, tendenziose o altre risposte discutibili. Prima di utilizzare Modello Italia in qualsiasi contesto, gli sviluppatori sono fortemente incoraggiati a eseguire test di sicurezza e adattamento specifici per le loro applicazioni.

We are aware of the biases and potential problematic/toxic content that current pretrained large language models exhibit: more specifically, as probabilistic models of (Italian and English) languages, they reflect and amplify the biases of their training data.

For more information about this issue, please refer to our survey paper:

Biases in Large Language Models: Origins, Inventory, and Discussion

🚨 Model architecture

The model architecture is based on GPT-NeoX.

Downloads last month: 1,002

GGUF

Model size

9B params

Architecture

gptneox

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

32-bit

Model tree for fbaldassarri/modello-italia-9B-GGUF

Base model

sapienzanlp/modello-italia-9b

Quantized

(2)

this model