v1.0 to v1.1

---

name: waypoint-bio

description: Use when working with Outpost Bio's open microbiome foundation models - the Waypoint checkpoints (Waypoint-6m, Waypoint-45m, Waypoint-170m), the Atlas pretraining corpus, the Compass eight-task benchmark, or the `waypoint` CLI from the `waypoint-bio` package. Covers embedding microbiome samples, fine-tuning on taxonomic abundance data, benchmarking a checkpoint on Compass, pretraining a GPT-2 model on taxonomic abundance profiles, and converting MetaPhlAn, Kraken2, QIIME 2, or MGnify abundance tables into waypoint format.

license: MIT

compatibility: Requires Python 3.10+ with `waypoint-bio` (pulls torch, transformers, datasets, peft, scikit-learn). Needs network access and a Hugging Face token with access granted to the gated outpost-bio repos. A GPU is strongly recommended for pretraining and benchmarking.

metadata:

skill-author: K-Dense Inc.

upstream-version: "waypoint-bio 1.0.2 (PyPI); GitHub main 1.0.4"

last-reviewed: "2026-08-17"

openclaw:

primaryEnv: HF_TOKEN

envVars:

- name: HF_TOKEN

required: true

description: Hugging Face read token with access to the gated outpost-bio/Waypoint-*, outpost-bio/Atlas, and outpost-bio/Compass repos.

---

# 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](https://www.biorxiv.org/content/10.64898/2026.05.02.722381v2):

| Artefact | What it is | Hugging Face |

| --- | --- | --- |

| **Waypoint** | GPT-2-style causal LMs over taxonomic tokens, 6M–170M params | `outpost-bio/Waypoint-6m`, `-45m`, `-170m` |

| **Atlas** | 539,308 microbiome samples scraped from MGnify (485,377 pretrain / 53,931 benchmark) | `outpost-bio/Atlas` |

| **Compass** | Eight downstream tasks over four studies | `outpost-bio/Compass` |

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](#scientific-caveats). A random forest on relative abundances is the better tool

there, and the paper says so.

## Setup

```bash

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](https://huggingface.co/outpost-bio/Waypoint-6m),

[Waypoint-45m](https://huggingface.co/outpost-bio/Waypoint-45m),

[Waypoint-170m](https://huggingface.co/outpost-bio/Waypoint-170m),

[Atlas](https://huggingface.co/datasets/outpost-bio/Atlas),

[Compass](https://huggingface.co/datasets/outpost-bio/Compass).

2. Authenticate locally:

```bash

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.

| Column | Type | Notes |

| --- | --- | --- |

| `Taxa` | `list[str]` | Full lineage strings, `;`-separated: `k__Bacteria; p__Firmicutes; ...; g__Lactobacillus` |

| `Relative Abundances` | `list[float]` | Same length as `Taxa`, same order |

| *(any)* | scalar | Targets, covariates, or a `Split` column |

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:

```bash

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:

```bash

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.

```bash

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

```bash

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

```bash

# 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

```bash

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

```bash

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](https://github.com/Outpost-Bio/waypoint) ·

package `waypoint-bio` ·

paper [bioRxiv 2026.05.02.722381](https://www.biorxiv.org/content/10.64898/2026.05.02.722381v2) ·

community [Waypoint Slack](https://join.slack.com/t/outpostbio-waypoint/shared_invite/zt-3w6ivgtba-WJOCkdxiISxQpwVq9ZZxTA) ·

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](https://www.outpost.bio/citations).