# extropic-ai/thrml

Thermodynamic Hypergraphical Model Library in JAX

Repository: https://github.com/extropic-ai/thrml
Canonical: https://ross.abutalabs.com/products/thrml
Homepage: https://docs.thrml.ai/en/latest/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: jax, machine-learning, probabilistic-computing, probabilistic-graphical-models
Last push: 2026-08-04T17:18:17+00:00

## Health v2 (maintenance only)
Score: 71/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 96, release rhythm 64, longevity 25
- inputs: {"age_days": 351, "days_push": 29, "days_rel": 29, "gap_med": 278, "n_releases_24m": 2}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1144, forks 141 (observed 2026-08-28T04:03:45.066332+00:00)

## What it is
THRML is a JAX library for building and sampling probabilistic graphical models, focused on efficient block Gibbs sampling of energy-based models on sparse, heterogeneous graphs. It is developed by Extropic as a software counterpart to their probabilistic sampling hardware, providing GPU-accelerated sampling tools today.

## Use cases
- sample from probabilistic graphical models with block Gibbs in JAX
- build and sample Ising models and discrete energy-based models on GPU
- prototype sampling algorithms for future Extropic probabilistic hardware
- run parallel Gibbs sweeps over graph-coloured blocks of a factor graph
- simulate large spin models for research on diffusion-like probabilistic models

## When to choose
- you need fast, GPU-accelerated block Gibbs sampling of discrete PGMs or EBMs
- your models are sparse, heterogeneous factor graphs expressible in JAX
- you want to experiment with Extropic's probabilistic computing paradigm in software

## When to avoid
- you need general-purpose probabilistic programming with inference beyond Gibbs sampling
- you work with continuous variables or non-JAX ML stacks like PyTorch or TensorFlow
- you need a mature, battle-tested MCMC library with broad model support

## Facets
- artifact type: library
- maturity: active
- function: machine-learning, simulation, gpu-computing
- domain: machine-learning, deep-learning, gpu-computing
- platform: python, cross-platform
- tags: jax, probabilistic-graphical-models, gibbs-sampling, energy-based-models, ising-model, probabilistic-computing, mcmc, algorithms, gpu

## Member repositories
- extropic-ai/thrml (main) score 71

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:45.066332+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:34:44.253549+00:00, confidence not recorded.
  - readme: https://github.com/extropic-ai/thrml (fetched 2026-08-28T04:03:45.066332+00:00, sha 4ca6bf4d8816)
  - homepage: https://docs.thrml.ai/en/latest/ (fetched 2026-08-29T12:39:55.805585+00:00, sha 83bd6670a34f)
  - site_page: https://docs.thrml.ai/en/latest/getting-started.html (fetched 2026-08-29T12:39:55.809286+00:00, sha adee74ed1ae5)
  - registry_pypi: https://pypi.org/pypi/thrml/json (fetched 2026-08-29T12:39:55.811313+00:00, sha 39e3edb34645)
- Data as of 2026-08-30T08:39:29.467469+00:00.