Instructions to use Dasuperhub/DA with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Dasuperhub/DA with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Dasuperhub/DA",
	filename="DA-v3-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Dasuperhub/DA with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Dasuperhub/DA:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Dasuperhub/DA:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Dasuperhub/DA:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf Dasuperhub/DA: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 Dasuperhub/DA:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Dasuperhub/DA: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 Dasuperhub/DA:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Dasuperhub/DA:Q4_K_M

Use Docker

docker model run hf.co/Dasuperhub/DA:Q4_K_M

LM Studio
Jan

vLLM

How to use Dasuperhub/DA with vLLM:

Install from pip and serve model

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

Use Docker

docker model run hf.co/Dasuperhub/DA:Q4_K_M

Ollama
How to use Dasuperhub/DA with Ollama:
```
ollama run hf.co/Dasuperhub/DA:Q4_K_M
```

Unsloth Studio new

How to use Dasuperhub/DA 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 Dasuperhub/DA 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 Dasuperhub/DA to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Dasuperhub/DA to start chatting

Pi new

How to use Dasuperhub/DA with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Dasuperhub/DA: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": "Dasuperhub/DA:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Dasuperhub/DA with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf Dasuperhub/DA: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 Dasuperhub/DA:Q4_K_M

Run Hermes

hermes

Docker Model Runner
How to use Dasuperhub/DA with Docker Model Runner:
```
docker model run hf.co/Dasuperhub/DA:Q4_K_M
```

Lemonade

How to use Dasuperhub/DA with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Dasuperhub/DA:Q4_K_M

Run and chat with the model

lemonade run user.DA-Q4_K_M

List all available models

lemonade list

Dasuperhub commited on Feb 12

Commit

b4de4f4

verified ·

1 Parent(s): 5daafc4

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+---
+language:
+  - sus
+  - fr
+  - en
+license: apache-2.0
+tags:
+  - soussou
+  - guinea
+  - african-languages
+  - gguf
+  - offline
+  - mobile
+base_model: Qwen/Qwen3-0.6B
+pipeline_tag: text-generation
+---
+# DA — Guinea's AI
+**DA** is a fine-tuned 0.6B language model that speaks **Soussou** (Susu), a West African language spoken by ~3 million people in Guinea, Sierra Leone, and Guinea-Bissau.
+## What is DA?
+DA is the first AI model that natively speaks Soussou. Not through translation APIs — the language lives in the weights.
+```
+User: Qui es-tu?
+DA: N tan Guinius, DA AI guineen. Mu fe di i bere?
+```
+## Model Details
+| Property | Value |
+|----------|-------|
+| Base Model | Qwen3-0.6B |
+| Method | QLoRA (r=16, alpha=16) |
+| Training Data | 17,438 ChatML examples |
+| Languages | Soussou, French, English |
+| Format | GGUF Q4_K_M |
+| Size | 379 MB |
+| Context | 2048 tokens |
+| Final Loss | 0.85-0.94 |
+## Why DA?
+- **Offline**: Runs on $100 phones with no internet
+- **Free**: No API keys, no monthly costs
+- **Native**: Soussou in the weights, not a wrapper
+- **Small**: 379MB GGUF — fits anywhere
+## Usage
+### With llama.cpp
+```bash
+./llama-cli --model DA-v3-Q4_K_M.gguf -p "Translate to Soussou: Good morning"
+```
+### With Ollama
+```bash
+ollama create da -f Modelfile
+ollama run da
+```
+## Training Data
+17,438 examples covering:
+- Soussou-French-English translations (GATITOS + custom pairs)
+- Soussou Bible text
+- Grammar and morphology
+- Conversational Soussou
+- Cultural context
+## Built by
+**DASH** — Diop Abdoul Aziz | Guinea
+*"Be the Best amongst the Bests — With Care and Love"*
+Part of the [Guinius](https://guinius.dasuperhub.com) ecosystem.