Instructions to use mradermacher/Meidebenne-120b-v1.0-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/Meidebenne-120b-v1.0-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/Meidebenne-120b-v1.0-i1-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use mradermacher/Meidebenne-120b-v1.0-i1-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/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
# Run inference directly in the terminal:
llama cli -hf mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

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/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

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/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

Use Docker

docker model run hf.co/mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

LM Studio
Jan
Ollama
How to use mradermacher/Meidebenne-120b-v1.0-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
```
Unsloth Desktop
Docker Model Runner
How to use mradermacher/Meidebenne-120b-v1.0-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S
```

Lemonade

How to use mradermacher/Meidebenne-120b-v1.0-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/Meidebenne-120b-v1.0-i1-GGUF:IQ1_S

Run and chat with the model

lemonade run user.Meidebenne-120b-v1.0-i1-GGUF-IQ1_S

List all available models

lemonade list

Atomic Chat

About

weighted/imatrix quants of https://huggingface.co/MatrixC7/Meidebenne-120b-v1.0

static quants are available at https://huggingface.co/mradermacher/Meidebenne-120b-v1.0-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	i1-IQ1_S	25.7	for the desperate
GGUF	i1-IQ2_XXS	32.2
GGUF	i1-IQ2_XS	35.8
GGUF	i1-IQ2_S	37.6
GGUF	i1-IQ2_M	40.9
GGUF	i1-Q2_K	44.6	IQ3_XXS probably better
GGUF	i1-IQ3_XXS	46.6	lower quality
GGUF	i1-IQ3_XS	49.4
PART 1 PART 2	i1-Q3_K_S	52.2	IQ3_XS probably better
PART 1 PART 2	i1-IQ3_S	52.4	beats Q3_K*
PART 1 PART 2	i1-IQ3_M	54.2
PART 1 PART 2	i1-Q3_K_M	58.2	IQ3_S probably better
PART 1 PART 2	i1-Q3_K_L	63.4	IQ3_M probably better
PART 1 PART 2	i1-IQ4_XS	64.6
PART 1 PART 2	i1-Q4_K_S	68.7	optimal size/speed/quality
PART 1 PART 2	i1-Q4_K_M	72.6	fast, recommended
PART 1 PART 2	i1-Q5_K_S	83.2
PART 1 PART 2	i1-Q5_K_M	85.4
PART 1 PART 2 PART 3	i1-Q6_K	99.1	practically like static Q6_K

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: 73

GGUF

Model size

120B params

Architecture

llama

Hardware compatibility

1-bit

2-bit

3-bit

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Model tree for mradermacher/Meidebenne-120b-v1.0-i1-GGUF

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MatrixC7/Meidebenne-120b-v1.0

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