Instructions to use appvoid/void.0 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use appvoid/void.0 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/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.0
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama serve -hf appvoid/void.0 # Run inference directly in the terminal: llama cli -hf appvoid/void.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/void.0 # Run inference directly in the terminal: ./llama-cli -hf appvoid/void.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/void.0 # Run inference directly in the terminal: ./build/bin/llama-cli -hf appvoid/void.0
Use Docker
docker model run hf.co/appvoid/void.0
- LM Studio
- Jan
- Ollama
How to use appvoid/void.0 with Ollama:
ollama run hf.co/appvoid/void.0
- Unsloth Desktop
- Docker Model Runner
How to use appvoid/void.0 with Docker Model Runner:
docker model run hf.co/appvoid/void.0
- Lemonade
How to use appvoid/void.0 with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull appvoid/void.0
Run and chat with the model
lemonade run user.void.0-{{QUANT_TAG}}List all available models
lemonade list
- Atomic Chat
Download train_results.json from appvoid/void.0: direct link, hf CLI and curl.
- Browser
- Download file 569 Bytes
-
https://huggingface.co/appvoid/void.0/resolve/main/train_results.json
- Command line
-
hf download hf://appvoid/void.0/train_results.json
-
curl -L -o train_results.json https://huggingface.co/appvoid/void.0/resolve/main/train_results.json
569 Bytes
| { | |
| "effective_batch_size": 32, | |
| "epoch": 2.0, | |
| "final_gradient_accumulation_steps": 1, | |
| "final_micro_batch_size": 32, | |
| "num_input_tokens_seen": 726302720, | |
| "peak_cuda_memory_gib": 213.5380449295044, | |
| "real_tokens_per_epoch": 363149667, | |
| "requested_epochs": 2.0, | |
| "total_flos": 4.372023498453811e+17, | |
| "total_optimizer_steps_planned": 5542, | |
| "train_blocks": 88660, | |
| "train_loss": 2.3499925993530812, | |
| "train_runtime": 10647.232, | |
| "train_samples_per_second": 16.654, | |
| "train_steps_per_second": 0.521, | |
| "warmup_steps": 111 | |
| } |