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```bibtex theme={null} @ARTICLE{Brixi2026-jn, title = "Genome modelling and design across all domains of life with Evo 2", author = "Brixi, Garyk and Durrant, Matthew G and Ku, Jerome and Naghipourfar, Mohsen and Poli, Michael and Sun, Gwanggyu and Brockman, Greg and Chang, Daniel and Fanton, Alison and Gonzalez, Gabriel A and King, Samuel H and Li, David B and Merchant, Aditi T and Nguyen, Eric and Ricci-Tam, Chiara and Romero, David W and Schmok, Jonathan C and Taghibakhshi, Ali and Vorontsov, Anton and Yang, Brandon and Deng, Myra and Gorton, Liv and Nguyen, Nam and Wang, Nicholas K and Pearce, Michael T and Simon, Elana and Adams, Etowah and Amador, Zachary J and Ashley, Euan A and Baccus, Stephen A and Dai, Haoyu and Dillmann, Steven and Ermon, Stefano and Guo, Daniel and Herschl, Michael H and Ilango, Rajesh and Janik, Ken and Lu, Amy X and Mehta, Reshma and Mofrad, Mohammad R K and Ng, Madelena Y and Pannu, Jaspreet and R{\'e}, Christopher and St John, John and Sullivan, Jeremy and Tey, Joseph and Viggiano, Ben and Zhu, Kevin and Zynda, Greg and Balsam, Daniel and Collison, Patrick and Costa, Anthony B and Hernandez-Boussard, Tina and Ho, Eric and Liu, Ming-Yu and McGrath, Thomas and Powell, Kimberly and Pinglay, Sudarshan and Burke, Dave P and Goodarzi, Hani and Hsu, Patrick D and Hie, Brian L", journal = "Nature", publisher = "Springer Science and Business Media LLC", pages = "1--13", doi = "10.1038/s41586-026-10176-5", month = mar, year = 2026, language = "en" } ```

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### Evo2 Sampling (`evo2-sample`) Generates DNA sequences by autoregressive sampling. Given one or more prompt sequences in Evo2's prompt format, the model extends each prompt nucleotide by nucleotide, drawing each new nucleotide from the model's predicted distribution under the configured `temperature`, `top_k`, and `top_p` settings, until `max_new_tokens` new nucleotides have been produced or an end-of-sequence token is sampled. A key-value cache makes long generations efficient and can be carried forward to continue a generation. #### API Reference

Source Prompt sequences to condition generation on. Can be provided as a single string or a list of strings.

Source Evo2 weights variant. Available options: `evo2_7b`, `evo2_20b`, `evo2_40b`, `evo2_7b_base`, `evo2_40b_base`, `evo2_1b_base`, `evo2_7b_262k`, `evo2_7b_microviridae` Override HuggingFace download with a local weights directory. Limit sampling to the top-k most probable tokens at each step. Maximum number of new tokens to generate per prompt (excludes prompt). Use the model's per-call KV cache during generation. Tokens to prefill in parallel before switching to autoregressive prompt forcing. Maximum sequence length the KV cache will be sized for. Filter EOS/PAD bytes from the detokenized output. Stop generation when an EOS (id=0) token is sampled. Worker-local KV cache handle returned by a previous persistent-worker generation call. Return worker-local KV cache handles for continued generation. Include per-position logits in the output. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the model on. Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. Include the input prompt at the start of each generated sequence; when `False`, only newly generated tokens are returned. Sampling temperature controlling randomness. Nucleus sampling threshold over per-position token probabilities. Number of sequences to process simultaneously.

Source One generated DNA sequence per prompt, with its logits and cache handle. Per-position logits for this sequence (shape: \[num\_generated\_tokens, vocab\_size]). Opaque worker-local cache handle for continued generation, valid only on the worker that produced it. The generated DNA sequence.

#### Applications This tool produces candidate DNA sequences for downstream design and screening, including genes, regulatory regions, and longer multi-gene segments. Because Evo2 is trained across all domains of life, it can be prompted with eukaryotic as well as prokaryotic and phage context, unlike the prokaryote-and-phage-only Evo1. The prompt sets the biological context for what follows. #### Usage Tips * **Match the checkpoint to the task.** `evo2_7b` (the default), `evo2_20b`, and `evo2_40b` are the 1M-context models in increasing size and capability. The `evo2_7b_base`, `evo2_40b_base`, and `evo2_1b_base` checkpoints are 8K-context counterparts (`evo2_1b_base` is the smallest); `evo2_7b_262k` is a 262K-context variant; `evo2_7b_microviridae` is a 7B model adapted on Microviridae genomes for generating that bacteriophage family. * **Prompts use Evo2's prompt format.** Prompt strings follow Evo2's special tokenization (for example a leading `+~` before DNA); see the upstream [Evo2 documentation](https://github.com/arcinstitute/evo2) for the conventions. * **`top_k` defaults to 4, the size of the DNA alphabet.** It exists mainly to keep generation on the four bases rather than other byte tokens, so it is not the diversity parameter; control diversity with `temperature` (lower stays near the training distribution, higher explores it) and leave `top_p` at its default unless you specifically want nucleus sampling. * **Output includes the prompt by default.** `prepend_prompt=True` (the default for this toolkit) returns the prompt joined to its continuation; set it `False` to receive only the newly generated nucleotides. * **Prompt length plus `max_new_tokens` (default 32) must fit the checkpoint's context window.** The model cannot attend beyond that window, so a long prompt directly reduces how much can be generated; pick a longer-context checkpoint when the combined length is large. * **`stop_at_eos` ends generation early** when the model emits an end-of-sequence token; set it to `False` to always produce the full `max_new_tokens`. * **Generated sequences are candidates.** Validate them with downstream tools (for example ORF detection, structure prediction, or homology search) before drawing biological conclusions.

### Evo2 Scoring (`evo2-score`) Scores existing DNA sequences under the Evo2 model. For each sequence, it computes the model's predicted probability of every nucleotide given the preceding nucleotides and aggregates these into a log-likelihood, an average log-likelihood per nucleotide, and a perplexity. Optionally returns the per-position logits, alongside the token vocabulary giving their column order. #### API Reference

Source Sequences to score. Can be provided as a single string or a list of strings.

Source Evo2 weights variant. Available options: `evo2_7b`, `evo2_20b`, `evo2_40b`, `evo2_7b_base`, `evo2_40b_base`, `evo2_1b_base`, `evo2_7b_262k`, `evo2_7b_microviridae` Override HuggingFace download with a local weights directory. Prepend a beginning-of-sequence token before scoring. Verbosity level (0=quiet, 1=info, 2=debug, 3=raw subprocess stderr). `True` is coerced to `1` and `False` to `0`. Device to run the model on. Maximum execution time in seconds. `None` waits indefinitely. Random seed. When set, tools run reproducibly up to small GPU float noise (see `BaseToolOutput.approx_equal`), and the seed participates in cache keys. When None, cacheable seed-sensitive tools skip cache until seeded. Number of sequences to process simultaneously on GPU. Larger batches improve throughput but use more GPU memory; reduce if encountering out-of-memory errors. Include per-position logits in the output.

Source List of scoring outputs, one per input sequence. Each entry is a `Metrics` subclass with scalar metrics (`log_likelihood`, `avg_log_likelihood`, `perplexity`) and optional per-position `_pp`-suffixed list extras; `logits` and `vocab` are declared fields for raw model outputs. Per-position logits array `(seq_len, vocab_size)`. `None` unless `return_logits=True`. Token ordering for `logits`. Name of the metric that best summarizes the result overall (e.g. `"avg_plddt"` for AlphaFold2). Used by downstream UI and reporting to pick a headline value. Concrete Metrics subclass tag; enables typed reconstruction after a serialization round-trip. **Metrics** (one set per `scores` item) | Metric | Type | Range | Availability | | -------------------- | ----- | ----- | ------------ | | `log_likelihood` | float | ≤ 0.0 | always | | `avg_log_likelihood` | float | ≤ 0.0 | always | | `perplexity` | float | ≥ 1.0 | always |

#### Applications This tool measures how well a DNA sequence matches the patterns the model learned during training across all domains of life. Lower perplexity means the sequence is more consistent with that distribution. Use it to rank or filter candidate sequences (including the output of `evo2-sample`), to compare variants of a sequence, or to assess sequences from organisms outside the prokaryotic and phage range that Evo1 covers. #### Usage Tips * **Compare length-normalized scores within one checkpoint.** Total `log_likelihood` scales with sequence length, so use `perplexity` or `avg_log_likelihood` when comparing sequences of different lengths. Different checkpoints learn different distributions that are not calibrated to a common scale, so scores from different `model_checkpoint` values are hard to compare directly; a lower perplexity means the sequence is more consistent with that checkpoint's training distribution. * **`return_logits` defaults to `False`.** Leave it off unless you need the per-position distributions, since the logits tensor is large (sequence length by a 512-token vocabulary). * **`prepend_bos` adds a beginning-of-sequence token** before scoring; leave it `False` unless matching a specific upstream convention.