Instructions to use webbigdata/VoiceCore_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- llama-cpp-python
How to use webbigdata/VoiceCore_gguf with llama-cpp-python:
# !pip install llama-cpp-python from llama_cpp import Llama llm = Llama.from_pretrained( repo_id="webbigdata/VoiceCore_gguf", filename="VoiceCore-BF16.gguf", )
llm.create_chat_completion( messages = "No input example has been defined for this model task." )
- Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use webbigdata/VoiceCore_gguf with llama.cpp:
Install from brew
brew install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf webbigdata/VoiceCore_gguf:BF16 # Run inference directly in the terminal: llama-cli -hf webbigdata/VoiceCore_gguf:BF16
Install from WinGet (Windows)
winget install llama.cpp # Start a local OpenAI-compatible server with a web UI: llama-server -hf webbigdata/VoiceCore_gguf:BF16 # Run inference directly in the terminal: llama-cli -hf webbigdata/VoiceCore_gguf:BF16
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 webbigdata/VoiceCore_gguf:BF16 # Run inference directly in the terminal: ./llama-cli -hf webbigdata/VoiceCore_gguf:BF16
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 webbigdata/VoiceCore_gguf:BF16 # Run inference directly in the terminal: ./build/bin/llama-cli -hf webbigdata/VoiceCore_gguf:BF16
Use Docker
docker model run hf.co/webbigdata/VoiceCore_gguf:BF16
- LM Studio
- Jan
- Ollama
How to use webbigdata/VoiceCore_gguf with Ollama:
ollama run hf.co/webbigdata/VoiceCore_gguf:BF16
- Unsloth Studio
How to use webbigdata/VoiceCore_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 webbigdata/VoiceCore_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 webbigdata/VoiceCore_gguf to start chatting
Using HuggingFace Spaces for Unsloth
# No setup required # Open https://huggingface.co/spaces/unsloth/studio in your browser # Search for webbigdata/VoiceCore_gguf to start chatting
- Docker Model Runner
How to use webbigdata/VoiceCore_gguf with Docker Model Runner:
docker model run hf.co/webbigdata/VoiceCore_gguf:BF16
- Lemonade
How to use webbigdata/VoiceCore_gguf with Lemonade:
Pull the model
# Download Lemonade from https://lemonade-server.ai/ lemonade pull webbigdata/VoiceCore_gguf:BF16
Run and chat with the model
lemonade run user.VoiceCore_gguf-BF16
List all available models
lemonade list
Update README.md
Browse files
README.md
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## システム概要
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[llama.cpp サーバー]
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[SNACデコーダー]
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VoiceCoreは直接WAVファイルを生成するのではなく、SNAC用の音声トークンを出力します。
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## セットアップガイド(Mac/初心者向け)
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## システム概要
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```
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[ユーザー入力]
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[llama.cpp サーバー]
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[SNACデコーダー]
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```
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VoiceCoreは直接WAVファイルを生成するのではなく、SNAC用の音声トークンを出力します。
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そのため、コマンドラインよりサーバー形式で動作させる方が柔軟性が高くなります
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## セットアップガイド(Mac/初心者向け)
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