Instructions to use QuantFactory/Minerva-3B-base-RAG-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Minerva-3B-base-RAG-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Minerva-3B-base-RAG-GGUF",
	filename="Minerva-3B-base-RAG.Q2_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Minerva-3B-base-RAG-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Minerva-3B-base-RAG-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/Minerva-3B-base-RAG-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Minerva-3B-base-RAG-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/Minerva-3B-base-RAG-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M

LM Studio
Jan

vLLM

How to use QuantFactory/Minerva-3B-base-RAG-GGUF with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "QuantFactory/Minerva-3B-base-RAG-GGUF"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "QuantFactory/Minerva-3B-base-RAG-GGUF",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M

Ollama
How to use QuantFactory/Minerva-3B-base-RAG-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Minerva-3B-base-RAG-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/Minerva-3B-base-RAG-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/Minerva-3B-base-RAG-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/Minerva-3B-base-RAG-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Minerva-3B-base-RAG-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Minerva-3B-base-RAG-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Minerva-3B-base-RAG-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Minerva-3B-base-RAG-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Minerva-3B-base-RAG-GGUF

This is quantized version of DeepMount00/Minerva-3B-base-RAG created using llama.cpp

Model Card for Minerva-3B-base-QA-v1.0

Minerva-3B-base-RAG is a specialized question-answering (QA) model derived through the finetuning of Minerva-3B-base-v1.0. This finetuning was independently conducted to enhance the model's performance for QA tasks, making it ideally suited for use in Retrieval-Augmented Generation (RAG) applications.

Overview

Model Type: Fine-tuned Large Language Model (LLM)
Base Model: Minerva-3B-base-v1.0, developed by Sapienza NLP in collaboration with Future Artificial Intelligence Research (FAIR) and CINECA
Specialization: Question-Answering (QA)
Ideal Use Case: Retrieval-Augmented Generation applications

Downloads last month: 652

GGUF

Model size

3B params

Architecture

llama

Hardware compatibility

2-bit

3-bit

4-bit

5-bit

6-bit

8-bit

Model tree for QuantFactory/Minerva-3B-base-RAG-GGUF

Base model

DeepMount00/Minerva-3B-base-RAG

Quantized

(2)

this model

QuantFactory
/

Minerva-3B-base-RAG-GGUF

QuantFactory/Minerva-3B-base-RAG-GGUF

Model Card for Minerva-3B-base-QA-v1.0

Overview

Model tree for QuantFactory/Minerva-3B-base-RAG-GGUF

Dataset used to train QuantFactory/Minerva-3B-base-RAG-GGUF