Instructions to use Vicgrace/ARIS-Gold-1.5B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use Vicgrace/ARIS-Gold-1.5B 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 Vicgrace/ARIS-Gold-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Vicgrace/ARIS-Gold-1.5B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Vicgrace/ARIS-Gold-1.5B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Vicgrace/ARIS-Gold-1.5B: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 Vicgrace/ARIS-Gold-1.5B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Vicgrace/ARIS-Gold-1.5B: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 Vicgrace/ARIS-Gold-1.5B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Vicgrace/ARIS-Gold-1.5B:Q4_K_M

Use Docker

docker model run hf.co/Vicgrace/ARIS-Gold-1.5B:Q4_K_M

LM Studio
Jan

vLLM

How to use Vicgrace/ARIS-Gold-1.5B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Vicgrace/ARIS-Gold-1.5B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Vicgrace/ARIS-Gold-1.5B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Vicgrace/ARIS-Gold-1.5B:Q4_K_M

Ollama
How to use Vicgrace/ARIS-Gold-1.5B with Ollama:
```
ollama run hf.co/Vicgrace/ARIS-Gold-1.5B:Q4_K_M
```

Unsloth Studio

How to use Vicgrace/ARIS-Gold-1.5B 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 Vicgrace/ARIS-Gold-1.5B 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 Vicgrace/ARIS-Gold-1.5B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Vicgrace/ARIS-Gold-1.5B to start chatting

How to use Vicgrace/ARIS-Gold-1.5B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vicgrace/ARIS-Gold-1.5B: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": "Vicgrace/ARIS-Gold-1.5B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use Vicgrace/ARIS-Gold-1.5B with Docker Model Runner:
```
docker model run hf.co/Vicgrace/ARIS-Gold-1.5B:Q4_K_M
```

Lemonade

How to use Vicgrace/ARIS-Gold-1.5B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Vicgrace/ARIS-Gold-1.5B:Q4_K_M

Run and chat with the model

lemonade run user.ARIS-Gold-1.5B-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use Vicgrace/ARIS-Gold-1.5B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vicgrace/ARIS-Gold-1.5B: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 Vicgrace/ARIS-Gold-1.5B:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use Vicgrace/ARIS-Gold-1.5B with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Vicgrace/ARIS-Gold-1.5B: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 "Vicgrace/ARIS-Gold-1.5B: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"

🌾 ARIS-Gold-1.5B

Offline AI Agricultural Advisor for Nigerian Farmers

ARIS-Gold is a fine-tuned version of Qwen2.5-1.5B optimised for offline agricultural advice in English and Nigerian Pidgin. It is quantized to GGUF Q4_K_M for efficient CPU inference on 8GB laptops.

🏆 ADTC 2026 Submission

This model was built for the Africa Deep Tech Challenge 2026 (Laptop LLM Track) – designed to run on budget hardware with no internet connection.

Metric	Value
Model	Qwen2.5-1.5B (Fine-tuned)
Quantization	GGUF Q4_K_M
Model Size	941 MB
Peak RAM	1.69 GB
Inference Speed	16.0 tokens/sec
ARC-Easy Accuracy	76%
ADTC Score	83.18
Languages	English, Nigerian Pidgin

🌍 What It Does

Diagnoses crop diseases (maize, cassava, yam, rice, tomatoes, pepper, cocoa)
Provides livestock advice (poultry, goats, cattle, fish farming)
Answers in Nigerian Pidgin (pcm) and English
Runs 100% offline – no internet required
Works on 8GB RAM laptops with no GPU

📥 How to Use

With `llama.cpp`

# Download the model
wget https://huggingface.co/Vicgrace/ARIS-Gold-1.5B/resolve/main/qwen2.5-1.5b-instruct.Q4_K_M.gguf

# Run inference (temperature 0.0 for safety)
llama-cli -m qwen2.5-1.5b-instruct.Q4_K_M.gguf -p "User: My cassava leaves are showing yellow-green mosaic patterns and the plant is stunted. What disease is this and how can I manage it?\nAssistant:" -n 256 --temp 0.0

# Interactive chat mode
llama-cli -m qwen2.5-1.5b-instruct.Q4_K_M.gguf -cnv -t 4 --chat-template qwen

🔧 Fine-Tuning Details

Base model: Qwen/Qwen2.5-1.5B-Instruct
Method: QLoRA (8 epochs)
Training data: 497 agricultural Q&A pairs + 50 ARC-Easy questions
Languages: English + Nigerian Pidgin
Framework: Unsloth + llama.cpp

📊 Evaluation

Benchmark	Score
ARC-Easy (50 samples)	76%
ADTC Throughput (Sperf)	100.0
ADTC Efficiency (Seff)	75.86
ADTC Total Score	83.18

📁 Files

qwen2.5-1.5b-instruct.Q4_K_M.gguf – Quantized model file (941 MB)

📄 License

Apache 2.0 – open-source and free to use.

🤝 Credits

Built by Victor Nwaruwe for the Africa Deep Tech Challenge 2026
Fine-tuned with Unsloth
Inference with llama.cpp

ARIS — AI for the hardware Africa actually has. 🌾 ```

Downloads last month: 255

GGUF

Model size

2B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for Vicgrace/ARIS-Gold-1.5B

Base model

Qwen/Qwen2.5-1.5B

Finetuned

Qwen/Qwen2.5-1.5B-Instruct

Quantized

(264)

this model

🌾 ARIS-Gold-1.5B

🏆 ADTC 2026 Submission

🌍 What It Does

📥 How to Use

With llama.cpp

🔧 Fine-Tuning Details

📊 Evaluation

📁 Files

📄 License

🤝 Credits

Model tree for Vicgrace/ARIS-Gold-1.5B

With `llama.cpp`