Instructions to use nvidia/magpie_tts_multilingual_357m with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries
NeMo
How to use nvidia/magpie_tts_multilingual_357m with NeMo:
```
# tag did not correspond to a valid NeMo domain.
```
Notebooks
Google Colab
Kaggle
Local Apps Settings

How to use nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m:F16
# Run inference directly in the terminal:
llama cli -hf nvidia/magpie_tts_multilingual_357m:F16

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf nvidia/magpie_tts_multilingual_357m:F16
# Run inference directly in the terminal:
llama cli -hf nvidia/magpie_tts_multilingual_357m:F16

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 nvidia/magpie_tts_multilingual_357m:F16
# Run inference directly in the terminal:
./llama-cli -hf nvidia/magpie_tts_multilingual_357m:F16

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 nvidia/magpie_tts_multilingual_357m:F16
# Run inference directly in the terminal:
./build/bin/llama-cli -hf nvidia/magpie_tts_multilingual_357m:F16

Use Docker

docker model run hf.co/nvidia/magpie_tts_multilingual_357m:F16

LM Studio
Jan
Ollama
How to use nvidia/magpie_tts_multilingual_357m with Ollama:
```
ollama run hf.co/nvidia/magpie_tts_multilingual_357m:F16
```

Unsloth Studio

How to use nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m 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 nvidia/magpie_tts_multilingual_357m to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for nvidia/magpie_tts_multilingual_357m to start chatting

Docker Model Runner
How to use nvidia/magpie_tts_multilingual_357m with Docker Model Runner:
```
docker model run hf.co/nvidia/magpie_tts_multilingual_357m:F16
```

Lemonade

How to use nvidia/magpie_tts_multilingual_357m with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull nvidia/magpie_tts_multilingual_357m:F16

Run and chat with the model

lemonade run user.magpie_tts_multilingual_357m-F16

List all available models

lemonade list

Atomic Chat

Add NeMo-Speech.cpp GGUF and tokenizer assets

by prabhsimrans-nv - opened 14 days ago

base: refs/heads/main

←

from: refs/pr/6

Discussion Files changed

+37

-2

prabhsimrans-nv

NVIDIA org 14 days ago

add: v2602 F16 GGUF and ready-to-use tokenizer assets for local inference with NeMo-Speech.cpp | update: model card with inference instructions

Add NeMo-Speech.cpp GGUF and tokenizer assets61a0a449

fix: extract Magpie tokenizer assets from NeMo archive6106b17d

prabhsimrans-nv changed pull request status to open 7 days ago

blisc changed pull request status to merged 7 days ago

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