Instructions to use helizac/Novus-7b-tr_v1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use helizac/Novus-7b-tr_v1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("helizac/Novus-7b-tr_v1-GGUF", dtype="auto")

llama-cpp-python

How to use helizac/Novus-7b-tr_v1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="helizac/Novus-7b-tr_v1-GGUF",
	filename="Novus-7b-tr_v1.F32.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf helizac/Novus-7b-tr_v1-GGUF:Q4_K_M

Use Docker

docker model run hf.co/helizac/Novus-7b-tr_v1-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use helizac/Novus-7b-tr_v1-GGUF with Ollama:
```
ollama run hf.co/helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
```

Unsloth Studio

How to use helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_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 helizac/Novus-7b-tr_v1-GGUF to start chatting

Docker Model Runner
How to use helizac/Novus-7b-tr_v1-GGUF with Docker Model Runner:
```
docker model run hf.co/helizac/Novus-7b-tr_v1-GGUF:Q4_K_M
```

Lemonade

How to use helizac/Novus-7b-tr_v1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull helizac/Novus-7b-tr_v1-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Novus-7b-tr_v1-GGUF-Q4_K_M

List all available models

lemonade list

Novus-7b-tr_v1 - GGUF

Model creator: mlabonne
Original model: Daredevil-7B
Model Fine-Tuner: Novus Research
Fine-tuned model: Novus-7b-tr_v1

Description

This repo contains GGUF format model files for mlabonne's Daredevil-7B model, fine-tuned to create Novus-7b-tr_v1 by Novus Research.

About GGUF

GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp.

Here is an incomplete list of clients and libraries that are known to support GGUF:

llama.cpp. The source project for GGUF. Offers a CLI and a server option.
text-generation-webui, the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
KoboldCpp, a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for storytelling.
GPT4All, a free and open-source local running GUI, supporting Windows, Linux, and macOS with full GPU accel.
LM Studio, an easy-to-use and powerful local GUI for Windows and macOS (Silicon), with GPU acceleration. Linux available, in beta as of 27/11/2023.
LoLLMS Web UI, a great web UI with many interesting and unique features, including a full model library for easy model selection.
Faraday.dev, an attractive and easy-to-use character-based chat GUI for Windows and macOS (both Silicon and Intel), with GPU acceleration.
llama-cpp-python, a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
candle, a Rust ML framework with a focus on performance, including GPU support, and ease of use.
ctransformers, a Python library with GPU accel, LangChain support, and OpenAI-compatible AI server. Note, as of time of writing (November 27th 2023), ctransformers has not been updated in a long time and does not support many recent models.

Novus Research

Novus Research is committed to pushing the boundaries in natural language processing by collaborating with the open-source community through innovative research. This commitment is coupled with our focus on empowering businesses with tailored, on-site AI and large language model solutions.

Compatibility

These quantized GGUF files are compatible with candle from Hugging Face.

Provided Files

Name	Bit	Quant Method	Size	Use case
Novus-7b-tr_v1.Q2_K.gguf	2	Q2_K	2.72G	Smallest size, lowest precision
Novus-7b-tr_v1.Q3_K.gguf	3	Q3_K	3.16G	Very low precision
Novus-7b-tr_v1.Q3_K_S.gguf	3	Q3_K_S	3.52G	Low precision, level 0
Novus-7b-tr_v1.Q3_K_M.gguf	3	Q3_K_M	3.82G	Slightly better than Q4_0
Novus-7b-tr_v1.Q3_K_L.gguf	3	Q3_K_L	3.47G	Kernel optimized, low precision
Novus-7b-tr_v1.Q4_0.gguf	4	Q4_0	4.11G	Moderate precision, level 0
Novus-7b-tr_v1.Q4_K_M.gguf	4	Q4_K_M	4.37G	Better than Q5_0
Novus-7b-tr_v1.Q5_0.gguf	5	Q5_0	5.00G	Kernel optimized, moderate precision
Novus-7b-tr_v1.Q5_K_S.gguf	5	Q5_K_S	5.00G	Higher precision than Q5_K
Novus-7b-tr_v1.Q5_K_M.gguf	5	Q5_K_M	5.13G	Higher precision, level 0
Novus-7b-tr_v1.Q6_K.gguf	6	Q6_K	5.94G	Highest precision, level 1
Novus-7b-tr_v1.Q8_0.gguf	8	Q8_0	7.77G	Kernel optimized, high precision
Novus-7b-tr_v1.F32.gguf	32	F32	29.00G	Single-precision floating point

How to Download

To download the models, you can use the huggingface-cli command or the equivalent Python code with hf_hub_download.

Using `huggingface-cli` command:

huggingface-cli download helizac/Novus-7b-tr_v1-GGUF <model_file>

For example, to download the Q2_K model:

huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q2_K.gguf

Downloading all models:

huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q2_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q3_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q4_0.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q4_1.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q4_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q5_0.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q5_1.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q5_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q6_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q8_0.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q8_1.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_Q8_K.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_F16.gguf
huggingface-cli download helizac/Novus-7b-tr_v1-GGUF Novus-7b-tr_v1_F32.gguf

Using Python:

from huggingface_hub import hf_hub_download

hf_hub_download("helizac/Novus-7b-tr_v1-GGUF", "<model_file>")

To download all models, you can run:

model_files = [
    "Novus-7b-tr_v1_Q2_K.gguf",
    "Novus-7b-tr_v1_Q3_K.gguf",
    "Novus-7b-tr_v1_Q4_0.gguf",
    "Novus-7b-tr_v1_Q4_1.gguf",
    "Novus-7b-tr_v1_Q4_K.gguf",
    "Novus-7b-tr_v1_Q5_0.gguf",
    "Novus-7b-tr_v1_Q5_1.gguf",
    "Novus-7b-tr_v1_Q5_K.gguf",
    "Novus-7b-tr_v1_Q6_K.gguf",
    "Novus-7b-tr_v1_Q8_0.gguf",
    "Novus-7b-tr_v1_Q8_1.gguf",
    "Novus-7b-tr_v1_Q8_K.gguf",
    "Novus-7b-tr_v1_F32.gguf"
]

for model_file in model_files:
    hf_hub_download("helizac/Novus-7b-tr_v1-GGUF", model_file)

You can also specify a folder to download the file(s) to:

hf_hub_download("helizac/Novus-7b-tr_v1-GGUF", "<model_file>", local_dir="<output_directory>")

Usage

!pip install llama-cpp-python

from llama_cpp import Llama

# Download the model from Hugging Face (replace URL with the actual one)
model_url = "https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q8_0.gguf"
model_path = "Novus-7b-tr_v1.gguf"  # Local filename

# Function to download the model (optional)
def download_model(url, filename):
  import urllib.request
  if not os.path.isfile(filename):
    urllib.request.urlretrieve(url, filename)
    print(f"Downloaded model: {filename}")

download_model(model_url, model_path)

# Load the model
llm = Llama(model_path=model_path)

prompt = "Büyük dil modelleri nelerdir?"

# Adjust these parameters for different outputs
max_tokens = 256
temperature = 0.7

output = llm(prompt, max_tokens=max_tokens, temperature=temperature)
output_text = output["choices"][0]["text"].strip()

print(output_text)

Acknowledgements

This model is built on top of the efforts from the NovusResearch and mlabonne teams, and we appreciate their contribution to the AI community.

GGUF model card:

{Furkan Erdi}

Downloads last month: 98

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

2-bit

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4-bit

5-bit

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8-bit

32-bit

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Model tree for helizac/Novus-7b-tr_v1-GGUF

Base model

mlabonne/Daredevil-7B

Quantized

(8)

this model