Instructions to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF",
	filename="Turkish-LLM-14B-Instruct-F16.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 ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

Use Docker

docker model run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ogulcanaydogan/Turkish-LLM-14B-Instruct-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": "ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

Ollama
How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with Ollama:
```
ollama run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
```

Unsloth Studio

How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF to start chatting

How to use ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-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": "ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

Run Hermes

hermes

Atomic Chat new

OpenClaw new

How to use ogulcanaydogan/Turkish-LLM-14B-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 ogulcanaydogan/Turkish-LLM-14B-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 "ogulcanaydogan/Turkish-LLM-14B-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"

Docker Model Runner
How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with Docker Model Runner:
```
docker model run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M
```

Lemonade

How to use ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Turkish-LLM-14B-Instruct-GGUF-Q4_K_M

List all available models

lemonade list

Turkish-LLM-14B-Instruct-GGUF

GGUF quantized versions of Turkish-LLM-14B-Instruct, a 14.7B parameter Turkish language model fine-tuned from Qwen2.5-14B-Instruct.

Available Quantizations

File	Quant	Size	RAM Needed	Best For
Turkish-LLM-14B-Instruct-F16.gguf	F16	28 GB	32-35 GB	Full precision, A100/H100, 2x 24GB GPU
Turkish-LLM-14B-Instruct-Q8_0.gguf	Q8_0	15 GB	18-20 GB	32GB RAM, RTX 3090/4090
Turkish-LLM-14B-Instruct-Q5_K_M.gguf	Q5_K_M	9.8 GB	13-14 GB	16GB+ RAM, M2/M3 Mac
Turkish-LLM-14B-Instruct-Q4_K_M.gguf	Q4_K_M	8.4 GB	11-12 GB	16GB RAM laptop, M1/M2 Mac

Recommended Quantization

Q4_K_M for most consumer hardware (best size/quality ratio)
Q5_K_M if you have extra RAM and want slightly better quality
Q8_0 for minimal quality loss with enough RAM
F16 for research/evaluation with high-end hardware

Usage

llama.cpp

# Download a quantization
huggingface-cli download ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF Turkish-LLM-14B-Instruct-Q4_K_M.gguf

# Run inference
./llama-cli -m Turkish-LLM-14B-Instruct-Q4_K_M.gguf \
  -p "<|im_start|>system\nSen yardimci bir Turkce yapay zeka asistanisin.<|im_end|>\n<|im_start|>user\nTurkiye'nin baskenti neresidir?<|im_end|>\n<|im_start|>assistant\n" \
  -n 256 --temp 0.7

Ollama

ollama run hf.co/ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF:Q4_K_M

LM Studio

Download any GGUF file and load it directly in LM Studio.

Chat Template

This model uses the ChatML format:

<|im_start|>system
Sen yardimci bir Turkce yapay zeka asistanisin.<|im_end|>
<|im_start|>user
{user_message}<|im_end|>
<|im_start|>assistant
{assistant_response}<|im_end|>

Model Details

Base Model: Turkish-LLM-14B-Instruct (Qwen2.5-14B-Instruct + SFT)
Parameters: 14.7B
Architecture: Qwen2, 48 layers, 5120 hidden size
Context Length: 32,768 tokens
Vocabulary: 152,064 tokens
Quantized with: llama.cpp

Turkish-LLM-14B-Instruct - Original model (safetensors)
Turkish-LLM-7B-Instruct - Lighter 7B variant
Turkish-LLM-14B-Chat - Live demo

Author

Ogulcan Aydogan | GitHub | LinkedIn

Downloads last month: 473

GGUF

Model size

15B params

Architecture

qwen2

Hardware compatibility

4-bit

5-bit

8-bit

16-bit

Model tree for ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF

Base model

Qwen/Qwen2.5-14B

Finetuned

Qwen/Qwen2.5-14B-Instruct

Finetuned

ogulcanaydogan/Turkish-LLM-14B-Instruct

Quantized

(3)

this model

Collection including ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF

Turkish LLM Family

Collection

Open-source Turkish LLM family (1.5B-32B). Models, GGUF quantizations, datasets, and demos. • 8 items • Updated Mar 21

ogulcanaydogan
/

Turkish-LLM-14B-Instruct-GGUF

Turkish-LLM-14B-Instruct-GGUF

Available Quantizations

Recommended Quantization

Usage

llama.cpp

Ollama

LM Studio

Chat Template

Model Details

Related

Author

Model tree for ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF

Collection including ogulcanaydogan/Turkish-LLM-14B-Instruct-GGUF

Turkish LLM Family