Instructions to use unsloth/DeepSeek-V3.1-Terminus-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use unsloth/DeepSeek-V3.1-Terminus-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("unsloth/DeepSeek-V3.1-Terminus-GGUF", dtype="auto")

llama-cpp-python

How to use unsloth/DeepSeek-V3.1-Terminus-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="unsloth/DeepSeek-V3.1-Terminus-GGUF",
	filename="BF16/DeepSeek-V3.1-Terminus-BF16-00001-of-00030.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 unsloth/DeepSeek-V3.1-Terminus-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
llama-cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

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 unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./llama-cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

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 unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
# Run inference directly in the terminal:
./build/bin/llama-cli -hf unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

Use Docker

docker model run hf.co/unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

LM Studio
Jan
Ollama
How to use unsloth/DeepSeek-V3.1-Terminus-GGUF with Ollama:
```
ollama run hf.co/unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
```

Unsloth Studio

How to use unsloth/DeepSeek-V3.1-Terminus-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 unsloth/DeepSeek-V3.1-Terminus-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 unsloth/DeepSeek-V3.1-Terminus-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for unsloth/DeepSeek-V3.1-Terminus-GGUF to start chatting

Docker Model Runner
How to use unsloth/DeepSeek-V3.1-Terminus-GGUF with Docker Model Runner:
```
docker model run hf.co/unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL
```

Lemonade

How to use unsloth/DeepSeek-V3.1-Terminus-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull unsloth/DeepSeek-V3.1-Terminus-GGUF:UD-Q4_K_XL

Run and chat with the model

lemonade run user.DeepSeek-V3.1-Terminus-GGUF-UD-Q4_K_XL

List all available models

lemonade list

Why UD-TQ1_0.gguf is named after Ternary quants when it has nothing to do with Ternary quants?

by Nekotekina - opened Oct 25, 2025

Discussion

Nekotekina

Oct 25, 2025

It's misleading. It should be named IQ1_S after the smallest quants it uses for layers.

danielhanchen

Unsloth AI org Oct 30, 2025

Because there's no other name you can use for a smaller quant to make it appear on our page. Also only 5% of people who utilize the quant will know what a ternary quant is.

Nekotekina

Nov 1, 2025

Sorry if I sounded rude. I respect your work and your explanation totally makes sense, I just was nitpicking after all. Although I personally would make up something like IQ1_XS for consistency.

The reason I created this topic was hope that you achieved "the impossible"—true ternarization without quality loss, but looking at tensor table dispelled my hopes.
Wishing you the best, I close this topic.

Nekotekina changed discussion status to closed Nov 1, 2025

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