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Daredevil-7B
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metadata
base_model: mlabonne/Daredevil-7B
language:
  - tr
license: cc-by-nc-4.0
model_creator: mlabonne
model_name: Novus-7b-tr_v1
model_type: transformer
quantized_by: Furkan Erdi
tags:
  - GGUF
  - Transformers
  - Novus-7b-tr_v1
  - Daredevil-7B
library_name: transformers
architecture: transformer
inference: false

Novus-7b-tr_v1 - GGUF

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}