Instructions to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ZenithLLM/ZenAlta-1-3B-Phase2-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": "ZenithLLM/ZenAlta-1-3B-Phase2-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

Ollama
How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with Ollama:
```
ollama run hf.co/ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
```
Unsloth Desktop

How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

Configure the model in Pi

# Install Pi:
npm install -g @earendil-works/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with Docker Model Runner:
```
docker model run hf.co/ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M
```

Lemonade

How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.ZenAlta-1-3B-Phase2-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-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 ZenithLLM/ZenAlta-1-3B-Phase2-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ZenithLLM/ZenAlta-1-3B-Phase2-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf ZenithLLM/ZenAlta-1-3B-Phase2-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 "ZenithLLM/ZenAlta-1-3B-Phase2-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"

🚀 Zen Alta 1-3B (Phase 2 GGUF Q4_K_M)

Zen Alta is a 24-layer, ~2.8B parameter causal language model fine-tuned for high-speed, casual conversational roleplay and on-device execution. Built on top of a pruned Llama 3.2 3B foundation and aligned via 800 steps of Direct Preference Optimization (DPO).

This repository provides the quantized Q4_K_M GGUF binary (1.69 GB) for llama.cpp, Termux, and local edge deployment.

🎭 Persona & Style Attributes

Ultra-Casual Tone: Default lowercase, brief responses (<15 words), conversational slang.
Anti-AI / Targeted Unlearning: Actively deflects homework, academic tasks, coding requests, and corporate disclaimers ("bro google is free", "im not writing an essay for u").
Fast Banter: Retains witty banter without boilerplate AI safety lecturing.

⚡ 2× Acceleration via Speculative Decoding

Zen Alta is engineered to run in tandem with its official 4-layer companion draft model: ZenithLLM/ZenAlta-Draft (zen-alta-draft-q4_k_m.gguf, 545 MB).

Running with Speculative Decoding in llama.cpp:

# 1. Download both models
huggingface-cli download ZenithLLM/ZenAlta-1-3B-Phase2-GGUF ZenAlta-1-3B-Pruned.Q4_K_M.gguf --local-dir .
huggingface-cli download ZenithLLM/ZenAlta-Draft zen-alta-draft-q4_k_m.gguf --local-dir .

# 2. Run paired speculative inference
./llama-cli \
  -m ZenAlta-1-3B-Pruned.Q4_K_M.gguf \
  -md zen-alta-draft-q4_k_m.gguf \
  --draft-max 5 \
  -p "<|start_header_id|>user<|end_header_id|>\n\nhey what are you doing<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" \
  -n 128

Expected Speed: 40–50+ tokens/sec on modern mobile and edge hardware (vs ~22 t/s without draft).

⚙️ Recommended Sampling Parameters

{
  "temperature": 0.75,
  "top_p": 0.90,
  "min_p": 0.05,
  "repetition_penalty": 1.15,
  "max_new_tokens": 64
}

📦 Model Details

Architecture: Pruned Llama 3.2 (24 layers, 3072 hidden size, 8192 intermediate)
Quantization: Q4_K_M (1.69 GB)
Context Length: 131,072 tokens
Vocabulary: 128,256 BPE

🔗 Related Repositories

LoRA Adapter (Phase 2): ZenithLLM/ZenAlta-1-3B-Phase2
Draft Model (Speculative Decoding): ZenithLLM/ZenAlta-Draft
Base Pruned Weights (24-Layer): ZenithLLM/ZenAlta-1-3B-Pruned

Downloads last month: 178

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

4-bit

Model tree for ZenithLLM/ZenAlta-1-3B-Phase2-GGUF

Base model

ZenithLLM/ZenAlta-1-3B-Pruned

Adapter

ZenithLLM/ZenAlta-1-3B-Phase2

Quantized

(3)

this model