fair-esm
Evolutionary Scale Modeling (esm): Pretrained language models for proteins. From Facebook AI Research.
Decision gist · record as of 2026-08-14
Yes, with conditions. The models are scientifically rigorous and widely cited, and the MIT license is permissive. However, the repository is archived and unmaintained since February 2024, so there will be no bug fixes or compatibility updates. Install only if you can tolerate a frozen codebase and are willing to manage PyTorch compatibility yourself. For active development or production systems requiring ongoing support, consider whether a maintained fork or alternative is available.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- PyTorch must be installed separately; the package does not declare it as a dependency.
- Pre-trained model weights are downloaded on first use and require sufficient disk space.
- Installation is straightforward with no runtime dependencies.
License · maintenance · safety
MIT (permissive) — MIT license permits commercial and private use with minimal restrictions, making it suitable for research and production applications without licensing friction.
last release 2022-11-01 (1382 days) · last repo commit 2024-02-07 · 4,169 stars · archived
0 known vulnerabilities (OSV.dev, 2026-08-14) · 170,221 downloads/mo, #10,401 on PyPI
Alternatives
Verify before relying
pip install fair-esm
import esm
model, alphabet = esm.pretrained.esm2_t36_3B_UR50D()
# Use model for inference on protein sequences- Exact disk space and memory requirements for different model variants (8M, 35M, 150M, 700M, 3B, 15B parameters)
- Whether PyTorch dependency is intentionally omitted or an oversight in packaging
- Current state of pre-trained weight URLs and download reliability given repository archival
What it is and what it does
Fair-esm is a collection of pre-trained transformer models for protein sequence analysis from Facebook AI Research. It includes ESM-2 (a general-purpose protein language model available in multiple sizes), ESMFold (end-to-end structure prediction), ESM-1v (specialized for variant effect prediction), MSA Transformer (for multiple sequence alignment analysis), and ESM-IF1 (for inverse folding and sequence design). The models are trained on large-scale protein sequence datasets and can generate embeddings, predict 3D structures from sequences, assess the functional impact of mutations, and design new sequences for given protein structures.
The package is designed for researchers and practitioners working with protein analysis. It requires PyTorch (not declared as a dependency) and downloads pre-trained weights on first use. The repository is no longer actively maintained—the last commit was February 2024 and the latest PyPI release is from November 2022—so users should expect no updates, bug fixes, or support for new PyTorch versions.
Use it for
- Generate protein embeddings for downstream machine learning tasks like function prediction or homology detection
- Predict 3D protein structures directly from amino acid sequences without multiple sequence alignments
- Assess how amino acid substitutions affect protein function using zero-shot variant effect prediction
- Design new protein sequences for a given 3D structure using inverse folding
- Extract contact predictions and structural features from protein sequences for comparative analysis
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
The models are scientifically rigorous and widely cited, and the MIT license is permissive. However, the repository is archived and unmaintained since February 2024, so there will be no bug fixes or compatibility updates. Install only if you can tolerate a frozen codebase and are willing to manage PyTorch compatibility yourself. For active development or production systems requiring ongoing support, consider whether a maintained fork or alternative is available.
Install
fair-esm on PyPI
Before you install
Installation is straightforward with no runtime dependencies. However, the repository is archived and unmaintained since February 2024, with the latest PyPI release from November 2022. PyTorch is a prerequisite but not declared as a package dependency.
PyTorch must be installed separately; the package does not declare it as a dependency. Pre-trained model weights are downloaded on first use and require sufficient disk space.
License in practice
MIT license permits commercial and private use with minimal restrictions, making it suitable for research and production applications without licensing friction.
Quickstart
pip install fair-esm
import esm
model, alphabet = esm.pretrained.esm2_t36_3B_UR50D()
# Use model for inference on protein sequencesVerify before relying
- Exact disk space and memory requirements for different model variants (8M, 35M, 150M, 700M, 3B, 15B parameters)
- Whether PyTorch dependency is intentionally omitted or an oversight in packaging
- Current state of pre-trained weight URLs and download reliability given repository archival
Package facts
| License | MIT permissive |
| Python support | Not specified |
| Install friction | Low. Pure-Python wheel |
| Runtime dependencies | None |
| Maintenance | Abandoned 1,382 days since the last release |
| Last repo commit | repository archived |
| First released | |
| Downloads | 170,221 / month, #10,401 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
Evidence: fair_esm-2.0.0-py3-none-any.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “protein language model”
- fair-esmProvides pre-trained transformer protein language models (ESM-2,…
- antibertyAntiBERTy is a transformer language model pre-trained on antibody…
- fhaviaryA gymnasium framework for defining custom reinforcement learning…
Give your agent the search over MCP, or paste the wish link into any chat.
More Artificial Intelligence packages
LiteLLM provides a unified Python interface to call 100+ LLM providers (OpenAI, Anthropic, Gemini, Bedrock, Azure, and others) using OpenAI-compatible API format, available as both a Python SDK and a self-hosted AI Gateway proxy server.
Install it if you need to work with multiple LLM providers or want to centralize LLM routing in your organization.
Client library and CLI tool for downloading, uploading, and managing models, datasets, and repositories on the Hugging Face Hub platform.
Install it if you work with Hugging Face Hub models or datasets.
LangChain provides a framework for building agents and LLM-powered applications by composing language models, tools, and memory through a unified API that abstracts over multiple model providers.
hf-xet provides chunk-based deduplication and efficient file transfer for the Hugging Face Hub, enabling faster uploads and downloads of large files with local disk caching.
Tokenizers converts raw text into token sequences for NLP models, with support for training custom vocabularies and using pre-built tokenizers (BPE, WordPiece) optimized for speed via Rust.
Transformers provides a unified framework for loading, fine-tuning, and running state-of-the-art pretrained models across text, vision, audio, video, and multimodal tasks using PyTorch, JAX, or TensorFlow.
Install it if you need to run or train any transformer-based model for NLP, vision, audio, or multimodal tasks.
See also pytorch-pretrained-bert · adapters · antiberty · sentence-transformers · pydssp · pyhmmer · sklearn2pmml · refgenconf · propka · pinder