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### Evo1 Sampling (`evo1-sample`) Generates DNA sequences by autoregressive sampling. Given one or more prompt sequences, 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. Optionally returns a per-sequence likelihood score (log-likelihood, average log-likelihood, and perplexity) for the generated sequences. #### API Reference

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

Source Evo1 weights variant; `evo-1-8k-*` variants use an 8,192-token context, `evo-1-131k-base` extends to 131,072 tokens, and `-crispr`/`-transposon` are domain fine-tunes. Available options: `evo-1.5-8k-base`, `evo-1-8k-base`, `evo-1-131k-base`, `evo-1-8k-crispr`, `evo-1-8k-transposon` Limit sampling to the top-k most probable tokens at each step. Defaults to `4` (one per DNA base). Maximum number of new tokens to generate per prompt (excludes prompt). Use the KV cache for autoregressive generation. Number of tokens to prefill in parallel before switching to autoregressive prompt forcing; lower values reduce peak memory. 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. Prepend the input prompt to each generated sequence; when `False` (the default), only newly generated tokens are returned. Softmax temperature; lower values are more deterministic. Nucleus sampling threshold over per-position token probabilities. Number of prompts to process simultaneously on GPU.

Source One generated DNA sequence per prompt, with its scores. Scoring metrics for this sequence, including log\_likelihood, avg\_log\_likelihood, and perplexity. The generated DNA sequence.

#### Applications This tool produces candidate DNA sequences for downstream design and screening, including synthetic genes, regulatory regions, CRISPR systems (using the `evo-1-8k-crispr` checkpoint), and transposable elements (using the `evo-1-8k-transposon` checkpoint). The prompt sets the biological context for what follows, for example a start codon or promoter region. #### Usage Tips * **Match the checkpoint to the task.** `evo-1-8k-base` (the default) is the general prokaryotic and phage DNA model and `evo-1-131k-base` is its genome-scale, long-context counterpart. `evo-1-8k-crispr` and `evo-1-8k-transposon` are task-specific variants of `evo-1-8k-base` for generating CRISPR-Cas systems and IS200/IS605 transposons; use them when generating those systems and a base checkpoint otherwise. * **`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 excludes the prompt by default.** `prepend_prompt=False` returns only the newly generated nucleotides, not the prompt joined to its continuation; set it `True` if you need the full sequence back. * **Prompt length plus `max_new_tokens` must fit the checkpoint's context window** (8,192 nucleotides for the 8k checkpoints, 131,072 for `evo-1-131k-base`). The model cannot attend beyond that window, so a long prompt directly reduces how much can be generated. * **Generated sequences are candidates.** Validate them with downstream tools (for example ORF detection, structure prediction, or homology search) before drawing biological conclusions.

### Evo1 Scoring (`evo1-score`) Scores existing DNA sequences under the Evo1 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 and the token vocabulary. #### API Reference

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

Source Evo1 weights variant. Available options: `evo-1.5-8k-base`, `evo-1-8k-base`, `evo-1-131k-base`, `evo-1-8k-crispr`, `evo-1-8k-transposon` 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. 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 from natural prokaryotic and phage genomes. Lower perplexity means the sequence is more consistent with that training distribution. Use it to rank or filter candidate sequences (including the output of `evo1-sample`), to compare variants of a sequence, or to flag sequences that fall far outside the model's training domain. #### 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_name` 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).