Instructions to use ClaudioItaly/Pullulation-2-9B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ClaudioItaly/Pullulation-2-9B with Transformers:

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

pipe = pipeline("text-generation", model="ClaudioItaly/Pullulation-2-9B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ClaudioItaly/Pullulation-2-9B")
model = AutoModelForCausalLM.from_pretrained("ClaudioItaly/Pullulation-2-9B")
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

vLLM

How to use ClaudioItaly/Pullulation-2-9B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ClaudioItaly/Pullulation-2-9B"
# 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/Pullulation-2-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ClaudioItaly/Pullulation-2-9B

SGLang

How to use ClaudioItaly/Pullulation-2-9B 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/Pullulation-2-9B" \
    --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/Pullulation-2-9B",
		"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/Pullulation-2-9B" \
        --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/Pullulation-2-9B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ClaudioItaly/Pullulation-2-9B with Docker Model Runner:
```
docker model run hf.co/ClaudioItaly/Pullulation-2-9B
```

Pullulation-2-9B / README.md

ClaudioItaly

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	---
	base_model:
	- jsgreenawalt/gemma-2-9B-it-advanced-v2.1
	- nbeerbower/gemma2-gutenberg-9B
	- ifable/gemma-2-Ifable-9B
	- UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
	- lemon07r/Gemma-2-Ataraxy-9B
	- BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference
	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 [Model Stock](https://arxiv.org/abs/2403.19522) merge method using [nbeerbower/gemma2-gutenberg-9B](https://huggingface.co/nbeerbower/gemma2-gutenberg-9B) as a base.

	### Models Merged

	The following models were included in the merge:
	* [jsgreenawalt/gemma-2-9B-it-advanced-v2.1](https://huggingface.co/jsgreenawalt/gemma-2-9B-it-advanced-v2.1)
	* [ifable/gemma-2-Ifable-9B](https://huggingface.co/ifable/gemma-2-Ifable-9B)
	* [UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3](https://huggingface.co/UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3)
	* [lemon07r/Gemma-2-Ataraxy-9B](https://huggingface.co/lemon07r/Gemma-2-Ataraxy-9B)
	* [BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference](https://huggingface.co/BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	models:
	- model: nbeerbower/gemma2-gutenberg-9B
	parameters:
	weight: 0.25
	- model: UCLA-AGI/Gemma-2-9B-It-SPPO-Iter3
	parameters:
	weight: 0.25
	- model: ifable/gemma-2-Ifable-9B
	parameters:
	weight: 0.25
	- model: jsgreenawalt/gemma-2-9B-it-advanced-v2.1
	parameters:
	weight: 0.25
	- model: lemon07r/Gemma-2-Ataraxy-9B
	parameters:
	weight: 0.25
	- model: BAAI/Gemma2-9B-IT-Simpo-Infinity-Preference
	parameters:
	weight: 0.25

	base_model: nbeerbower/gemma2-gutenberg-9B # Modello di riferimento per la fusione

	parameters:
	t: [0, 0.33, 0.67, 1] # Parametri di interpolazione
	dtype: bfloat16
	merge_method: model_stock # Metodo di fusione


	```