Novus-7b-tr_v1-GGUF

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

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}