Instructions to use anakin87/gemma-2-9b-neogenesis-ita with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use anakin87/gemma-2-9b-neogenesis-ita with Transformers:

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

pipe = pipeline("text-generation", model="anakin87/gemma-2-9b-neogenesis-ita")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("anakin87/gemma-2-9b-neogenesis-ita")
model = AutoModelForCausalLM.from_pretrained("anakin87/gemma-2-9b-neogenesis-ita", device_map="auto")
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]:]))

Inference
Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use anakin87/gemma-2-9b-neogenesis-ita with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "anakin87/gemma-2-9b-neogenesis-ita"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "anakin87/gemma-2-9b-neogenesis-ita",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/anakin87/gemma-2-9b-neogenesis-ita

SGLang

How to use anakin87/gemma-2-9b-neogenesis-ita 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 "anakin87/gemma-2-9b-neogenesis-ita" \
    --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": "anakin87/gemma-2-9b-neogenesis-ita",
		"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 "anakin87/gemma-2-9b-neogenesis-ita" \
        --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": "anakin87/gemma-2-9b-neogenesis-ita",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use anakin87/gemma-2-9b-neogenesis-ita with Docker Model Runner:
```
docker model run hf.co/anakin87/gemma-2-9b-neogenesis-ita
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Gemma 2 9B Neogenesis ITA

Fine-tuned version of VAGOsolutions/SauerkrautLM-gemma-2-9b-it optimized for better performance in Italian.

Good model with 9.24 billion parameters
Supports 8k context length

GGUF quants

Need a smaller model? Try gemma-2-2b-neogenesis-ita.

🎮 Usage

💬🇮🇹 Try the model on Hugging Face Spaces

Text generation with Transformers

import torch
from transformers import pipeline

model_id="anakin87/gemma-2-9b-neogenesis-ita"

pipe = pipeline(
    "text-generation",
    model=model_id,
    model_kwargs={"torch_dtype": torch.bfloat16},
    device="cuda",
)

messages = [{"role": "user", "content": "Cos'è l'interesse composto? Spiega in maniera semplice e chiara."}]
outputs = pipe(messages, max_new_tokens=500)

print(outputs[0]["generated_text"][1]["content"])

🏆 Evaluation Results

The model was submitted and evaluated in the Open Ita LLM Leaderboard, the most popular leaderboard for Italian Language Models.

Model	MMLU_IT	ARC_IT	HELLASWAG_IT	Average
google/gemma-2-9b-it	65.67	55.6	68.95	63.41
VAGOsolutions/SauerkrautLM-gemma-2-9b-it	65.76	61.25	72.10	66.37
anakin87/gemma-2-9b-neogenesis-ita	65.82	61.25	73.29	66.79

These results establish this model as a strong 9B model for Italian, outperforming 13-14B models and even surpassing some in the 30-70B range.

🔧 Training details

The model was fine-tuned using Hugging Face TRL and applying Direct Preference Optimization.

I adopted a relatively new technique for parameter-efficient learning: Spectrum. The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest. Specifically, training focused on the top 20% most informative layers.

Batch size: 16; learning rate: 1e-6; epochs: 1.

The training process took approximately 12 hours on a single NVIDIA A100 GPU (80GB VRAM).

For the training code, see the DPO section in this 📓 Kaggle notebook, modified to use a different base model, hyperparameters, and no on-policy data.

🗃️ Training data

The model was trained primarily on Italian data, with a small portion of English data included.

For Direct Preference Optimization

Italian data
English data
- mlabonne/orpo-dpo-mix-40k

🙏 Thanks to the authors for providing these datasets.

🛡️ Safety

While this model was not specifically fine-tuned for safety, its selective training with the Spectrum technique helps preserve certain safety features from the original model.