Instructions to use RJuro/munin-neuralbeagle-7b-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use RJuro/munin-neuralbeagle-7b-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="RJuro/munin-neuralbeagle-7b-GGUF",
	filename="ggml-model-Q6_K.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use RJuro/munin-neuralbeagle-7b-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 RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf RJuro/munin-neuralbeagle-7b-GGUF:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
# Run inference directly in the terminal:
llama cli -hf RJuro/munin-neuralbeagle-7b-GGUF:Q6_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 RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf RJuro/munin-neuralbeagle-7b-GGUF:Q6_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 RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf RJuro/munin-neuralbeagle-7b-GGUF:Q6_K

Use Docker

docker model run hf.co/RJuro/munin-neuralbeagle-7b-GGUF:Q6_K

LM Studio
Jan
Ollama
How to use RJuro/munin-neuralbeagle-7b-GGUF with Ollama:
```
ollama run hf.co/RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
```

Unsloth Studio

How to use RJuro/munin-neuralbeagle-7b-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 RJuro/munin-neuralbeagle-7b-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 RJuro/munin-neuralbeagle-7b-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for RJuro/munin-neuralbeagle-7b-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use RJuro/munin-neuralbeagle-7b-GGUF with Docker Model Runner:
```
docker model run hf.co/RJuro/munin-neuralbeagle-7b-GGUF:Q6_K
```

Lemonade

How to use RJuro/munin-neuralbeagle-7b-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull RJuro/munin-neuralbeagle-7b-GGUF:Q6_K

Run and chat with the model

lemonade run user.munin-neuralbeagle-7b-GGUF-Q6_K

List all available models

lemonade list

munin-neuralbeagle-7b

This is a merge of pre-trained language models created using mergekit.

Merge Details

Merge Method

This model was merged using the DARE TIES merge method using danish-foundation-models/munin-7b-alpha as a base.

Models Merged

The following models were included in the merge:

mlabonne/NeuralBeagle14-7B

Configuration

The following YAML configuration was used to produce this model:

models:
  - model: danish-foundation-models/munin-7b-alpha
    # No parameters necessary for base model
  - model: mlabonne/NeuralBeagle14-7B
    parameters:
      density: 0.53
      weight: 0.6
merge_method: dare_ties
base_model: danish-foundation-models/munin-7b-alpha
parameters:
  int8_mask: true
dtype: bfloat16