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How to fine-tune a Voice Mem model family adapter with Voice Mem

Train your own adapter on the provided training script and reproduce the default configuration.

Who it is forA machine learning engineer who wants a custom Voice Mem memory-reading adapter for a different dataset or base model.

The problemOff-the-shelf models may not read Voice Mem memory well for a specific domain, so the team needs its own adapter.

The outcomeA trained adapter checkpoint produced with the default settings, matching the configuration of the released checkpoint-3318.

What you need first

  • Python 3.10 or newer
  • An OpenAI API key, used for fact extraction when memories are written
  • Git and access to GitHub and Hugging Face to clone the repo and download the default models
  • Audio input (a WAV file or a microphone) if you want to ingest speech

Steps

  1. Clone the repo and install the training tools with pip install ms-swift==4.5.2 bitsandbytes.
  2. Prepare training data in the format described in finetune/README.md.
  3. Run python finetune/train.py --data data/train.jsonl with default parameters.
  4. Check GPU memory requirements and base model options in finetune/README.md before changing the base model.

Prompt for your agent

In the VoiceMem repo at <repo path>, install ms-swift==4.5.2 and bitsandbytes, then run finetune/train.py with my dataset <dataset path>. Expected output is a trained adapter. Verify the run uses the default parameters described in the README.

Caveat. The README mentions GPU memory requirements without giving numbers here, so check finetune/README.md before starting a run.

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