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

Libraries

How to use wolfram/miquliz-120b-GGUF with Transformers:

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

llama-cpp-python

How to use wolfram/miquliz-120b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="wolfram/miquliz-120b-GGUF",
	filename="miquliz-120b.IQ3_XXS.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 wolfram/miquliz-120b-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wolfram/miquliz-120b-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf wolfram/miquliz-120b-GGUF:IQ3_XXS

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf wolfram/miquliz-120b-GGUF:IQ3_XXS
# Run inference directly in the terminal:
llama-cli -hf wolfram/miquliz-120b-GGUF:IQ3_XXS

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 wolfram/miquliz-120b-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./llama-cli -hf wolfram/miquliz-120b-GGUF:IQ3_XXS

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 wolfram/miquliz-120b-GGUF:IQ3_XXS
# Run inference directly in the terminal:
./build/bin/llama-cli -hf wolfram/miquliz-120b-GGUF:IQ3_XXS

Use Docker

docker model run hf.co/wolfram/miquliz-120b-GGUF:IQ3_XXS

LM Studio
Jan
Ollama
How to use wolfram/miquliz-120b-GGUF with Ollama:
```
ollama run hf.co/wolfram/miquliz-120b-GGUF:IQ3_XXS
```

Unsloth Studio new

How to use wolfram/miquliz-120b-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 wolfram/miquliz-120b-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 wolfram/miquliz-120b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for wolfram/miquliz-120b-GGUF to start chatting

Docker Model Runner
How to use wolfram/miquliz-120b-GGUF with Docker Model Runner:
```
docker model run hf.co/wolfram/miquliz-120b-GGUF:IQ3_XXS
```

Lemonade

How to use wolfram/miquliz-120b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull wolfram/miquliz-120b-GGUF:IQ3_XXS

Run and chat with the model

lemonade run user.miquliz-120b-GGUF-IQ3_XXS

List all available models

lemonade list

miquliz-120b-GGUF

EXL2: 2.4bpw | 2.65bpw | 2.9bpw | 4.0bpw
GGUF: IQ3_XXS | Q4_K_S+Q4_K_M
HF: wolfram/miquliz-120b

This is a 120b frankenmerge created by interleaving layers of miqu-1-70b-sf with lzlv_70b_fp16_hf using mergekit.

Inspired by goliath-120b.

Thanks for the support, CopilotKit - the open-source platform for building in-app AI Copilots into any product, with any LLM model. Check out their GitHub.

Thanks for the EXL2 and GGUF quants, Lone Striker and NanoByte!

Prompt template: Mistral

<s>[INST] {prompt} [/INST]

Model Details

Max Context: 32768 tokens
Layers: 137

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Configuration

The following YAML configuration was used to produce this model:

dtype: float16
merge_method: passthrough
slices:
- sources:
  - layer_range: [0, 16]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [8, 24]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [17, 32]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [25, 40]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [33, 48]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [41, 56]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [49, 64]
    model: 152334H/miqu-1-70b-sf
- sources:
  - layer_range: [57, 72]
    model: lizpreciatior/lzlv_70b_fp16_hf
- sources:
  - layer_range: [65, 80]
    model: 152334H/miqu-1-70b-sf

Credits & Special Thanks

1st model:
- original (unreleased) model: mistralai (Mistral AI_)
- leaked model: miqudev/miqu-1-70b
- f16 model: 152334H/miqu-1-70b-sf
2nd model: lizpreciatior/lzlv_70b_fp16_hf
mergekit: arcee-ai/mergekit: Tools for merging pretrained large language models.
mergekit_config.yml: alpindale/goliath-120b
gguf quantization: ggerganov/llama.cpp: Port of Facebook's LLaMA model in C/C++

Support

My Ko-fi page if you'd like to tip me to say thanks or request specific models to be tested or merged with priority. Also consider supporting your favorite model creators, quantizers, or frontend/backend devs if you can afford to do so. They deserve it!

DISCLAIMER: THIS IS BASED ON A LEAKED ASSET AND HAS NO LICENSE ASSOCIATED WITH IT. USE AT YOUR OWN RISK.

Downloads last month: 9

GGUF

Model size

118B params

Architecture

llama

Hardware compatibility

3-bit

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