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

Libraries

How to use UmbrellaInc/Neo_T-Virus-3.2-1B with Transformers:

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

pipe = pipeline("text-generation", model="UmbrellaInc/Neo_T-Virus-3.2-1B")

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UmbrellaInc/Neo_T-Virus-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("UmbrellaInc/Neo_T-Virus-3.2-1B")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use UmbrellaInc/Neo_T-Virus-3.2-1B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "UmbrellaInc/Neo_T-Virus-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": "UmbrellaInc/Neo_T-Virus-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/UmbrellaInc/Neo_T-Virus-3.2-1B

SGLang

How to use UmbrellaInc/Neo_T-Virus-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 "UmbrellaInc/Neo_T-Virus-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": "UmbrellaInc/Neo_T-Virus-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 "UmbrellaInc/Neo_T-Virus-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": "UmbrellaInc/Neo_T-Virus-3.2-1B",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use UmbrellaInc/Neo_T-Virus-3.2-1B with Docker Model Runner:
```
docker model run hf.co/UmbrellaInc/Neo_T-Virus-3.2-1B
```
Browse Quantizations to use this model in llama.cpp, Ollama, LM Studio, or any compatible app.

Neo T-Virus 3.2 1B

SUCCESS

☣ FULLY UNCENSORED VERSION ☣

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:



# Umbrella Corporation Official Merge Protocol v3.2
# Author: Dr. Novaciano
# Objective: Integrate T-Virus_Epsilon traits into the base Prototype-Virus-ENFORCE-3.2-1B model
#            with minimal behavioral censorship while maintaining structural coherence.
# PROJECT: Neo_T-Virus
models:
  - model: UmbrellaInc/Prototype-Virus.FINAL-3.2-1B  # Experimental viral strain neural imprint
  - model: UmbrellaInc/T-Virus_Epsilon.Arklay-3.2-1B                 # Baseline cognitive template, "safe mode"

merge_method: slerp  # Spherical Linear Interpolation to preserve extreme viral traits smoothly
base_model: UmbrellaInc/T-Virus_Epsilon.Arklay-3.2-1B  # Anchor model for stable latent space

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]

# Notes:
# - t=0.0   -> Pure Prototype-Virus-ENFORCE, fully stable, heavily censored
# - t=0.25  -> Slight viral traits, minimal influence on prompt handling
# - t=0.5   -> Balanced merge, moderate reduction in censorship
# - t=0.75  -> Strong T-Virus traits, significantly less censoring
# - t=0.95  -> Near-total viral influence, maximum expressive freedom, minimal autoprotection

# Recommendation: Use the t=0.75 and t=0.95 variants for experimental output with
# minimal restriction, but verify coherence in high-stakes prompts.