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"
- Atomic Chat
Add Musicroll-50M model card
Browse files
README.md
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---
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library_name: mlx
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pipeline_tag: text-generation
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tags:
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- music
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- piano-roll
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- causal-lm
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- qwen3
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- safetensors
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---
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# Musicroll-50M
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Musicroll-50M is a small Qwen3-style causal language model over a 259-token
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byte-level music vocabulary. It was used as the MusicRoll negative-control model
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in the replication artifact for "Many Next-Token Predictors are In-Context
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Learners".
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The checkpoint is provided as `model.safetensors` with a compact tokenizer:
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- vocabulary size: 259
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- hidden size: 512
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- layers: 16
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- attention heads: 4
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- key/value heads: 2
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- context length: 4096
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- BOS/EOS/PAD token ids: 256/257/258
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The bitstring replication harness encodes tasks as piano-roll-like text and
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loads this checkpoint through the local MLX model loader.
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## Intended Use
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This upload is primarily for reproducing the MusicRoll negative-control runs in
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the ICLManyReplication artifact. General music-generation behavior has not been
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characterized here.
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