Instructions to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Turkcell-LLM-7b-v1-GGUF",
	filename="Turkcell-LLM-7b-v1.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Turkcell-LLM-7b-v1-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 QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Turkcell-LLM-7b-v1-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 QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Turkcell-LLM-7b-v1-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 QuantFactory/Turkcell-LLM-7b-v1-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 QuantFactory/Turkcell-LLM-7b-v1-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Turkcell-LLM-7b-v1-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Turkcell-LLM-7b-v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Turkcell-LLM-7b-v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Turkcell-LLM-7b-v1-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Turkcell-LLM-7b-v1-GGUF

This is quantized version of TURKCELL/Turkcell-LLM-7b-v1 created using llama.cpp

Original Model Card

Turkcell-LLM-7b-v1

This model is an extended version of a Mistral-based Large Language Model (LLM) for Turkish. It was trained on a cleaned Turkish raw dataset containing 5 billion tokens. The training process involved using the DORA method initially. Following this, we utilized Turkish instruction sets created from various open-source and internal resources for fine-tuning with the LORA method.

Model Details

Base Model: Mistral 7B based LLM
Tokenizer Extension: Specifically extended for Turkish
Training Dataset: Cleaned Turkish raw data with 5 billion tokens, custom Turkish instruction sets
Training Method: Initially with DORA, followed by fine-tuning with LORA

DORA Configuration

lora_alpha: 128
lora_dropout: 0.05
r: 64
target_modules: "all-linear"

LORA Fine-Tuning Configuration

lora_alpha: 128
lora_dropout: 0.05
r: 256
target_modules: "all-linear"

Usage Examples


from transformers import AutoModelForCausalLM, AutoTokenizer

device = "cuda" # the device to load the model onto

model = AutoModelForCausalLM.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")
tokenizer = AutoTokenizer.from_pretrained("TURKCELL/Turkcell-LLM-7b-v1")

messages = [
    {"role": "user", "content": "Türkiye'nin başkenti neresidir?"},
]

encodeds = tokenizer.apply_chat_template(messages, return_tensors="pt")

eos_token = tokenizer("<|im_end|>",add_special_tokens=False)["input_ids"][0]

model_inputs = encodeds.to(device)
model.to(device)

generated_ids = model.generate(model_inputs, 
                               max_new_tokens=1024, 
                               do_sample=True, 
                               eos_token_id=eos_token)
                               
decoded = tokenizer.batch_decode(generated_ids)
print(decoded[0])

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GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

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4-bit

5-bit

6-bit

8-bit

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