Instructions to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="KareemBb/Qwen2.5-7B-Instruct-Jordanian",
	filename="qwen2.5-7b-instruct.Q4_K_M-001.gguf",
)

llm.create_chat_completion(
	messages = "{\n    \"question\": \"What is my name?\",\n    \"context\": \"My name is Clara and I live in Berkeley.\"\n}"
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian 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 KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian: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 KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian: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 KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M

Use Docker

docker model run hf.co/KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M

LM Studio
Jan
Ollama
How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with Ollama:
```
ollama run hf.co/KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
```

Unsloth Studio

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian 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 KareemBb/Qwen2.5-7B-Instruct-Jordanian 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 KareemBb/Qwen2.5-7B-Instruct-Jordanian to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for KareemBb/Qwen2.5-7B-Instruct-Jordanian to start chatting

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian: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": "KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian: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 KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf KareemBb/Qwen2.5-7B-Instruct-Jordanian: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 "KareemBb/Qwen2.5-7B-Instruct-Jordanian: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"

Docker Model Runner
How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with Docker Model Runner:
```
docker model run hf.co/KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M
```

Lemonade

How to use KareemBb/Qwen2.5-7B-Instruct-Jordanian with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull KareemBb/Qwen2.5-7B-Instruct-Jordanian:Q4_K_M

Run and chat with the model

lemonade run user.Qwen2.5-7B-Instruct-Jordanian-Q4_K_M

List all available models

lemonade list

Qwen2.5-7B-Instruct-Jordanian

This is a fine-tuned version of Qwen2.5-7B-Instruct, specifically trained to speak in Jordanian dialect. The Model was initially trained to be a part of a Graduation project from a university in Jordan, and this model is the first open source LLM that is trained for the Jordanian Dialect. It was trained using consumer hardware (RTX 4080 16gb VRAM).

Note that this model was made to be used for a RAG Chatbot, so its a question answering model. Its not ready for production and wasn't tested enough.

Training Data

The data used to train the Model consistest of 1500 question and answer paris, created based on actual universtiy rules and regulastion using real jordanian dialect words.

Usage with Ollama

Since this model includes a custom Modelfile, you can set it up in Ollama:

Download the Modelfile and qwen2.5-7b-instruct.Q4_K_M-001.gguf file to the same folder.
Run the following command in your terminal within that folder:

   ollama create jordanian-model -f Modelfile

Downloads last month: 4

GGUF

Model size

8B params

Architecture

qwen2

Hardware compatibility

4-bit

Model tree for KareemBb/Qwen2.5-7B-Instruct-Jordanian

Base model

Qwen/Qwen2.5-7B

Finetuned

Qwen/Qwen2.5-7B-Instruct

Quantized

(373)

this model