THRML is a JAX library for building and sampling probabilistic graphical models, with a focus on efficient block Gibbs sampling and energy-based models. Extropic is developing hardware to make sampling from certain classes of discrete PGMs massively more energy efficient; THRML provides GPU‑accelerated tools for block sampling on sparse, heterogeneous graphs, making it a natural place to prototype today and experiment with future Extropic hardware.

Features include:

• Blocked Gibbs sampling for PGMs
• Arbitrary PyTree node states
• Support for heterogeneous graphical models
• Discrete EBM utilities
• Enables early experimentation with future Extropic hardware

From a technical point of view, the internal structure compiles factor-based interactions to a compact "global" state representation, minimizing Python loops and maximizing array-level parallelism in JAX.

Requires Python 3.10+.

pip install thrml

or

uv pip install thrml

Available at docs.thrml.ai.

If you use THRML in your research, please cite us!

@misc{jelinčič2025efficientprobabilistichardwarearchitecture,
      title={An efficient probabilistic hardware architecture for diffusion-like models}, 
      author={Andraž Jelinčič and Owen Lockwood and Akhil Garlapati and Guillaume Verdon and Trevor McCourt},
      year={2025},
      eprint={2510.23972},
      archivePrefix={arXiv},
      primaryClass={cs.LG},
      url={https://arxiv.org/abs/2510.23972}, 
}

Sampling a small Ising chain with two-color block Gibbs:

import jax
import jax.numpy as jnp
from thrml import SpinNode, Block, SamplingSchedule, sample_states
from thrml.models import IsingEBM, IsingSamplingProgram, hinton_init

nodes = [SpinNode() for _ in range(5)]
edges = [(nodes[i], nodes[i+1]) for i in range(4)]
biases = jnp.zeros((5,))
weights = jnp.ones((4,)) * 0.5
beta = jnp.array(1.0)
model = IsingEBM(nodes, edges, biases, weights, beta)

free_blocks = [Block(nodes[::2]), Block(nodes[1::2])]
program = IsingSamplingProgram(model, free_blocks, clamped_blocks=[])

key = jax.random.key(0)
k_init, k_samp = jax.random.split(key, 2)
init_state = hinton_init(k_init, model, free_blocks, ())
schedule = SamplingSchedule(n_warmup=100, n_samples=1000, steps_per_sample=2)

samples = sample_states(k_samp, program, schedule, init_state, [], [Block(nodes)])

For a more extensive example, see the dtm-replication repository which runs on THRML.