Instructions to use mradermacher/miquliz-120b-v2.0-i1-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use mradermacher/miquliz-120b-v2.0-i1-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("mradermacher/miquliz-120b-v2.0-i1-GGUF", dtype="auto")

llama-cpp-python

How to use mradermacher/miquliz-120b-v2.0-i1-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="mradermacher/miquliz-120b-v2.0-i1-GGUF",
	filename="miquliz-120b-v2.0.i1-IQ1_M.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/miquliz-120b-v2.0-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/miquliz-120b-v2.0-i1-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M
# Run inference directly in the terminal:
llama cli -hf mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_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 mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M
# Run inference directly in the terminal:
./llama-cli -hf mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_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 mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M
# Run inference directly in the terminal:
./build/bin/llama-cli -hf mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M

Use Docker

docker model run hf.co/mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M

LM Studio
Jan
Ollama
How to use mradermacher/miquliz-120b-v2.0-i1-GGUF with Ollama:
```
ollama run hf.co/mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M
```

Unsloth Studio

How to use mradermacher/miquliz-120b-v2.0-i1-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/miquliz-120b-v2.0-i1-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/miquliz-120b-v2.0-i1-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/miquliz-120b-v2.0-i1-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use mradermacher/miquliz-120b-v2.0-i1-GGUF with Docker Model Runner:
```
docker model run hf.co/mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M
```

Lemonade

How to use mradermacher/miquliz-120b-v2.0-i1-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull mradermacher/miquliz-120b-v2.0-i1-GGUF:IQ1_M

Run and chat with the model

lemonade run user.miquliz-120b-v2.0-i1-GGUF-IQ1_M

List all available models

lemonade list

miquliz-120b-v2.0-i1-GGUF

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---
base_model:
- 152334H/miqu-1-70b-sf
- lizpreciatior/lzlv_70b_fp16_hf
exported_from: wolfram/miquliz-120b-v2.0
language:
- en
library_name: transformers
license: other
quantized_by: mradermacher
tags:
- mergekit
- merge
---
## About

weighted/imatrix quants of https://huggingface.co/wolfram/miquliz-120b-v2.0

<!-- provided-files -->
static quants are available at https://huggingface.co/mradermacher/miquliz-120b-v2.0-GGUF
## Usage

If you are unsure how to use GGUF files, refer to one of [TheBloke's
READMEs](https://huggingface.co/TheBloke/KafkaLM-70B-German-V0.1-GGUF) 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](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ1_S.gguf) | i1-IQ1_S | 25.7 | for the desperate |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_XXS.gguf) | i1-IQ2_XXS | 32.2 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_XS.gguf) | i1-IQ2_XS | 35.8 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_S.gguf) | i1-IQ2_S | 37.6 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ2_M.gguf) | i1-IQ2_M | 40.9 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q2_K.gguf) | i1-Q2_K | 44.6 | IQ3_XXS probably better |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_XXS.gguf) | i1-IQ3_XXS | 47.3 | lower quality |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_XS.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_XS.gguf.split-ab) | i1-Q3_K_XS | 49.3 |  |
| [GGUF](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_XS.gguf) | i1-IQ3_XS | 49.4 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_S.gguf.split-ab) | i1-Q3_K_S | 52.2 | IQ3_XS probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_S.gguf.part2of2) | i1-IQ3_S | 52.4 | beats Q3_K* |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ3_M.gguf.part2of2) | i1-IQ3_M | 54.2 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_M.gguf.split-ab) | i1-Q3_K_M | 58.2 | IQ3_S probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_L.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q3_K_L.gguf.split-ab) | i1-Q3_K_L | 63.4 | IQ3_M probably better |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ4_XS.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-IQ4_XS.gguf.part2of2) | i1-IQ4_XS | 64.6 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_0.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_0.gguf.part2of2) | i1-Q4_0 | 68.1 | fast, low quality |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_S.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_S.gguf.split-ab) | i1-Q4_K_S | 68.7 | optimal size/speed/quality |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_M.gguf.split-aa) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q4_K_M.gguf.split-ab) | i1-Q4_K_M | 72.6 | fast, recommended |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_S.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_S.gguf.part2of2) | i1-Q5_K_S | 83.2 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_M.gguf.part1of2) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q5_K_M.gguf.part2of2) | i1-Q5_K_M | 85.4 |  |
| [PART 1](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q6_K.gguf.part1of3) [PART 2](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q6_K.gguf.part2of3) [PART 3](https://huggingface.co/mradermacher/miquliz-120b-v2.0-i1-GGUF/resolve/main/miquliz-120b-v2.0.i1-Q6_K.gguf.part3of3) | 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):

![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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

## Thanks

I thank my company, [nethype GmbH](https://www.nethype.de/), for letting
me use its servers and providing upgrades to my workstation to enable
this work in my free time.

<!-- end -->