| name | evo2-inference |
| description | Install, configure, and use Evo 2 for DNA sequence scoring, embeddings, and generation across local GPU inference, Docker, and Nvidia hosted API or NIM deployments. Use when Codex needs to write, fix, explain, or review Evo 2 Python code, shell commands, notebooks, or deployment steps involving checkpoint selection, hardware compatibility, `Evo2(...)`, tokenization, forward passes, embeddings extraction, generation, or Evo 2 installation and troubleshooting. |
Evo 2 Inference
Overview
Use this skill to produce conservative Evo 2 setup instructions and runnable Python snippets. Prefer the simplest supported path first, and keep hardware assumptions explicit.
Follow This Decision Flow
- Choose the execution path.
- Use local inference when the user has supported Linux or WSL2, CUDA, and NVIDIA GPUs.
- Use Nvidia hosted API when the user wants generation without local installation.
- Use Nvidia NIM when the user wants a self-hosted service endpoint.
- For hosted API work, assume endpoint health can change during a run; prefer retry and fallback plans.
- Choose the checkpoint that matches the hardware.
- Prefer
evo2_7b for the lightest widely usable local path.
- Treat
evo2_20b, evo2_40b, and evo2_1b_base as FP8-dependent models that require Transformer Engine and Hopper GPUs.
- Treat
evo2_40b as a multi-GPU workload that needs multiple H100 GPUs.
- For hosted API forward and embedding extraction, prefer
evo2-7b first.
- For hosted API generation, try
evo2-7b first and fall back to evo2-40b if needed.
- Choose the task mode.
- Use a forward pass when the user wants logits or sequence likelihood style scoring.
- Use embeddings when the user wants downstream representations or classifiers.
- Use generation when the user wants sequence completion or design from prompts.
- For variant-effect requests, use a REF-vs-ALT proxy from forward and embedding deltas.
- State clearly that Evo2 does not expose an AlphaGenome-style
predict_variant(...) endpoint in this skill.
- Validate the environment before going deep.
- Confirm Python 3.11 or 3.12.
- Confirm CUDA 12.1+, cuDNN 9.3+, and a compatible Torch build for local inference.
- Run the packaged generation test after installation or configuration changes.
- Present the smallest working example.
- Return a short runnable snippet first.
- Add Docker, NIM, or hosted API examples only when the user is on that path.
- Route training and finetuning requests to Savanna or BioNemo instead of inventing unsupported local instructions.
Grounded API Surface
Treat the following names and patterns as grounded by the bundled README:
from evo2 import Evo2Evo2("evo2_7b")evo2_model.tokenizer.tokenize(sequence)evo2_model(input_ids)evo2_model(input_ids, return_embeddings=True, layer_names=[...])evo2_model.generate(...)python -m evo2.test.test_evo2_generation --model_name evo2_7b
Treat the following hosted API patterns as grounded by this skill's references and examples:
POST /v1/biology/arc/evo2-7b/forward with {"sequence": ..., "output_layers": [...]}POST /v1/biology/arc/evo2-7b/generate with {"sequence": ..., "num_tokens": ..., "top_k": ...}POST /v1/biology/arc/evo2-40b/generate as generation fallback- Forward outputs can arrive as base64-encoded ZIP payloads containing
.npy arrays.
Verify any additional helper, tokenizer behavior, or advanced inference option against installed packages or official docs before relying on it.
Response Style
- Prefer a hardware-aware recommendation before giving install commands.
- State clearly when a request is incompatible with the user's platform.
- Call out when a model choice implies FP8, Transformer Engine, Hopper GPUs, or multiple GPUs.
- Explicitly label REF-vs-ALT scores as a variant-effect proxy when using Evo2.
- Keep training and finetuning guidance high level unless a grounded source is available.
References
