Instructions to use webbigdata/VoiceCore_gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

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

VoiceCore_gguf / VoiceCore-Q4_K-f16.gguf

dahara1

Upload VoiceCore-Q4_K-f16.gguf

8c3a1f1 verified 11 months ago

download

history blame

2.66 GB

This file is stored with Xet . It is too big to display, but you can still download it.

Xet Pointer Details

( Raw pointer file )

Xet hash:: 555db4b72646da62a01096b9d3016fcd24c4b5f4d2f565893511582bff11749d
Size of remote file:: 2.66 GB
SHA256:: 93a3f262828753f3d1710ecb4014076a2cf1d32cf6ca9935e151d166c6fbe6c9

Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.