Instructions to use mradermacher/orthorus_v3_125b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/orthorus_v3_125b-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/orthorus_v3_125b-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/orthorus_v3_125b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/orthorus_v3_125b-GGUF",
	filename="orthorus_v3_125b.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 mradermacher/orthorus_v3_125b-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 mradermacher/orthorus_v3_125b-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf mradermacher/orthorus_v3_125b-GGUF:Q2_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/orthorus_v3_125b-GGUF:Q2_K
# Run inference directly in the terminal:
llama cli -hf mradermacher/orthorus_v3_125b-GGUF:Q2_K

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 mradermacher/orthorus_v3_125b-GGUF:Q2_K
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/orthorus_v3_125b-GGUF:Q2_K

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 mradermacher/orthorus_v3_125b-GGUF:Q2_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/orthorus_v3_125b-GGUF:Q2_K

Use Docker

docker model run hf.co/mradermacher/orthorus_v3_125b-GGUF:Q2_K

LM Studio
Jan
Ollama
How to use mradermacher/orthorus_v3_125b-GGUF with Ollama:
```
ollama run hf.co/mradermacher/orthorus_v3_125b-GGUF:Q2_K
```

Unsloth Studio

How to use mradermacher/orthorus_v3_125b-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 mradermacher/orthorus_v3_125b-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 mradermacher/orthorus_v3_125b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for mradermacher/orthorus_v3_125b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/orthorus_v3_125b-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/orthorus_v3_125b-GGUF:Q2_K
```

Lemonade

How to use mradermacher/orthorus_v3_125b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/orthorus_v3_125b-GGUF:Q2_K

Run and chat with the model

lemonade run user.orthorus_v3_125b-GGUF-Q2_K

List all available models

lemonade list

About

static quants of https://huggingface.co/ibivibiv/orthorus_125b_v3

weighted/imatrix quants are available at https://huggingface.co/mradermacher/orthorus_v3_125b-i1-GGUF

Usage

If you are unsure how to use GGUF files, refer to one of TheBloke's READMEs for more details, including on how to concatenate multi-part files.

Provided Quants

(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)

Link	Type	Size/GB	Notes
GGUF	Q2_K	46.0
PART 1 PART 2	IQ3_XS	51.3
PART 1 PART 2	IQ3_S	54.2	beats Q3_K*
PART 1 PART 2	Q3_K_S	54.2
PART 1 PART 2	IQ3_M	55.6
PART 1 PART 2	Q3_K_M	60.2	lower quality
PART 1 PART 2	Q3_K_L	65.3
PART 1 PART 2	IQ4_XS	67.6
PART 1 PART 2	Q4_K_S	71.4	fast, recommended
PART 1 PART 2	Q4_K_M	75.7	fast, recommended
PART 1 PART 2	Q5_K_S	86.3
PART 1 PART 2	Q5_K_M	88.9
PART 1 PART 2 PART 3	Q6_K	102.9	very good quality
PART 1 PART 2 PART 3	Q8_0	133.3	fast, best quality

Here is a handy graph by ikawrakow comparing some lower-quality quant types (lower is better):

And here are Artefact2's thoughts on the matter: https://gist.github.com/Artefact2/b5f810600771265fc1e39442288e8ec9

FAQ / Model Request

See https://huggingface.co/mradermacher/model_requests for some answers to questions you might have and/or if you want some other model quantized.

Thanks

I thank my company, nethype GmbH, for letting me use its servers and providing upgrades to my workstation to enable this work in my free time.

Downloads last month: 3

GGUF

Model size

125B params

Architecture

llama

Hardware compatibility

2-bit

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