Instructions to use Nuodebot/GenAudit_Mistral_7b_GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nuodebot/GenAudit_Mistral_7b_GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nuodebot/GenAudit_Mistral_7b_GGUF",
	filename="GenAudit_Mistral7b_F16_base.gguf",
)

llm.create_chat_completion(
	messages = "\"I like you. I love you\""
)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use Nuodebot/GenAudit_Mistral_7b_GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
# Run inference directly in the terminal:
llama-cli -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
# Run inference directly in the terminal:
llama-cli -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

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 Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
# Run inference directly in the terminal:
./llama-cli -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

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 Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

Use Docker

docker model run hf.co/Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

LM Studio
Jan
Ollama
How to use Nuodebot/GenAudit_Mistral_7b_GGUF with Ollama:
```
ollama run hf.co/Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
```

Unsloth Studio new

How to use Nuodebot/GenAudit_Mistral_7b_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 Nuodebot/GenAudit_Mistral_7b_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 Nuodebot/GenAudit_Mistral_7b_GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nuodebot/GenAudit_Mistral_7b_GGUF to start chatting

Docker Model Runner
How to use Nuodebot/GenAudit_Mistral_7b_GGUF with Docker Model Runner:
```
docker model run hf.co/Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE
```

Lemonade

How to use Nuodebot/GenAudit_Mistral_7b_GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nuodebot/GenAudit_Mistral_7b_GGUF:F16_BASE

Run and chat with the model

lemonade run user.GenAudit_Mistral_7b_GGUF-F16_BASE

List all available models

lemonade list

Model Details

The 7B Mistral Model from GenAudit served in Q4_K_S and F16 GGUF format.
Merged and Quantised with Unsloth.AI

Model Description

Inspired by this paper: https://genaudit.org/
Original Code here: https://github.com/kukrishna/genaudit

Converted to GGUF format for running it on Ollama/Llama.cpp so as to take advantage of VRAM offloading to RAM (something Huggingface transformers is unable to do for now).

Merged base mistral_v0.1_instruct with Qlora and quantised to Q4_k_s gguf format
You may find the base 16 bit model here (but further quantisation is advisable as their Qlora module was fine tuned on the 4bit nf4 base llm)

Developed by: Nuode Chen
Finetuned from model: Mistral_V0.1_instruct

Model Sources

https://github.com/kukrishna/genaudit

Uses

For evaluating the abstractive summaries of LLM given a source article.
This tool will be able to extract evidences supporting each sentence in the summary as well as provide edits to correct its factuality (if applicable)

Refer to original paper for more in-depth information.

Downloads last month: 14

GGUF

Model size

7B params

Architecture

llama

Hardware compatibility

4-bit

16-bit