Instructions to use appvoid/appvoid-cloud-7-q8_0-GGUF with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use appvoid/appvoid-cloud-7-q8_0-GGUF with Transformers:
# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("appvoid/appvoid-cloud-7-q8_0-GGUF", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use appvoid/appvoid-cloud-7-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-7-q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf appvoid/appvoid-cloud-7-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-7-q8_0-GGUF:Q8_0 # Run inference directly in the terminal: llama cli -hf appvoid/appvoid-cloud-7-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-7-q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./llama-cli -hf appvoid/appvoid-cloud-7-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-7-q8_0-GGUF:Q8_0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/appvoid-cloud-7-q8_0-GGUF:Q8_0
Use Docker
docker model run hf.co/appvoid/appvoid-cloud-7-q8_0-GGUF:Q8_0
- LM Studio
- Jan
- Ollama
How to use appvoid/appvoid-cloud-7-q8_0-GGUF with Ollama:
ollama run hf.co/appvoid/appvoid-cloud-7-q8_0-GGUF:Q8_0
- Unsloth Studio
How to use appvoid/appvoid-cloud-7-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-7-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-7-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-7-q8_0-GGUF to start chatting
- Docker Model Runner
How to use appvoid/appvoid-cloud-7-q8_0-GGUF with Docker Model Runner:
docker model run hf.co/appvoid/appvoid-cloud-7-q8_0-GGUF:Q8_0
- Lemonade
How to use appvoid/appvoid-cloud-7-q8_0-GGUF with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/appvoid-cloud-7-q8_0-GGUF:Q8_0
Run and chat with the model
lemonade run user.appvoid-cloud-7-q8_0-GGUF-Q8_0
List all available models
lemonade list
- Atomic Chat
File size: 1,464 Bytes
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base_model: appvoid/cloud-7
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:
# Main complementary skill: terminal / agentic / tool behavior
- model: appvoid/palmer-005-core
parameters:
weight: 0.28
density: 0.38
# Assistant tone / conversational instruction-following
- model: squ11z1/claude-oss-350m
parameters:
weight: 0.22
density: 0.34
# Generalization / synthetic behavior
- model: mkurman/LiquidAI-LFM2.5-350M-SYNTH
parameters:
weight: 0.14
density: 0.28
```
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