Instructions to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF", dtype="auto", device_map="auto")

llama-cpp-python

How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF",
	filename="Mistral-Nemo-Prism-12B-v7.Q2_K.gguf",
)

llm.create_chat_completion(
	messages = "No input example has been defined for this model task."
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with llama.cpp:

Install (macOS, Linux)

curl -LsSf https://llama.app/install.sh | sh
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama cli -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-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 QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-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 QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
```

Unsloth Studio

How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-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 QuantFactory/Mistral-Nemo-Prism-12B-v7-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 QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistral-Nemo-Prism-12B-v7-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF

This is quantized version of nbeerbower/Mistral-Nemo-Prism-12B-v7 created using llama.cpp

Original Model Card

🧪 Just Another Model Experiment

This is one of many experimental iterations I'm sharing publicly while I mess around with training parameters and ideas. It's not a "real" release - just me being transparent about my learning process. Feel free to look under the hood, but don't expect anything production-ready!

Mistral-Nemo-Prism-12B-v7

Mahou-1.5-mistral-nemo-12B-lorablated finetuned on Arkhaios-DPO and Purpura-DPO.

The goal was to reduce archaic language and purple prose in a completely uncensored model.

Method

ORPO tuned with 8x A40 for 10 epochs.

For this version, beta was increased to 2.

In conclusion, LoRA does not seem to be able to completely remove some of the language issues deeply embedded in the model.

Downloads last month: 154

GGUF

Model size

12B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Inference Providers NEW

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Model tree for QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF

Base model

nbeerbower/Mahou-1.5-mistral-nemo-12B-lorablated

Quantized

(5)

this model

QuantFactory
/

Mistral-Nemo-Prism-12B-v7-GGUF

QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF

Original Model Card

Mistral-Nemo-Prism-12B-v7

Method

Model tree for QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF

Datasets used to train QuantFactory/Mistral-Nemo-Prism-12B-v7-GGUF