Instructions to use VLTX/VertaLily-1.2-1B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use VLTX/VertaLily-1.2-1B-GGUF with Transformers:

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

pipe = pipeline("text-generation", model="VLTX/VertaLily-1.2-1B-GGUF")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("VLTX/VertaLily-1.2-1B-GGUF", dtype="auto")

llama-cpp-python

How to use VLTX/VertaLily-1.2-1B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="VLTX/VertaLily-1.2-1B-GGUF",
	filename="VertaLily-1.2-1B-Q3_K-stable.gguf",
)

llm.create_chat_completion(
	messages = [
		{
			"role": "user",
			"content": "What is the capital of France?"
		}
	]
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use VLTX/VertaLily-1.2-1B-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Use Docker

docker model run hf.co/VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use VLTX/VertaLily-1.2-1B-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "VLTX/VertaLily-1.2-1B-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "VLTX/VertaLily-1.2-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

SGLang

How to use VLTX/VertaLily-1.2-1B-GGUF 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 "VLTX/VertaLily-1.2-1B-GGUF" \
    --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": "VLTX/VertaLily-1.2-1B-GGUF",
		"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 "VLTX/VertaLily-1.2-1B-GGUF" \
        --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": "VLTX/VertaLily-1.2-1B-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Ollama
How to use VLTX/VertaLily-1.2-1B-GGUF with Ollama:
```
ollama run hf.co/VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
```

Unsloth Studio

How to use VLTX/VertaLily-1.2-1B-GGUF with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VLTX/VertaLily-1.2-1B-GGUF to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for VLTX/VertaLily-1.2-1B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for VLTX/VertaLily-1.2-1B-GGUF to start chatting

How to use VLTX/VertaLily-1.2-1B-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use VLTX/VertaLily-1.2-1B-GGUF with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use VLTX/VertaLily-1.2-1B-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Configure OpenClaw

# Install OpenClaw:
npm install -g openclaw@latest
# Register the local server and set it as the default model:
openclaw onboard --non-interactive --mode local \
  --auth-choice custom-api-key \
  --custom-base-url http://127.0.0.1:8080/v1 \
  --custom-model-id "VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M" \
  --custom-provider-id llama-cpp \
  --custom-compatibility openai \
  --custom-text-input \
  --accept-risk \
  --skip-health

Run OpenClaw

openclaw agent --local --agent main --message "Hello from Hugging Face"

Docker Model Runner
How to use VLTX/VertaLily-1.2-1B-GGUF with Docker Model Runner:
```
docker model run hf.co/VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M
```

Lemonade

How to use VLTX/VertaLily-1.2-1B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull VLTX/VertaLily-1.2-1B-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.VertaLily-1.2-1B-GGUF-Q4_K_M

List all available models

lemonade list

BOSSHuggingFace commited on May 17

Commit

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+---
+license: apache-2.0
+language:
+- en
+tags:
+- vltx
+- sovereign
+- gguf
+- techina-x
+- void
+- verta-lily
+library_name: transformers
+pipeline_tag: text-generation
+---
+<div align="center">
+![VertaLily Logo](VertaLilysmall.png)
+<div style="display: flex; justify-content: center; gap: 0.8em; margin-top: 1em; margin-bottom: 1.5em;">
+  <a href="https://huggingface.co/BOSSHuggingFace/VertaLily-Techina-X-Student-PS-1B-GGUF"><strong> Sovereign Home</strong></a> •
+  <a href="https://huggingface.co/BOSSHuggingFace/VertaLily-Techina-X-Student-PS-1B-GGUF/tree/main"><strong> Model Vault</strong></a> •
+  <a href="https://huggingface.co/BOSSHuggingFace"><strong> Architect Repo</strong></a>
+</div>
+*VertaLily Techina X: Student-Perfect Soil*
+*FAS 1.0 Alignment | Architecture: vltx |
+Identifier: [CDK_VRT:5F9C2E1A7B:KVC0904A]*
+---
+</div>
+### Model Specifications
+- **Architecture:** `vltx`
+- **Deployment:** Optimized for ARM CPU and Higher Computations
+---
+## Model Purpose: Knowledge Harvest for writing any AI weight & Low-power Agentic Inferences
+**VertaLily-Student-PS-1B** is a specialized 1-billion parameter student model, quantized to **4-bit (Q4_K) or 3-bit (Q3_K)** for extreme efficiency. Unlike general-purpose small models, this "Perfect Soil" variant is architected specifically as a **distillation vessel**, **writing model weights**, or **cloud/local agent inferences**.
+It's primary purpose is to act as a high-affinity student for **Knowledge Harvesting**. It is designed to learn the logits, reasoning patterns, and hidden representations of larger "Teacher" models with minimal information loss or any information fields it exposed with.
+### Key Specifications:
+- **Model Name:** VertaLily-Student-PS-1B
+- **Quantization:** Q3_K & Q4_K (GGUF format)
+---
+## Distillation Strategy
+This model is intended to be used in **Soft Target Distillation** and **Intermediate Representation Matching**.
+1. **Vessel Affinity:** Optimized for high learning rates during the distillation phase.
+2. **Logit Mimicry:** Designed to mirror the probability distributions (soft targets) of Teacher models across diverse tasks.
+3. **Perfect Soil:** Neutralized pre-training weights to prevent "Teacher-Student Conflict," ensuring the student inherits the Teacher's reasoning without bias from poor quality base data.
+---
+## Usage
+This model is optimized for use with `llama.cpp`, `Ollama`, and other inference engines of any llama.cpp based.
+# Running the vessel in your terminal:
+```bash
+./llama-cli -m VertaLily(any x file).gguf -p "The Architect's Command:"
+```
+---
+## About This Model
+This student model inherits the refined reasoning architecture of **Verta Lily Techina X** — also known as **Verta Lily - VOID** — a layered thinking system I first developed in 2024. This design predates and complements the dense, single-pass inference breakthroughs seen in models like DeepSeek (January 2025). Where others optimize for speed, VOID optimizes for **depth, safety, and recursive self-correction**.
+## Compatibility & Deployment
+The model is fully compatible with **llama.cpp** and any of it's forks or heritage implementations. While I have not yet publicly released a dedicated VLTX fork of llama.cpp, that work is highly already on the roadmap. In due time, I will contribute the VLTX inference architecture to the public via a pull request or release my own fork — one optimized to load and run this model with full functional relevances.
+## Scalability & Swarm Reasoning
+This model is designed to be lightweight enough to run on **low-CPU environments**, yet flexible enough to scale across **CPU + GPU inference sets** when more power is needed. Multiple instances can be run in **parallel swarms**, each trained or exposed to different knowledge domains — books, research papers, technical fields — and then interleaved or merged. This makes it possible to **grow new weights from scratch**, building a complete learning library for future AI systems.
+## Bring Your Own Agent Setting
+This model comes pre-trained on tool use and web search inferences. When paired with a well-structured framework, it performs smoothly and reliably — and in many cases, it can exceed the capabilities of even the most advanced frontier models available today.
+## 🏹 Extended Capabilities
+This model is designed for versatility across a wide range of practical applications, including:
+- Web scraping and automated data extraction
+- Computer use for interface navigation and task execution
+- Integration with extended internet knowledge bases
+- Frontend network branching across cloud, mobile, and hybrid environments
+- Full local inference with no internet connection required
+- Deployment in robotic systems as a reasoning engine
+- Bootstrapping and training new AI models from the ground up
+## A Personal Note from the Creator
+I develop this work as a **passion project** — a hobby pursued with love, not yet a fully funded or full-time endeavor. Progress may sometimes feel slow, but every line of code and every layer of reasoning is crafted with care. Thank you for your patience, your curiosity, and your trust.
+---
+*With sovereignty and warmth,*
+**KEVIN**
+*Architect of Verta Lily AI — VLTX Lab*