Waypoint: Outpost Bio's Open Microbiome Foundation Models

Overview

Outpost Bio open-sourced three artefacts under Apache 2.0, described in Treloar et al., bioRxiv 2026.05.02.722381:

The unifying idea: a microbiome sample is a sentence. Each taxon is one token, tokens are ordered by descending abundance z-score, and the model is trained with next-token prediction. A pretrained checkpoint then supplies sample-level embeddings or a fine-tuning backbone for prediction tasks.

All of it is driven by one CLI, waypoint, with five subcommands: prepare-dataset, embed, finetune, benchmark, pretrain.

When to use

• Embedding 16S/shotgun taxonomic profiles into fixed-size vectors for clustering, visualisation, or a downstream classifier.
• Fine-tuning a Waypoint checkpoint to predict a phenotype, treatment, or continuous readout from community composition.
• Scoring your own microbiome model against Compass so the number is comparable to the paper.
• Pretraining a taxonomic language model on Atlas or on your own corpus.
• Converting profiler output (MetaPhlAn, Kraken2/Bracken, QIIME 2, MGnify TSVs) into the input format these tools expect.

Do not reach for this when you have fewer than ~1,000 labelled samples — see Scientific caveats. A random forest on relative abundances is the better tool there, and the paper says so.

Setup

pip install waypoint-bio       # installs the `waypoint` command

Atlas, Compass, and every Waypoint checkpoint are gated. Access is auto-approved, but you must click through once per repo and then authenticate:

1. Request access on each repo page you need: Waypoint-6m, Waypoint-45m, Waypoint-170m, Atlas, Compass.

2. Authenticate locally:

   hf auth login          # or: export HF_TOKEN=hf_...

A 401/403 from any subcommand almost always means access was never requested on that specific repo — a token alone is not enough. Use a read-scoped token. The tokenizer loads via trust_remote_code=True, so pin a revision if you need the remote code fixed across runs.

The waypoint data format

Everything except prepare-dataset consumes waypoint format: a .parquet / .csv / .tsv whose rows are samples, with two aligned list-columns plus any label columns you need.

Prefer parquet. CSV/TSV stores the lists as repr strings and round-trips through ast.literal_eval.

Give full lineages, not bare names. The tokenizer extracts the genus segment (g__) from each lineage and falls back to the most specific higher rank when genus is missing. Bare names disable that fallback entirely.

Workflow

1. Get your data into waypoint format

If you already have a sample × taxa (or taxa × sample) abundance matrix with lineage labels:

waypoint prepare-dataset \
    --input abundance_matrix.tsv \
    --metadata sample_labels.csv \
    --output dataset.parquet

Orientation is auto-detected from the first column header (taxonomy, lineage, taxon, otu, #otu id ⇒ taxa-as-rows); override with --orientation. Rows are normalised to sum to 1 unless you pass --no_normalize, and zeros are dropped unless you pass --keep_zeros.

prepare-dataset cannot read profiler output directly — MetaPhlAn uses | separators, Kraken2 reports encode the hierarchy as indentation, and QIIME 2/SILVA prefixes the domain d__ instead of k__ (which the tokenizer silently ignores). Use the bundled converter for those:

python scripts/profiler_to_waypoint.py \
    --input merged_metaphlan.tsv --format metaphlan \
    --output dataset.parquet

python scripts/profiler_to_waypoint.py \
    --input reports/*.kreport --format kraken \
    --output dataset.parquet

python scripts/profiler_to_waypoint.py \
    --input feature-table.tsv --format qiime2 \
    --output dataset.parquet

See references/data-preparation.md for every input layout, rank handling, and the d__/| gotchas.

2. Check vocabulary coverage before anything else

Waypoint's vocabulary is fixed at pretraining time from Atlas. Taxa absent from it become <unk> and are silently dropped by waypoint embed; the paper names this as the models' main limitation. A sample whose taxa are all out-of-vocabulary yields a degenerate [BOS][EOS] embedding.

python scripts/vocab_coverage.py --model outpost-bio/Waypoint-6m --data dataset.parquet

It reports per-sample and abundance-weighted coverage and flags samples below a threshold. Treat median abundance-weighted coverage under ~0.8 as a reason to re-examine your taxonomy labels before trusting any downstream number.

3. Embed samples

waypoint embed \
    --model outpost-bio/Waypoint-6m \
    --data dataset.parquet \
    --output embeddings.parquet

Output is indexed by sample ID with columns dim_0 … dim_{H-1} (H = 256 for 6m, 512 for 45m, 768 for 170m). Defaults: --pooling last_token, --batch_size 32, --max_length 512, device auto-detected (cuda → mps → cpu).

Keep --pooling last_token unless you have a reason to change it: it matches how the checkpoints were pretrained and how benchmark and finetune pool. mean is a reasonable alternative for unsupervised use; first_token/cls_token return the BOS position and carry little signal in a causal LM.

4. Fine-tune on your labels

# classification
waypoint finetune \
    --model outpost-bio/Waypoint-45m \
    --data dataset.parquet \
    --output_dir outputs/ft_disease \
    --task_type classification \
    --target "Disease Status" \
    --config configs/finetune_classification.yaml

# regression, with a categorical covariate one-hot appended to the pooled embedding
waypoint finetune \
    --model outpost-bio/Waypoint-45m \
    --data dataset.parquet \
    --output_dir outputs/ft_degradation \
    --task_type regression \
    --target "Degradation Rate" \
    --covariate_column Drug \
    --config configs/finetune_regression.yaml

Config paths resolve against the bundled waypoint_bio/configs/ tree, so configs/... works from any directory without cloning.

Defaults worth overriding for small datasets: warmup_steps: 1000 (drop to ~50 so warmup finishes before early stopping), num_epochs: 1 in the shipped configs (raise it — early stopping on validation loss is what actually terminates training), and use_lora: true when VRAM is tight (~1% of parameters trained; adapters are merged back before saving, so the checkpoint stays a plain AutoModel).

Splits default to a random 80/10/10. Set split_column to a Split column whenever samples are correlated — repeated measures, one donor sampled over time, technical replicates — or a random split leaks and the test score is meaningless.

Outputs land in --output_dir: best_model/ (loadable by embed/benchmark), test_metrics.json, training_log.csv + .html, and finetune_results.json.

5. Benchmark on Compass

waypoint benchmark --model outpost-bio/Waypoint-6m --output_dir outputs/benchmark
waypoint benchmark --model outputs/pretrain/best_model --tasks 1 6 --output_dir outputs/smoke

Fine-tunes a fresh head per task and writes benchmark_results.json. Classification tasks score macro-F1; the one regression task scores R² clamped to [0, 1]; final_score is the unweighted mean across tasks. Full task table, metric keys, and result-file schema: references/compass-benchmark.md.

6. Pretrain

waypoint pretrain \
    --model_config configs/models/gpt2-45m.yaml \
    --pretrain_config configs/pretraining.yaml \
    --output_dir outputs/pretrain_45m

Downloads Atlas, builds a taxonomic tokenizer from the corpus, computes per-token abundance mean/std for z-score ordering, then trains with next-token prediction and early stopping. Add --data my_corpus.parquet to pretrain on your own waypoint-format corpus instead, and --max_samples N for a smoke test.

Nine architectures ship, from gpt2-6m.yaml (8 layers, 256 hidden) to gpt2-170m.yaml (24 layers, 768 hidden); per-head dimension is fixed at 64 throughout. references/cli-reference.md has the full table and every config key.

Scientific caveats

These are load-bearing. Ignoring them produces numbers that look fine and mean nothing.

• Below ~1,000 labelled examples, Waypoint underperforms a random forest on raw abundances. The paper's crossover against the RF baseline sits near 10,000 training examples. Fit the baseline first; only adopt the transformer if it wins on your data.
• Out-of-vocabulary taxa are dropped, not flagged. Every Compass dataset carries some. Run scripts/vocab_coverage.py and report the coverage alongside your results.
• 45M, not 170M, was the best benchmark model. Pretraining loss keeps falling with scale, but downstream Compass score does not — start at 6m or 45m and only scale up if it demonstrably helps.
• Genus-level tokenisation is the default, so species-level distinctions are collapsed. Changing taxon_rank requires re-pretraining, not just re-tokenising.
• Compositional data. Relative abundances are constrained to sum to 1; differences in one taxon induce apparent changes in others. This affects interpretation of any per-taxon attribution.
• Batch and study effects dominate microbiome data. Atlas spans MGnify pipelines v1.0–v5.0 and four sequencing modalities. Never let a study or run boundary coincide with your label boundary.
• Not a clinical or diagnostic tool. The model cards state this explicitly.

References

• references/cli-reference.md — every subcommand flag, every config key, the model-size table.
• references/compass-benchmark.md — the eight tasks, filters, metrics, benchmark_results.json schema.
• references/data-preparation.md — waypoint format, profiler conversions, taxonomy string rules.
• references/python-api.md — using the tokenizer, datasets, heads, and checkpoints from Python.

Scripts

• scripts/profiler_to_waypoint.py — MetaPhlAn / Kraken2 / QIIME 2 / generic lineage tables → waypoint format.
• scripts/vocab_coverage.py — tokenizer coverage report for a waypoint-format file.

Upstream

Code github.com/Outpost-Bio/waypoint · package waypoint-bio · paper bioRxiv 2026.05.02.722381 · community Waypoint Slack · contact waypoint@outpost.bio.

Cite Treloar, N. J., Ur-Rehman, S., Yang, J., & Outpost Bio (2026). Learning the Language of the Microbiome with Transformers. bioRxiv. Per-artefact DOIs are listed at outpost.bio/citations.

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Kassis, T., Agarwal, V., He, Y., Patel, D., & Brueckner, A. M. (2026). Scientific Agent Skills: A Library of Procedural Knowledge for Research Agents. arXiv:2609.00065. https://doi.org/10.48550/arXiv.2609.00065

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