Instructions to use appvoid/appvoid-cloud-03-q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use appvoid/appvoid-cloud-03-q8_0-GGUF with Transformers:

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("appvoid/appvoid-cloud-03-q8_0-GGUF", device_map="auto")

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use appvoid/appvoid-cloud-03-q8_0-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 appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
llama cli -hf appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

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 appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./llama-cli -hf appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

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 appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
# Run inference directly in the terminal:
./build/bin/llama-cli -hf appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

Use Docker

docker model run hf.co/appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

LM Studio
Jan
Ollama
How to use appvoid/appvoid-cloud-03-q8_0-GGUF with Ollama:
```
ollama run hf.co/appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
```

Unsloth Studio

How to use appvoid/appvoid-cloud-03-q8_0-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 appvoid/appvoid-cloud-03-q8_0-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 appvoid/appvoid-cloud-03-q8_0-GGUF to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for appvoid/appvoid-cloud-03-q8_0-GGUF to start chatting

Docker Model Runner
How to use appvoid/appvoid-cloud-03-q8_0-GGUF with Docker Model Runner:
```
docker model run hf.co/appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0
```

Lemonade

How to use appvoid/appvoid-cloud-03-q8_0-GGUF with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull appvoid/appvoid-cloud-03-q8_0-GGUF:Q8_0

Run and chat with the model

lemonade run user.appvoid-cloud-03-q8_0-GGUF-Q8_0

List all available models

lemonade list

Atomic Chat

appvoid-cloud-03-q8_0-GGUF / README.md

appvoid

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	---
	base_model: appvoid/cloud-03
	library_name: transformers
	tags:
	- mergekit
	- merge
	- llama-cpp
	- gguf
	---
	# merge

	This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).

	## Merge Details
	### Merge Method

	This model was merged using the [DARE TIES](https://arxiv.org/abs/2311.03099) merge method using [MihaiPopa-1/LFM2.5-350M-heretic](https://huggingface.co/MihaiPopa-1/LFM2.5-350M-heretic) as a base.

	### Models Merged

	The following models were included in the merge:
	* [mkurman/LiquidAI-LFM2.5-350M-SYNTH](https://huggingface.co/mkurman/LiquidAI-LFM2.5-350M-SYNTH)
	* [squ11z1/claude-oss-350m](https://huggingface.co/squ11z1/claude-oss-350m)
	* [appvoid/palmer-005-core](https://huggingface.co/appvoid/palmer-005-core)

	### Configuration

	The following YAML configuration was used to produce this model:

	```yaml
	merge_method: dare_ties
	base_model: MihaiPopa-1/LFM2.5-350M-heretic
	dtype: bfloat16

	parameters:
	normalize: false
	int8_mask: true

	models:
	# slot_1_strongest
	- model: squ11z1/claude-oss-350m
	parameters:
	weight: 0.28
	density: 0.38

	# slot_2_medium
	- model: appvoid/palmer-005-core
	parameters:
	weight: 0.22
	density: 0.34

	# slot_3_light
	- model: mkurman/LiquidAI-LFM2.5-350M-SYNTH
	parameters:
	weight: 0.14
	density: 0.28

	```