Instructions to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="QuantFactory/Turkish-Llama-8b-v0.1-GGUF")

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Turkish-Llama-8b-v0.1-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Turkish-Llama-8b-v0.1-GGUF",
	filename="Turkish-Llama-8b-v0.1.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Turkish-Llama-8b-v0.1-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Turkish-Llama-8b-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M

SGLang

How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "QuantFactory/Turkish-Llama-8b-v0.1-GGUF" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Turkish-Llama-8b-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "QuantFactory/Turkish-Llama-8b-v0.1-GGUF" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Turkish-Llama-8b-v0.1-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Ollama
How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-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/Turkish-Llama-8b-v0.1-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Turkish-Llama-8b-v0.1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Turkish-Llama-8b-v0.1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Turkish-Llama-8b-v0.1-GGUF-Q4_K_M

List all available models

lemonade list

Turkish-Llama-8b-v0.1-GGUF

This is quantized version of ytu-ce-cosmos/Turkish-Llama-8b-v0.1 created using llama.cpp

Cosmos LLaMa

This model is a fully fine-tuned version of the LLaMA-3 8B model with a 30GB Turkish dataset.

The Cosmos LLaMa is designed for text generation tasks, providing the ability to continue a given text snippet in a coherent and contextually relevant manner. Due to the diverse nature of the training data, which includes websites, books, and other text sources, this model can exhibit biases. Users should be aware of these biases and use the model responsibly.

Example Usage

Here is an example of how to use the model in colab:

!pip install -U accelerate bitsandbytes

import torch
from transformers import pipeline, AutoTokenizer, AutoModelForCausalLM
from transformers import BitsAndBytesConfig
import time

model_name = "ytu-ce-cosmos/Turkish-Llama-8b-v0.1"

bnb_config = BitsAndBytesConfig(
    load_in_8bit=True,
    bnb_8bit_compute_dtype=torch.bfloat16,
    load_in_8bit_fp32_cpu_offload=True,
    device_map = 'auto'
)

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(
    model_name,
    device_map="auto",
    torch_dtype=torch.bfloat16,
    quantization_config=bnb_config,
)

text_generator = pipeline(
    "text-generation",
    model=model,
    tokenizer=tokenizer,
    device_map="auto",
    temperature=0.3,
    repetition_penalty=1.1,
    top_p=0.9,
    max_length=610,
    do_sample=True,
    return_full_text=False,
    min_new_tokens=32
)

text = """Yapay zeka hakkında 3 tespit yaz.\n"""

r = text_generator(text)

print(r[0]['generated_text'])

"""
1. Yapay Zeka (AI), makinelerin insan benzeri bilişsel işlevleri gerçekleştirmesini sağlayan bir teknoloji alanıdır.

2. Yapay zekanın geliştirilmesi ve uygulanması, sağlık hizmetlerinden eğlenceye kadar çeşitli sektörlerde çok sayıda fırsat sunmaktadır.

3. Yapay zeka teknolojisinin potansiyel faydaları önemli olsa da mahremiyet, işten çıkarma ve etik hususlar gibi konularla ilgili endişeler de var.
"""

Acknowledgments

Thanks to the generous support from the Hugging Face team, it is possible to download models from their S3 storage 🤗
Computing resources used in this work were provided by the National Center for High Performance Computing of Turkey (UHeM) under grant numbers 1016912023 and 1018512024
Research supported with Cloud TPUs from Google's TPU Research Cloud (TRC)

Cosmos Group Contact

COSMOS AI Research Group, Yildiz Technical University Computer Engineering Department
https://cosmos.yildiz.edu.tr/
cosmos@yildiz.edu.tr

license: llama3

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GGUF

Model size

8B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Turkish-Llama-8b-v0.1-GGUF

Base model

meta-llama/Meta-Llama-3-8B

Finetuned

ytu-ce-cosmos/Turkish-Llama-8b-v0.1

Quantized

(6)

this model