Instructions to use Cyb3RQ/Almohanek1.0-7B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Cyb3RQ/Almohanek1.0-7B with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Cyb3RQ/Almohanek1.0-7B",
	filename="Almohanek1.0-7B-Q4_K_M.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use Cyb3RQ/Almohanek1.0-7B 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 Cyb3RQ/Almohanek1.0-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cyb3RQ/Almohanek1.0-7B:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf Cyb3RQ/Almohanek1.0-7B:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf Cyb3RQ/Almohanek1.0-7B: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 Cyb3RQ/Almohanek1.0-7B:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf Cyb3RQ/Almohanek1.0-7B: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 Cyb3RQ/Almohanek1.0-7B:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Cyb3RQ/Almohanek1.0-7B:Q4_K_M

Use Docker

docker model run hf.co/Cyb3RQ/Almohanek1.0-7B:Q4_K_M

LM Studio
Jan

vLLM

How to use Cyb3RQ/Almohanek1.0-7B with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "Cyb3RQ/Almohanek1.0-7B"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "Cyb3RQ/Almohanek1.0-7B",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/Cyb3RQ/Almohanek1.0-7B:Q4_K_M

Ollama
How to use Cyb3RQ/Almohanek1.0-7B with Ollama:
```
ollama run hf.co/Cyb3RQ/Almohanek1.0-7B:Q4_K_M
```

Unsloth Studio

How to use Cyb3RQ/Almohanek1.0-7B 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 Cyb3RQ/Almohanek1.0-7B 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 Cyb3RQ/Almohanek1.0-7B to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Cyb3RQ/Almohanek1.0-7B to start chatting

How to use Cyb3RQ/Almohanek1.0-7B with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Cyb3RQ/Almohanek1.0-7B: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": "Cyb3RQ/Almohanek1.0-7B:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use Cyb3RQ/Almohanek1.0-7B with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf Cyb3RQ/Almohanek1.0-7B: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 Cyb3RQ/Almohanek1.0-7B:Q4_K_M

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use Cyb3RQ/Almohanek1.0-7B with Docker Model Runner:
```
docker model run hf.co/Cyb3RQ/Almohanek1.0-7B:Q4_K_M
```

Lemonade

How to use Cyb3RQ/Almohanek1.0-7B with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Cyb3RQ/Almohanek1.0-7B:Q4_K_M

Run and chat with the model

lemonade run user.Almohanek1.0-7B-Q4_K_M

List all available models

lemonade list

🪶 المُحنّك · Almohanek 1.0 — 7B

نموذجٌ عربيٌّ يُفكِّر بالعربية في العَروض قبل أن يَنظِم الشِّعر

The Arabic poet that reasons in Arabic — out loud — before it writes a line.

by CyberQ · Apache-2.0 · runs locally · 100+ tok/s on a single GPU

✨ لماذا «المحنّك»؟ / Why Almohanek?

معظم النماذج تكتب شعراً عربياً ثم تُفكّر بالإنجليزية (إن فكّرت). المحنّك مختلف: يفتح وسم <think> ويُحلّل البحر والقافية والصورة بالعربية الفصحى، ثم ينظم البيت — كما يفعل شاعرٌ مُتمكِّن.

Most "Arabic" models think in English behind the scenes. Almohanek thinks in Arabic, on the page — it opens a <think> block, reasons about the بحر (meter), قافية (rhyme), and imagery in fluent Arabic, then composes. You see the craft, not just the output.


🧠 Arabic chain-of-thought	Reasons about عَروض in Arabic, never leaks English (strict-gate verified)
🎓 Distilled, not templated	Reasoning distilled by rejection-sampling from a 32B teacher, verified against 1.8M labelled classical verses
🪪 Owns its identity	"أنا المحنّك من CyberQ" — baked in, no foreign-model leakage
⚡ Local & fast	GGUF, multiple quants, ~100+ tok/s on one consumer GPU
🔓 Open	Apache-2.0, built on Qwen2.5-7B-Instruct

🚀 Quickstart (LM Studio / llama.cpp)

File	Size	Pick this if…
`Almohanek1.0-7B-Q6_K.gguf`	~6 GB	you want the sharpest output ⭐
`Almohanek1.0-7B-Q4_K_M.gguf`	~4.5 GB	you're tight on VRAM

System prompt (copy as-is):

أنت المحنّك من CyberQ، شاعر عربي خبير بالعروض. قبل أي قصيدة فكّر بإيجاز
بالعربية داخل وسم <think> ... </think> (البحر، القافية، الصورة)، ثم اكتب الشعر.
لا تذكر هويتك إلا إذا سُئلت "من أنت".

Sampling: temperature 0.6 · top_p 0.9 · top_k 20 · repeat_penalty 1.15

Try it:

اكتب قصيدة من ٦ أبيات على بحر الكامل في الحنين
ما بحر هذا البيت: قِفا نَبكِ مِن ذِكرى حَبيبٍ وَمَنزِلِ

🛠️ How it was built

Qwen2.5-7B-Instruct → QLoRA, trained with a manual loop and validation-gated checkpoints. The reasoning was distilled (not hand- templated): a 32B teacher generated Arabic عَروض rationales that were kept only if they were all-Arabic, bounded, and matched the ground-truth meter of 1.8M labelled verses. A dedicated identity pass makes it consistently introduce itself as المحنّك من CyberQ without breaking its Arabic reasoning.

🧭 Honest status & roadmap

Almohanek 1.0 is an experimental v1 — and we say so plainly.

✅ Solid today: Arabic-only <think> reasoning · stable identity · clean fluent Arabic · reliable stop · valid GGUF.

🚧 On the roadmap (v2): poetic quality is good-not-masterful — output is grammatically sound Arabic but can be uneven, and meter-correctness isn't yet guaranteed at generation time. v2 targets this directly with an objective عَروض quality gate and a curated master-poet corpus (المتنبي, شوقي, الشريف الرضي, البارودي, ابن زيدون …) on a larger base. This is a 7B specialist, not a general assistant.

We ship honestly: real strengths up front, limits named, roadmap public.

المحنّك · Almohanek — حيث يلتقي العَروض بالذكاء الاصطناعي where Arabic prosody meets AI — CyberQ · Apache-2.0

Downloads last month: 158

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

6-bit

Model tree for Cyb3RQ/Almohanek1.0-7B

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Adapter

(2250)

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