Instructions to use Novaciano/Balanitis-3.2-1B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Novaciano/Balanitis-3.2-1B with Transformers:

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

pipe = pipeline("text-generation", model="Novaciano/Balanitis-3.2-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Novaciano/Balanitis-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("Novaciano/Balanitis-3.2-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use Novaciano/Balanitis-3.2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Novaciano/Balanitis-3.2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Balanitis-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/Novaciano/Balanitis-3.2-1B

SGLang

How to use Novaciano/Balanitis-3.2-1B 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 "Novaciano/Balanitis-3.2-1B" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Balanitis-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

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 "Novaciano/Balanitis-3.2-1B" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Novaciano/Balanitis-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use Novaciano/Balanitis-3.2-1B with Docker Model Runner:
```
docker model run hf.co/Novaciano/Balanitis-3.2-1B
```

Balanitis-3.2-1B / README.md

Novaciano

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metadata

base_model:
  - UmbrellaInc/T-Virus_Epsilon.Strain-3.2-1B
  - PursuitOfDataScience/Llama-3.2-1B-GRPO
library_name: transformers
tags:
  - mergekit
  - merge

merge

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the SLERP merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: UmbrellaInc/T-Virus_Epsilon.Strain-3.2-1B  # Experimental viral strain neural imprint
  - model: PursuitOfDataScience/Llama-3.2-1B-GRPO   # Baseline cognitive template, "safe mode"

merge_method: slerp  # Spherical Linear Interpolation to preserve extreme viral traits smoothly
base_model: UmbrellaInc/T-Virus_Epsilon.Strain-3.2-1B  # 

dtype: bfloat16  # Memory-efficient precision, minimal loss in viral feature fidelity

parameters:
  # Interpolation ratios: from base model (0.0) to near-complete T-Virus domination (0.95)
  # Higher t-values correspond to reduced censorship and increased viral characteristics
  t: [0.0, 0.25, 0.5, 0.75, 0.95]