Instructions to use Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF",
	filename="FusionNet_7Bx2_MoE_14B-Q3_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 Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
# Run inference directly in the terminal:
llama-cli -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_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 Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
# Run inference directly in the terminal:
./llama-cli -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_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 Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
# Run inference directly in the terminal:
./build/bin/llama-cli -hf Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S

Use Docker

docker model run hf.co/Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S

LM Studio
Jan
Ollama
How to use Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with Ollama:
```
ollama run hf.co/Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
```

Unsloth Studio

How to use Nan-Do/FusionNet_7Bx2_MoE_14B-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 Nan-Do/FusionNet_7Bx2_MoE_14B-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 Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF to start chatting

Atomic Chat new
Docker Model Runner
How to use Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with Docker Model Runner:
```
docker model run hf.co/Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S
```

Lemonade

How to use Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF:Q4_K_S

Run and chat with the model

lemonade run user.FusionNet_7Bx2_MoE_14B-GGUF-Q4_K_S

List all available models

lemonade list

FusionNet_7Bx2_MoE_14B

Original model: FusionNet_7Bx2_MoE_14B

Description

This repo contains GGUF format model files for FusionNet_7Bx2_MoE_14B.

Provided files

Name	Quantisation method	Bits	Size
FusionNet_7Bx2_MoE_14B-Q3_K_S.gguf	Q3_KS	3	5.59 GB
FusionNet_7Bx2_MoE_14B-Q3_K.gguf	Q3_K	3	6.21 GB
FusionNet_7Bx2_MoE_14B-Q4_0.gguf	Q4_0	4	7.28 GB
FusionNet_7Bx2_MoE_14B-Q4_K_S.gguf	Q4_K_S	4	7.34 GB
FusionNet_7Bx2_MoE_14B-Q4_K.gguf	Q4_K	4	7.78 GB
FusionNet_7Bx2_MoE_14B-Q4_1.gguf	Q4_1	4	8.08 GB
FusionNet_7Bx2_MoE_14B-Q5_K_S.gguf	Q5_KS	5	8.87 GB
FusionNet_7Bx2_MoE_14B-Q5_K.gguf	Q5_K	5	9.13 GB
FusionNet_7Bx2_MoE_14B-Q6_K.gguf	Q6_K	6	10.6 GB
FusionNet_7Bx2_MoE_14B-Q8_0.gguf	Q8_0	8	13.7 GB

Downloads last month: 223

GGUF

Model size

13B params

Architecture

llama

Hardware compatibility

3-bit

4-bit

5-bit

6-bit

8-bit

16-bit

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Model tree for Nan-Do/FusionNet_7Bx2_MoE_14B-GGUF

Base model

TomGrc/FusionNet_7Bx2_MoE_14B

Quantized

(6)

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