Instructions to use QuantFactory/Mistrilitary-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use QuantFactory/Mistrilitary-7b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("QuantFactory/Mistrilitary-7b-GGUF", dtype="auto")

llama-cpp-python

How to use QuantFactory/Mistrilitary-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="QuantFactory/Mistrilitary-7b-GGUF",
	filename="Mistrilitary-7b.Q2_K.gguf",
)

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

Notebooks
Google Colab
Kaggle
Local Apps

llama.cpp

How to use QuantFactory/Mistrilitary-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 QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistrilitary-7b-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/Mistrilitary-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
llama-cli -hf QuantFactory/Mistrilitary-7b-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/Mistrilitary-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./llama-cli -hf QuantFactory/Mistrilitary-7b-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/Mistrilitary-7b-GGUF:Q4_K_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M

Use Docker

docker model run hf.co/QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M

LM Studio
Jan
Ollama
How to use QuantFactory/Mistrilitary-7b-GGUF with Ollama:
```
ollama run hf.co/QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M
```

Unsloth Studio new

How to use QuantFactory/Mistrilitary-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 QuantFactory/Mistrilitary-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 QuantFactory/Mistrilitary-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 QuantFactory/Mistrilitary-7b-GGUF to start chatting

Docker Model Runner
How to use QuantFactory/Mistrilitary-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M
```

Lemonade

How to use QuantFactory/Mistrilitary-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull QuantFactory/Mistrilitary-7b-GGUF:Q4_K_M

Run and chat with the model

lemonade run user.Mistrilitary-7b-GGUF-Q4_K_M

List all available models

lemonade list

QuantFactory/Mistrilitary-7b-GGUF

This is quantized version of Heralax/Mistrilitary-7b created using llama.cpp

Original Model Card

Was torn between calling it MiLLM and Mistrillitary. Sigh naming is one of the two great problems in computer science...

This is a domain-expert finetune based on the US Army field manuals (the ones that are published and available for civvies like me). It's focused on factual question answer only, but seems to be able to answer slightly deeper questions in a pinch.

Model Quirks

I had to focus on the army field manuals because the armed forces publishes a truly massive amount of text.
No generalist assistant data was included, which means this is very very very focused on QA, and may be inflexible.
Experimental change: data was mostly generated by a smaller model, Mistral NeMo. Quality seems unaffected, costs are much lower. Had problems with the open-ended questions not being in the right format.
Low temperture recommended. Screenshots use 0.
ChatML
No special tokens added.

Examples:

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