Instructions to use DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M

Use Docker

docker model run hf.co/DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use DarkStrox/Agentic-Arabic-2.6-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "DarkStrox/Agentic-Arabic-2.6-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": "DarkStrox/Agentic-Arabic-2.6-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M

Ollama
How to use DarkStrox/Agentic-Arabic-2.6-GGUF with Ollama:
```
ollama run hf.co/DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
```

Unsloth Studio

How to use DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for DarkStrox/Agentic-Arabic-2.6-GGUF to start chatting

How to use DarkStrox/Agentic-Arabic-2.6-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DarkStrox/Agentic-Arabic-2.6-GGUF: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": "DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use DarkStrox/Agentic-Arabic-2.6-GGUF with Docker Model Runner:
```
docker model run hf.co/DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M
```

Lemonade

How to use DarkStrox/Agentic-Arabic-2.6-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Agentic-Arabic-2.6-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-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 DarkStrox/Agentic-Arabic-2.6-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use DarkStrox/Agentic-Arabic-2.6-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf DarkStrox/Agentic-Arabic-2.6-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 "DarkStrox/Agentic-Arabic-2.6-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"

🇸🇦 Agentic Arabic 2.6 (GGUF Q4_K_M)

Agentic Arabic 2.6 GGUF is a state-of-the-art 2.6B parameter model fine-tuned specifically for high-precision Arabic Function Calling, Tool Use, and General Instruction Following, built on top of Liquid AI's LFM 2.5 architecture using Unsloth.

👉 Interactive Playground & Demo: https://huggingface.co/spaces/DarkStrox/Agentic-Arabic-2.6-Demo

📈 Benchmark Performance

1. 🛠️ Arabic Function Calling & Tool Use

Evaluated live on unseen Arabic tool-invocation prompts against foundation baselines:

Model	Architecture / Size	Quantization	Function Calling Accuracy	VRAM Footprint
🟣 Agentic Arabic 2.6 (Q6_K)	Liquid LFM (2.6B)	Q6_K	95.0% 🟢	2.08 GB
🟣 Agentic Arabic 2.6 (Q4_K_M)	Liquid LFM (2.6B)	Q4_K_M	85.0% 🟡	1.67 GB
🩶 LFM 2.6 Base	Liquid LFM (2.6B)	Q5_K_M	24.0% 🔴	1.94 GB
⬛ Gemma 4 E4B IT	Transformer (7.4B)	Q4_K_XL	10.0% 🔴	4.21 GB

2. ⚡ General Arabic QA & Inference Speed

Evaluated across standard Arabic general instruction tasks (Math, Geography, Concept Explanation, Poetry, Reasoning):

Model	General QA Accuracy	Avg Response Latency	Speed Multiplier
🟣 Agentic Arabic 2.6 (Q4_K_M)	100% (5/5) 🟢	1,493 ms ⚡	1.0x (Baseline)
🟣 Agentic Arabic 2.6 (Q6_K)	100% (5/5) 🟢	9,570 ms	6.4x slower
🩶 LFM 2.6 Base	100% (5/5)	6,593 ms	4.4x slower
⬛ Gemma 4 E4B IT	100% (5/5)	13,662 ms	9.1x slower

💡 Quantization Insight: Q4_K_M delivers ultra-fast response speeds (~1.49s) and 100% general instruction accuracy in a ultra-lightweight 1.67 GB memory footprint. For strict JSON-schema function calling precision, Q6_K is recommended.

🚀 Quickstart Usage

1. Using `llama-cpp-python`

from huggingface_hub import hf_hub_download
from llama_cpp import Llama

# Download model GGUF from Hugging Face
model_path = hf_hub_download(
    repo_id="DarkStrox/Agentic-Arabic-2.6-GGUF",
    filename="LFM2.5-2.6B.Q4_K_M.gguf"
)

llm = Llama(
    model_path=model_path,
    n_ctx=4096,
    n_gpu_layers=-1
)

system_prompt = """You are a function calling AI model. You are provided with function signatures within <tools> </tools> XML tags.
<tools>
[{"name": "get_weather", "description": "Get current weather", "parameters": {"type": "object", "properties": {"city": {"type": "string"}}, "required": ["city"]}}]
</tools>"""

user_prompt = "ما هي حالة الطقس اليوم في عمان؟"

prompt = f"<|im_start|>system\n{system_prompt}<|im_end|>\n<|im_start|>user\n{user_prompt}<|im_end|>\n<|im_start|>assistant\n"

response = llm(prompt, max_tokens=512, stop=["<|im_end|>"])
print(response['choices'][0]['text'])

2. Using `llama-server` CLI

llama-server.exe -m LFM2.5-2.6B.Q4_K_M.gguf --ngl 99 --port 8080 --ctx-size 4096

📜 Model Details

Developed by: Custom Arabic Fine-Tuning Pipeline (Unsloth)
Base Architecture: Liquid AI LFM 2.5
Format: GGUF (Q4_K_M)
File Size: 1.67 GB
Live Space Demo: DarkStrox/Agentic-Arabic-2.6-Demo

Downloads last month: 57

GGUF

Model size

3B params

Architecture

lfm2

Hardware compatibility

4-bit

Model tree for DarkStrox/Agentic-Arabic-2.6-GGUF

Base model

LiquidAI/LFM2.5-2.6B-Base

Finetuned

LiquidAI/LFM2.5-2.6B

Quantized

(58)

this model

DarkStrox
/

Agentic-Arabic-2.6-GGUF

🇸🇦 Agentic Arabic 2.6 (GGUF Q4_K_M)

📈 Benchmark Performance

1. 🛠️ Arabic Function Calling & Tool Use

2. ⚡ General Arabic QA & Inference Speed

🚀 Quickstart Usage

1. Using `llama-cpp-python`

2. Using `llama-server` CLI

📜 Model Details

Model tree for DarkStrox/Agentic-Arabic-2.6-GGUF

Space using DarkStrox/Agentic-Arabic-2.6-GGUF 1

🇸🇦 Agentic Arabic 2.6 (GGUF Q4_K_M)

📈 Benchmark Performance

1. 🛠️ Arabic Function Calling & Tool Use

2. ⚡ General Arabic QA & Inference Speed

🚀 Quickstart Usage

1. Using llama-cpp-python

2. Using llama-server CLI

📜 Model Details

Model tree for DarkStrox/Agentic-Arabic-2.6-GGUF

Space using DarkStrox/Agentic-Arabic-2.6-GGUF 1

1. Using `llama-cpp-python`

2. Using `llama-server` CLI