README.md

---

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

- Model creator: [mlabonne](https://huggingface.co/mlabonne)
- Original model: [Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B)

- Model Fine-Tuner: [Novus Research](https://huggingface.co/NovusResearch)
- Fine-tuned model: [Novus-7b-tr_v1](https://huggingface.co/NovusResearch/Novus-7b-tr_v1)

## Description

This repo contains GGUF format model files for [mlabonne's Daredevil-7B](https://huggingface.co/mlabonne/Daredevil-7B) model, fine-tuned to create Novus-7b-tr_v1 by [Novus Research](https://huggingface.co/NovusResearch).

### 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](https://github.com/ggerganov/llama.cpp). The source project for GGUF. Offers a CLI and a server option.
* [text-generation-webui](https://github.com/oobabooga/text-generation-webui), the most widely used web UI, with many features and powerful extensions. Supports GPU acceleration.
* [KoboldCpp](https://github.com/LostRuins/koboldcpp), a fully featured web UI, with GPU accel across all platforms and GPU architectures. Especially good for storytelling.
* [GPT4All](https://gpt4all.io/index.html), a free and open-source local running GUI, supporting Windows, Linux, and macOS with full GPU accel.
* [LM Studio](https://lmstudio.ai/), 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](https://github.com/ParisNeo/lollms-webui), a great web UI with many interesting and unique features, including a full model library for easy model selection.
* [Faraday.dev](https://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](https://github.com/abetlen/llama-cpp-python), a Python library with GPU accel, LangChain support, and OpenAI-compatible API server.
* [candle](https://github.com/huggingface/candle), a Rust ML framework with a focus on performance, including GPU support, and ease of use.
* [ctransformers](https://github.com/marella/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q2_K.gguf) | 2   | Q2_K          | 2.72G | Smallest size, lowest precision |
| [Novus-7b-tr_v1.Q3_K.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q3_K.gguf) | 3   | Q3_K          | 3.16G | Very low precision |
| [Novus-7b-tr_v1.Q3_K_S.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q4_0.gguf) | 4   | Q4_0          | 4.11G | Moderate precision, level 0    |
| [Novus-7b-tr_v1.Q4_K_M.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q5_0.gguf) | 5   | Q5_0          | 5.00G | Kernel optimized, moderate precision |
| [Novus-7b-tr_v1.Q5_K_S.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q6_K.gguf) | 6   | Q6_K          | 5.94G | Highest precision, level 1     |
| [Novus-7b-tr_v1.Q8_0.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/Novus-7b-tr_v1.Q8_0.gguf) | 8   | Q8_0          | 7.77G | Kernel optimized, high precision |
| [Novus-7b-tr_v1.F32.gguf](https://huggingface.co/helizac/Novus-7b-tr_v1-GGUF/blob/main/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:

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

For example, to download the Q2_K model:

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

#### Downloading all models:

```shell
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:

```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:

```python
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:

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

## Usage

```python
!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](https://huggingface.co/NovusResearch) and [mlabonne](https://huggingface.co/mlabonne) teams, and we appreciate their contribution to the AI community.

# GGUF model card:

```
{Furkan Erdi}
```