Instructions to use byzp/muscriptor-large-gguf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use byzp/muscriptor-large-gguf with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="byzp/muscriptor-large-gguf",
	filename="model_fp16_8k.gguf",
)

output = llm(
	"Once upon a time,",
	max_tokens=512,
	echo=True
)
print(output)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use byzp/muscriptor-large-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 byzp/muscriptor-large-gguf
# Run inference directly in the terminal:
llama cli -hf byzp/muscriptor-large-gguf

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama serve -hf byzp/muscriptor-large-gguf
# Run inference directly in the terminal:
llama cli -hf byzp/muscriptor-large-gguf

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 byzp/muscriptor-large-gguf
# Run inference directly in the terminal:
./llama-cli -hf byzp/muscriptor-large-gguf

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 byzp/muscriptor-large-gguf
# Run inference directly in the terminal:
./build/bin/llama-cli -hf byzp/muscriptor-large-gguf

Use Docker

docker model run hf.co/byzp/muscriptor-large-gguf

LM Studio
Jan
Ollama
How to use byzp/muscriptor-large-gguf with Ollama:
```
ollama run hf.co/byzp/muscriptor-large-gguf
```

Unsloth Studio

How to use byzp/muscriptor-large-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 byzp/muscriptor-large-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 byzp/muscriptor-large-gguf to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for byzp/muscriptor-large-gguf to start chatting

Atomic Chat new
Docker Model Runner
How to use byzp/muscriptor-large-gguf with Docker Model Runner:
```
docker model run hf.co/byzp/muscriptor-large-gguf
```

Lemonade

How to use byzp/muscriptor-large-gguf with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull byzp/muscriptor-large-gguf

Run and chat with the model

lemonade run user.muscriptor-large-gguf-{{QUANT_TAG}}

List all available models

lemonade list

YAML Metadata Warning:empty or missing yaml metadata in repo card

Check out the documentation for more information.

MuScriptor

Audio-to-MIDI transcription using llama.cpp as the transformer backend.

8GB VRAM, 8x fast

Prerequisites

Python with torch, torchaudio
https://github.com/byzp/llama.cpp built with llama-server or download from this repo(linux amd64 cu128)
Model file: model_fp32_8k.gguf conditioning_weights.safetensors

Extract Conditioning Weights

Only needed once, if starting from the full safetensors:

python extract_conditioning_weights.py

This creates conditioning_weights.safetensors (~11 MB) from the full model weights.

This model can also be downloaded from this repo

CLI Transcription

Terminal 1 — start llama-server:

llama-server -m model_fp32_8k.gguf --port 8081 -ngl 99 --parallel 8

Terminal 2 — run transcription:

python muscriptor_cli.py audio.mp3 -o out.mid

Downloads last month: 237

GGUF

Model size

1B params

Architecture

muscriptor

Hardware compatibility

We're not able to determine the quantization variants.

View all variants

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