Daniel OS LFM2 pipeline

The website currently uses a verified profile index for factual questions and LFM2-350M for free-form local generation. The files in this directory create a genuinely personalized checkpoint before it is exported for WebGPU.

Train and evaluate

uv run --python 3.12 \
  --with 'torch>=2.6' \
  --with 'transformers>=4.55' \
  --with 'trl>=0.24' \
  --with 'peft>=0.17' \
  --with 'datasets>=3.0' \
  scripts/train_daniel_lfm2.py

uv run --python 3.12 \
  --with 'torch>=2.6' \
  --with 'transformers>=4.55' \
  scripts/evaluate_daniel_lfm2.py artifacts/daniel-lfm2-350m/merged

If training finishes but merging is interrupted, resume only that step with:

python3 scripts/merge_daniel_lfm2.py

The training script follows Liquid AI’s recommended LFM LoRA rank and scaling (rank 16, alpha 32). It targets every linear layer because LFM2 is a hybrid attention/convolution architecture and this narrow identity task must adapt both paths. It writes both the adapter and a merged Transformers checkpoint. Generated artifacts are intentionally excluded from Git.

Export for the browser without using local memory

The export script pins Liquid AI’s official LiquidONNX revision, creates a WebGPU-compatible symmetric Q4 graph, runs a CPU smoke test, and uploads the complete Transformers.js model directory. Run it on Hugging Face Jobs so the local machine only submits and monitors the task:

hf jobs uv run \
  --flavor cpu-upgrade \
  --timeout 2h \
  --secrets HF_TOKEN \
  --detach \
  scripts/export_daniel_lfm2_onnx.py --upload-hf

When Hugging Face CLI credentials are unavailable, dispatch .github/workflows/export-daniel-lfm2.yml. It performs the same export on a GitHub-hosted runner and publishes the flattened model files under the daniel-lfm2-onnx-v1 release tag for the browser worker.

After the job succeeds, update MODEL_ID and MODEL_REVISION in assets/js/lfm-worker.js and verify both WebGPU loading and generation in a Chromium browser.

The deterministic profile index must remain in front of generation. A small model checkpoint is useful for tone and narrow profile synthesis, but it is not a substitute for grounding when exact dates, metrics, and links matter.