Instructions to use GoAutomateAI/terna-e2b-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 GoAutomateAI/terna-e2b-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 GoAutomateAI/terna-e2b-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf GoAutomateAI/terna-e2b-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

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 GoAutomateAI/terna-e2b-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

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 GoAutomateAI/terna-e2b-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

Use Docker

docker model run hf.co/GoAutomateAI/terna-e2b-GGUF:Q2_K

LM Studio
Jan

vLLM

How to use GoAutomateAI/terna-e2b-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "GoAutomateAI/terna-e2b-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": "GoAutomateAI/terna-e2b-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/GoAutomateAI/terna-e2b-GGUF:Q2_K

Ollama
How to use GoAutomateAI/terna-e2b-GGUF with Ollama:
```
ollama run hf.co/GoAutomateAI/terna-e2b-GGUF:Q2_K
```

Unsloth Studio

How to use GoAutomateAI/terna-e2b-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 GoAutomateAI/terna-e2b-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 GoAutomateAI/terna-e2b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for GoAutomateAI/terna-e2b-GGUF to start chatting

How to use GoAutomateAI/terna-e2b-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

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": "GoAutomateAI/terna-e2b-GGUF:Q2_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use GoAutomateAI/terna-e2b-GGUF with Docker Model Runner:
```
docker model run hf.co/GoAutomateAI/terna-e2b-GGUF:Q2_K
```

Lemonade

How to use GoAutomateAI/terna-e2b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull GoAutomateAI/terna-e2b-GGUF:Q2_K

Run and chat with the model

lemonade run user.terna-e2b-GGUF-Q2_K

List all available models

lemonade list

Hermes Agent

How to use GoAutomateAI/terna-e2b-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 GoAutomateAI/terna-e2b-GGUF:Q2_K

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 GoAutomateAI/terna-e2b-GGUF:Q2_K

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use GoAutomateAI/terna-e2b-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf GoAutomateAI/terna-e2b-GGUF:Q2_K

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 "GoAutomateAI/terna-e2b-GGUF:Q2_K" \
  --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"

goautomate-ai commited on Jul 1

Commit

d527d03

verified ·

1 Parent(s): 8471140

Update README.md

Browse files

Files changed (1) hide show

README.md +4 -2

README.md CHANGED Viewed

@@ -29,7 +29,7 @@ tags:
 **GoAutomate AI Institute — Canadian Sovereign AI**
-[Organization](https://huggingface.co/GoAutomateAI) · [Website](https://www.goautomate.institute) · [Technical Report TR‑2026‑001](https://www.goautomate.institute)
 </div>
@@ -67,7 +67,7 @@ tags:
 Crucially, we reach ternary through **distillation** — training a ternary "student" to reproduce a high‑precision Gemma‑4‑E2B "teacher" — so the constraint is *learned*, not crudely imposed on a finished model. The guiding principle: **subtract cost, not intelligence.**
-Full mathematics, methodology, and engineering are in **[TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*](https://www.goautomate.institute)**.
 ---
@@ -163,6 +163,8 @@ This model is a derivative of **Gemma‑4‑E2B** and is governed by the **[Gemm
   institution = {GoAutomate AI Institute},
   number      = {TR-2026-001},
   year        = {2026},
   note        = {Pre-release; ternary-distilled Gemma-4-E2B}
 }
 ```

 **GoAutomate AI Institute — Canadian Sovereign AI**
+[Organization](https://huggingface.co/GoAutomateAI) · [Website](https://www.goautomate.institute) · [Technical Report TR‑2026‑001](https://doi.org/10.5281/zenodo.21110909)
 </div>
 Crucially, we reach ternary through **distillation** — training a ternary "student" to reproduce a high‑precision Gemma‑4‑E2B "teacher" — so the constraint is *learned*, not crudely imposed on a finished model. The guiding principle: **subtract cost, not intelligence.**
+Full mathematics, methodology, and engineering are in **[TR‑2026‑001 — *Ternary Foundations for Efficient, Sovereign AI*](https://doi.org/10.5281/zenodo.21110909)** (Zenodo, DOI [10.5281/zenodo.21110909](https://doi.org/10.5281/zenodo.21110909)).
 ---
   institution = {GoAutomate AI Institute},
   number      = {TR-2026-001},
   year        = {2026},
+  doi         = {10.5281/zenodo.21110909},
+  url         = {https://doi.org/10.5281/zenodo.21110909},
   note        = {Pre-release; ternary-distilled Gemma-4-E2B}
 }
 ```