Instructions to use N8Programs/Musicroll-50M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- MLX
How to use N8Programs/Musicroll-50M with MLX:
# Make sure mlx-lm is installed # pip install --upgrade mlx-lm # if on a CUDA device, also pip install mlx[cuda] # Generate text with mlx-lm from mlx_lm import load, generate model, tokenizer = load("N8Programs/Musicroll-50M") prompt = "Once upon a time in" text = generate(model, tokenizer, prompt=prompt, verbose=True) - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- LM Studio
- MLX LM
How to use N8Programs/Musicroll-50M with MLX LM:
Generate or start a chat session
# Install MLX LM uv tool install mlx-lm # Generate some text mlx_lm.generate --model "N8Programs/Musicroll-50M" --prompt "Once upon a time"
| library_name: mlx | |
| pipeline_tag: text-generation | |
| tags: | |
| - music | |
| - piano-roll | |
| - causal-lm | |
| - qwen3 | |
| - safetensors | |
| # Musicroll-50M | |
| Musicroll-50M is a small Qwen3-style causal language model over a 259-token | |
| byte-level music vocabulary. It was used as the MusicRoll negative-control model | |
| in the replication artifact for "Many Next-Token Predictors are In-Context | |
| Learners". | |
| The checkpoint is provided as `model.safetensors` with a compact tokenizer: | |
| - vocabulary size: 259 | |
| - hidden size: 512 | |
| - layers: 16 | |
| - attention heads: 4 | |
| - key/value heads: 2 | |
| - context length: 4096 | |
| - BOS/EOS/PAD token ids: 256/257/258 | |
| The bitstring replication harness encodes tasks as piano-roll-like text and | |
| loads this checkpoint through the local MLX model loader. | |
| ## Intended Use | |
| This upload is primarily for reproducing the MusicRoll negative-control runs in | |
| the ICLManyReplication artifact. General music-generation behavior has not been | |
| characterized here. | |