repente-v0.5-GGUF

Repente v0.5

A language model that writes Pure Data patches and SuperCollider code, and runs locally. Qwen2.5-Coder-7B-Instruct fine-tuned with QLoRA, exported to GGUF at Q4_K_M.

Use bidubr/repente-v0.7-GGUF instead. This version is published as the frozen reference on which the experiments reported in the paper were run, so that those results stay reproducible. It is not the model to reach for if you just want to generate patches.

Why this repository exists

Version 0.5 was published first, selected by a battery of five prompts sampled once each. A later measurement, resampling the same five prompts 30 times on every checkpoint, showed that the selection was a lucky draw: the result that chose v0.5 had a 1.5% chance of occurring, and v0.7 is measurably stronger.

Version 0.5 remains available because the baseline comparison and the prompting study in the paper were run against these exact weights, and withdrawing them would make those results unverifiable. The full account, including how the wrong model came to be published for four training cycles, is in Sections 7.6 and 11 of the paper.

What it does

Give it a description of a sound and it writes the code that produces it.

make a sine wave at 440 Hz connected to output

#N canvas 0 0 450 300 12;
#X obj 100 100 osc~ 440;
#X obj 100 150 dac~;
#X connect 0 0 1 0;
#X connect 0 0 1 1;

Running it

With Ollama, straight from this repository:

ollama run hf.co/bidubr/repente-v0.5-GGUF:Q4_K_M

With llama.cpp:

llama-server -m repente-v0.5-Q4_K_M.gguf -ngl 99 -c 4096

4.5 GB on disk. Fits in 8 GB of VRAM with room for a 4096-token context.

Prompting

The system prompt used throughout the reported experiments is short:

You are Repente, a musical programming expert.

Format validity improves substantially when three worked examples and a short chain-of-thought instruction are supplied together. Measured on frozen weights, the combination raises format validity from 75% to 100% across four prompt difficulty levels and eliminates output truncation, at no compute cost. Few-shot exemplars supplied alone introduce a context-leak failure mode in which the model continues the turn structure of the examples until the token limit; the reasoning instruction suppresses it. Details in Section 8 of the paper.

Known limitations

ELSE objects are not generated. Across 1,350 measured generations, an object from the ELSE library appears once. Five cycles of corpus weighting, including one that weighted ELSE explicitly, did not change this. Treat library coverage as a retrieval problem at inference time, not something the weights will supply.

Analysis responses are short. The unmodified base model produces analyses averaging 776 tokens; this model produces 151. The compression is a self-distillation artifact of using each version's own output to train the next, and it is documented rather than fixed.

Patch validity is not patch quality. The measurements above score a generation as passing when it carries a canvas header, an output object, and connections. That is necessary for a patch to make sound and not sufficient for it to make the right sound.

Related

• Paper: arXiv:[ARXIV_ID]
• Code, data and measurement protocol: https://repente.net
• pd-repente: a PlugData fork with a prompt bar in the patching window, https://github.com/dobidu/plugdata

Citation

@misc{repente2026,
  author       = {Batista, Carlos Eduardo Coelho Freire},
  title        = {Repente: A Specialized LLM for Musical Programming Languages
                  with SonicUnit Knowledge Architecture},
  year         = {2026},
  eprint       = {[ARXIV_ID]},
  archivePrefix= {arXiv},
  primaryClass = {cs.SD}
}

Licensed Apache 2.0, inherited from the base model.