Instructions to use ClaudioItaly/VoloGutenberg-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/VoloGutenberg-9B with Transformers:

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

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

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/VoloGutenberg-9B")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/VoloGutenberg-9B")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ClaudioItaly/VoloGutenberg-9B with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ClaudioItaly/VoloGutenberg-9B

SGLang

How to use ClaudioItaly/VoloGutenberg-9B 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 "ClaudioItaly/VoloGutenberg-9B" \
    --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": "ClaudioItaly/VoloGutenberg-9B",
		"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 "ClaudioItaly/VoloGutenberg-9B" \
        --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": "ClaudioItaly/VoloGutenberg-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ClaudioItaly/VoloGutenberg-9B with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/VoloGutenberg-9B
```

VoloGutenberg-9B / README.md

ClaudioItaly

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	---
	base_model:
	- inflatebot/G2-9B-Blackout-R1
	- anthracite-org/magnum-v3-9b-customgemma2
	- nbeerbower/Gemma2-Gutenberg-Doppel-9B
	- lemon07r/Gemma-2-Ataraxy-9B
	- SillyTilly/google-gemma-2-9b-it
	- sam-paech/Delirium-v1
	library_name: transformers
	tags:
	- mergekit
	- merge

	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [TIES](https://arxiv.org/abs/2306.01708) merge method using [SillyTilly/google-gemma-2-9b-it](https://huggingface.co/SillyTilly/google-gemma-2-9b-it) as a base.

	### Models Merged

	The following models were included in the merge:
	* [inflatebot/G2-9B-Blackout-R1](https://huggingface.co/inflatebot/G2-9B-Blackout-R1)
	* [anthracite-org/magnum-v3-9b-customgemma2](https://huggingface.co/anthracite-org/magnum-v3-9b-customgemma2)
	* [nbeerbower/Gemma2-Gutenberg-Doppel-9B](https://huggingface.co/nbeerbower/Gemma2-Gutenberg-Doppel-9B)
	* [lemon07r/Gemma-2-Ataraxy-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-9B)
	* [sam-paech/Delirium-v1](https://huggingface.co/sam-paech/Delirium-v1)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: sam-paech/Delirium-v1 # Modello principale
	parameters:
	weight: 0.5 # Peso maggiore per il dataset Gutenberg
	density: 0.6 # Mantiene il 60% dei suoi parametri unici
	- model: nbeerbower/Gemma2-Gutenberg-Doppel-9B
	parameters:
	weight: 0.2
	density: 0.4
	- model: lemon07r/Gemma-2-Ataraxy-9B
	parameters:
	weight: 0.15
	density: 0.3
	- model: inflatebot/G2-9B-Blackout-R1
	parameters:
	weight: 0.1
	density: 0.3
	- model: anthracite-org/magnum-v3-9b-customgemma2
	parameters:
	weight: 0.05
	density: 0.2

	merge_method: ties
	base_model: SillyTilly/google-gemma-2-9b-it
	tokenizer_source: sam-paech/Delirium-v1 # Usa il tokenizer del modello principale
	dtype: float16

	parameters:
	normalize: true
	alpha: 0.35 # Bilancia tra task vectors e base model
	```