Instructions to use ZenithLLM/ZenAlta-Draft 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-Draft 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-Draft:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZenithLLM/ZenAlta-Draft: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-Draft:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ZenithLLM/ZenAlta-Draft: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-Draft:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ZenithLLM/ZenAlta-Draft: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-Draft:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ZenithLLM/ZenAlta-Draft:Q4_K_M

Use Docker

docker model run hf.co/ZenithLLM/ZenAlta-Draft:Q4_K_M

LM Studio
Jan

vLLM

How to use ZenithLLM/ZenAlta-Draft with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/ZenithLLM/ZenAlta-Draft:Q4_K_M

Ollama
How to use ZenithLLM/ZenAlta-Draft with Ollama:
```
ollama run hf.co/ZenithLLM/ZenAlta-Draft:Q4_K_M
```
Unsloth Desktop

How to use ZenithLLM/ZenAlta-Draft 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-Draft: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-Draft:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use ZenithLLM/ZenAlta-Draft with Docker Model Runner:
```
docker model run hf.co/ZenithLLM/ZenAlta-Draft:Q4_K_M
```

Lemonade

How to use ZenithLLM/ZenAlta-Draft with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ZenithLLM/ZenAlta-Draft:Q4_K_M

Run and chat with the model

lemonade run user.ZenAlta-Draft-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use ZenithLLM/ZenAlta-Draft 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-Draft: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-Draft:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use ZenithLLM/ZenAlta-Draft 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-Draft: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-Draft: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"

ZenAlta-Draft / README.md

ZenithLLM

Update comprehensive Model Card with speculative decoding specs

7136a63 verified 3 days ago

preview code

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metadata

language:
  - en
license: llama3.2
base_model: ZenithLLM/ZenAlta-1-3B-Phase2
tags:
  - llama
  - llama3.2
  - speculative-decoding
  - draft-model
  - gguf
  - conversational
  - roleplay
  - mobile-llm
pipeline_tag: text-generation

⚡ Zen Alta 4-Layer Speculative Decoding Draft Model (~790M)

Zen Alta Draft is a ultra-lightweight, 4-layer speculative decoding companion model engineered by ZenithLLM. Sliced from the top of the 24-layer Zen Alta architecture, it shares the exact same 128,256 BPE vocabulary and embedding/LM head, enabling lossless 2× speculative inference acceleration in llama.cpp, vLLM, and mobile runtimes.

🎯 What is Speculative Decoding?

In standard autoregressive generation, deep models calculate every single token sequentially (e.g. 24 transformer layers per token).

With Zen Alta Draft:

The Fast Draft (4 Layers, 790M): Quickly guesses 4–5 candidate tokens in parallel in just ~20–30ms.
The Target Model (Zen Alta 24 Layers, 2.8B): Verifies all proposed tokens in a single parallel forward pass (~40ms).
Result: Accepted tokens are committed simultaneously, achieving 40–50+ tokens/sec on mobile chips with 0% degradation in output quality or persona.

📦 Model Specifications

Parameter	Value
Base Architecture	Llama 3.2 (CausalLM)
Hidden Layers	4 (vs 24 in Target model)
Hidden Dimension	3072
Intermediate Size	8192
Attention Heads	24 query heads / 8 KV heads
Vocabulary Size	128,256 (Identical to Llama 3.2 & Zen Alta)
Context Length	131,072 tokens
RoPE Theta	500,000.0

📂 Repository Contents

This consolidated repository contains both the raw PyTorch weights and the ready-to-run quantized GGUF:

File	Size	Description
`model.safetensors`	1.52 GB	Unquantized FP16 PyTorch weights (4 layers)
`zen-alta-draft-q4_k_m.gguf`	545.72 MB	Quantized 4-bit medium GGUF for `llama.cpp` & mobile
`config.json`	< 1 KB	4-layer model configuration
`tokenizer.json`	16.4 MB	Fast BPE tokenizer definition
`chat_template.jinja`	< 4 KB	Llama 3.2 conversational chat template

🚀 How to Run in llama.cpp

Speculative Decoding (Target + Draft Pairing)

Download the target model from ZenithLLM/ZenAlta-1-3B-Phase2-GGUF and the draft model from this repo:

# Speculative decoding command
./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 who are you?<|eot_id|><|start_header_id|>assistant<|end_header_id|>\n\n" \
  -n 128

Standalone Inference (Fast Preview)

./llama-cli -m zen-alta-draft-q4_k_m.gguf -p "what is up" -n 64

🔗 Related Models

Target Model (LoRA Adapter): ZenithLLM/ZenAlta-1-3B-Phase2
Target Model (GGUF Q4_K_M): ZenithLLM/ZenAlta-1-3B-Phase2-GGUF
Base Pruned Model (24-Layer): ZenithLLM/ZenAlta-1-3B-Pruned