Instructions to use N-Bot-Int/MistThena7B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use N-Bot-Int/MistThena7B-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("N-Bot-Int/MistThena7B-GGUF", dtype="auto")

llama-cpp-python

How to use N-Bot-Int/MistThena7B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="N-Bot-Int/MistThena7B-GGUF",
	filename="unsloth.F16.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 N-Bot-Int/MistThena7B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf N-Bot-Int/MistThena7B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf N-Bot-Int/MistThena7B-GGUF:F16
# Run inference directly in the terminal:
llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16

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 N-Bot-Int/MistThena7B-GGUF:F16
# Run inference directly in the terminal:
./llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16

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 N-Bot-Int/MistThena7B-GGUF:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf N-Bot-Int/MistThena7B-GGUF:F16

Use Docker

docker model run hf.co/N-Bot-Int/MistThena7B-GGUF:F16

LM Studio
Jan
Ollama
How to use N-Bot-Int/MistThena7B-GGUF with Ollama:
```
ollama run hf.co/N-Bot-Int/MistThena7B-GGUF:F16
```

Unsloth Studio new

How to use N-Bot-Int/MistThena7B-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 N-Bot-Int/MistThena7B-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 N-Bot-Int/MistThena7B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for N-Bot-Int/MistThena7B-GGUF to start chatting

Docker Model Runner
How to use N-Bot-Int/MistThena7B-GGUF with Docker Model Runner:
```
docker model run hf.co/N-Bot-Int/MistThena7B-GGUF:F16
```

Lemonade

How to use N-Bot-Int/MistThena7B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull N-Bot-Int/MistThena7B-GGUF:F16

Run and chat with the model

lemonade run user.MistThena7B-GGUF-F16

List all available models

lemonade list

A newer version of this model is available: N-Bot-Int/MistThena7BV2-GGUF

Support Us Through

[https://ko-fi.com/nexusnetworkint](Official Ko-FI link!)

GGUF Version

GGUF with Quants! Allowing you to run models using KoboldCPP and other AI Environments!

Quantizations:

Quant Type	Benefits	Cons
Q4_K_M	✅ Smallest size (fastest inference)	❌ Lowest accuracy compared to other quants
	✅ Requires the least VRAM/RAM	❌ May struggle with complex reasoning
	✅ Ideal for edge devices & low-resource setups	❌ Can produce slightly degraded text quality
Q5_K_M	✅ Better accuracy than Q4, while still compact	❌ Slightly larger model size than Q4
	✅ Good balance between speed and precision	❌ Needs a bit more VRAM than Q4
	✅ Works well on mid-range GPUs	❌ Still not as accurate as higher-bit models
Q8_0	✅ Highest accuracy (closest to full model)	❌ Requires significantly more VRAM/RAM
	✅ Best for complex reasoning & detailed outputs	❌ Slower inference compared to Q4 & Q5
	✅ Suitable for high-end GPUs & serious workloads	❌ Larger file size (takes more storage)