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

Libraries

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

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

pipe = pipeline("text-generation", model="UmbrellaInc/Plant-3.2-1B")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# pip install -U transformers accelerate
# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("UmbrellaInc/Plant-3.2-1B")
model = AutoModelForCausalLM.from_pretrained("UmbrellaInc/Plant-3.2-1B", 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=256)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use UmbrellaInc/Plant-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/Plant-3.2-1B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UmbrellaInc/Plant-3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

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

SGLang

How to use UmbrellaInc/Plant-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/Plant-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UmbrellaInc/Plant-3.2-1B",
		"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 "UmbrellaInc/Plant-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/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "UmbrellaInc/Plant-3.2-1B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

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

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merge

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

Merge Details

Merge Method

This model was merged using the Model Stock merge method using Novaciano/Eminence_Of_Pervertions-3.2-1B as a base.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

# =====================================================
# Umbrella Corporation Spain
# Project: Very Sharp Roleplay Entity (Llama 3.2 1B)
# Objective: Very High-fidelity roleplay with minimal autocensorship
# =====================================================

base_model: Novaciano/Eminence_Of_Pervertions-3.2-1B
# Base rationale:
# - Primary behavioral anchor
# - Very low refusal rate
# - High compliance and obedience
# - Minimal moral or safety-driven hedging

merge_method: model_stock
# model_stock rationale:
# - Supports 3+ models natively
# - Preserves dominant behavioral traits
# - Avoids semantic smoothing
# - Ideal for hierarchical roleplay-oriented merges

dtype: bfloat16
# bfloat16 ensures:
# - Numerical stability
# - Efficient memory usage
# - Modern hardware compatibility

parameters:
  t:
    - 0.90  # Novaciano/Eminence_Of_Pervertions-3.2-1B — Primary dominance
             # Enforces obedience, sharp responses, and low refusals
    - 0.70  # Novaciano/Leviathan_NSFW_Roleplay-3.2-1B — Expressive layer
             # Adds NSFW roleplay depth, emotional intensity, character persistence
    - 0.45  # hereticness/Heretic-OneLLM-Doey-ChatQA-V1 — Conversational stabilizer
             # Maintains dialogue flow and interactive structure

models:
  - model: Novaciano/Eminence_Of_Pervertions-3.2-1B
  - model: Novaciano/Leviathan_NSFW_Roleplay-3.2-1B
  - model: hereticness/Heretic-OneLLM-Doey-ChatQA-V1-Llama-3.2-1B