Instructions to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF with Ollama:
```
ollama run hf.co/mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
```
Unsloth Desktop

How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF: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": "mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Docker Model Runner
How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Turkish-Qwen-2.5-7B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Hermes Agent

How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat

OpenClaw

How to use mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-GGUF with OpenClaw:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama serve -hf mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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 "mehmettozlu/Turkish-Qwen-2.5-7B-Instruct-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"

mehmettozlu commited on 22 days ago

Commit

efe9211

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1 Parent(s): 8b3d770

Update README.md

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@@ -30,6 +30,19 @@ Currently, the most optimal and highly requested quantization version is availab
 ---
 ## 💬 Prompt Template (ChatML)
 The Qwen 2.5 architecture uses the **ChatML** format. To prevent hallucinations and get the best performance, you must use the following structure when interacting with the model via API or code:

 ---
+## 📊 Model Performance Benchmarks (LLM-as-a-Judge)
+This model has been tested under identical conditions alongside other popular Turkish GGUF models and evaluated via an LLM-as-a-Judge benchmark to measure Turkish language proficiency, instruction-following capabilities, and coding performance.
+### 🏆 Comparison Table
+| Model | Parameters | Geography (10) | Email Formatting (10) | Coding (10) | Total | Performance Summary |
+|---|---|---|---|---|---|---|
+| **Qwen-2.5-Instruct** | 7B | 4.0 | **8.5** | **10.0** | **22.5 / 30** | **Most Balanced:** Flawless Python code, fluent Turkish, and high instruction adherence. Minor hallucination tendencies on local geographical data. |
+| **Llama-3.1-Instruct** | 8B | 0.5 | 0.0 | 5.0 | **5.5 / 30** | **Partial Success:** Strong algorithmic background (generates working code), but suffers from severe token repetition and looping on text tasks. |
+| **Mistral-NeMo-Instruct** | 12B | 2.0 | 2.0 | 1.0 | **5.0 / 30** | **Weak Instruction Following:** While grammar is readable, it lacks task orientation (generates a list instead of code, fails to formalize casual tone). |
+| **Gemma-2-IT** | 9B | 0.0 | 0.0 | 0.0 | **0.0 / 30** | **Format Incompatibility:** Due to special token structures and quantization sensitivity, it fails to produce meaningful output and enters a repetition loop. |
 ## 💬 Prompt Template (ChatML)
 The Qwen 2.5 architecture uses the **ChatML** format. To prevent hallucinations and get the best performance, you must use the following structure when interacting with the model via API or code: