Pre-release

This release is a pre-release and may not be stable for production use.

Haliax

Though you don’t seem to be much for listening, it’s best to be careful. If you managed to catch hold of even just a piece of my name, you’d have all manner of power over me.
— Patrick Rothfuss, The Name of the Wind

Haliax is a JAX library for building neural networks with named tensors, in the tradition of Alexander Rush's Tensor Considered Harmful. Named tensors improve the legibility and compositionality of tensor programs by using named axes instead of positional indices as typically used in NumPy, PyTorch, etc.

Despite the focus on legibility, Haliax is also fast, typically about as fast as "pure" JAX code. Haliax is also built to be scalable: it can support Fully-Sharded Data Parallelism (FSDP) and Tensor Parallelism with just a few lines of code. Haliax powers Levanter, our companion library for training large language models and other foundation models, with scale proven up to 70B parameters and up to TPU v4-2048.

Example: Attention

Here's a minimal attention module implementation in Haliax. For a more detailed introduction, please see the Haliax tutorial. (We use the excellent Equinox library for its module system and tree transformations.)

import equinox as eqx
import jax
import jax.numpy as jnp
import haliax as hax
import haliax.nn as hnn

Pos = hax.Axis("position", 1024)  # sequence length
KPos = Pos.alias("key_position")
Head = hax.Axis("head", 8)  # number of attention heads
Key = hax.Axis("key", 64)  # key size
Embed = hax.Axis("embed", 512)  # embedding size

# alternatively:
#Pos, KPos, Head, Key, Embed = hax.make_axes(pos=1024, key_pos=1024, head=8, key=64, embed=512)


def attention_scores(Key, KPos, query, key, mask):
    # how similar is each query to each key
    scores = hax.dot(query, key, axis=Key) / jnp.sqrt(Key.size)

    if mask is not None:
        scores -= 1E9 * (1.0 - mask)

    # convert to probabilities
    scores = haliax.nn.softmax(scores, KPos)
    return scores


def attention(Key, KPos, query, key, value, mask):
    scores = attention_scores(Key, KPos, query, key, mask)
    answers = hax.dot(scores, value, axis=KPos)

    return answers


# Causal Mask means that if pos >= key_pos, then pos can attend to key_pos
causal_mask = hax.arange(Pos).broadcast_axis(KPos) >= hax.arange(KPos)


class Attention(eqx.Module):
    proj_q: hnn.Linear  # [Embed] -> [Head, Key]
    proj_k: hnn.Linear  # [Embed] -> [Head, Key]
    proj_v: hnn.Linear  # [Embed] -> [Head, Key]
    proj_answer: hnn.Linear  # output projection from [Head, Key] -> [Embed]

    @staticmethod
    def init(Embed, Head, Key, *, key):
        k_q, k_k, k_v, k_ans = jax.random.split(key, 4)
        proj_q = hnn.Linear.init(In=Embed, Out=(Head, Key), key=k_q)
        proj_k = hnn.Linear.init(In=Embed, Out=(Head, Key), key=k_k)
        proj_v = hnn.Linear.init(In=Embed, Out=(Head, Key), key=k_v)
        proj_answer = hnn.Linear.init(In=(Head, Key), Out=Embed, key=k_ans)
        return Attention(proj_q, proj_k, proj_v, proj_answer)

    def __call__(self, x, mask=None):
        q = self.proj_q(x)
        # Rename "position" to "key_position" for self attention
        k = self.proj_k(x).rename({"position": "key_position"})
        v = self.proj_v(x).rename({"position": "key_position"})

        answers = attention(Key, KPos, q, k, v, causal_mask)

        x = self.proj_answer(answers)
        return x

Haliax was created by Stanford's Center for Research on Foundation Models (CRFM)'s research engineering team. You can find us in the #levanter channel on the unofficial Jax LLM Discord.

Documentation

Tutorials

These are some tutorials to get you started with Haliax. They are available as Colab notebooks:

Introduction to Haliax with Transformers
Distributed Training in Haliax (including FSDP)
Tensor Parallelism in Haliax
Mixed Precision with jmp (This one is really a tutorial for jmp but it's how to use it with Haliax...)

API Reference

Haliax's API documentation is available at haliax.readthedocs.io.

Contributing

We welcome contributions! Please see CONTRIBUTING.md for more information. We also have a list of good first issues to help you get started. (If those don't appeal, don't hesitate to reach out to us on Discord!)

License

Haliax is licensed under the Apache License, Version 2.0. See LICENSE for the full license text.

Development Status
- 4 - Beta
Intended Audience
- Science/Research
License
- OSI Approved :: Apache Software License
Operating System
- MacOS :: MacOS X
- POSIX :: Linux
Programming Language
- Python :: 3

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

marin_haliax-0.2.101.dev33861062468.tar.gz (780.9 kB view details)

Uploaded Sep 4, 2026 Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

The dropdown lists show the available interpreters, ABIs, and platforms. Enable javascript to be able to filter the list of wheel files.

marin_haliax-0.2.101.dev33861062468-py3-none-any.whl (159.2 kB view details)

Uploaded Sep 4, 2026 Python 3

File details

Details for the file marin_haliax-0.2.101.dev33861062468.tar.gz.

File metadata

Download URL: marin_haliax-0.2.101.dev33861062468.tar.gz
Upload date: Sep 4, 2026
Size: 780.9 kB
Tags: Source
Uploaded using Trusted Publishing? Yes
Uploaded via: twine/7.0.0 CPython/3.13.14

File hashes

Hashes for marin_haliax-0.2.101.dev33861062468.tar.gz
Algorithm	Hash digest
SHA256	`3b06e20199481ae708a3ae828c2be043ac689acc30d08e140b70fb7a95d0efa6`
MD5	`cd84af7748190f2431ddbe796a052651`
BLAKE2b-256	`6bea0556805b78621cee9308c38752ea5b51d562b52389b55de2f8788dd6b28a`

See more details on using hashes here.

Provenance

The following attestation bundles were made for marin_haliax-0.2.101.dev33861062468.tar.gz:

Publisher: marin-release-libs-wheels.yaml on marin-community/marin

Attestations: Values shown here reflect the state when the release was signed and may no longer be current.

Statement:
- Statement type: https://in-toto.io/Statement/v1
- Predicate type: https://docs.pypi.org/attestations/publish/v1
- Subject name: marin_haliax-0.2.101.dev33861062468.tar.gz
- Subject digest: 3b06e20199481ae708a3ae828c2be043ac689acc30d08e140b70fb7a95d0efa6
- Sigstore transparency entry: 2709892418
- Sigstore integration time: Sep 4, 2026
Source repository:
- Permalink: marin-community/marin@b838ce1c0d2fa741335bab1ad812aae8d85e0bd5
- Branch / Tag: refs/heads/main
- Owner: https://github.com/marin-community
- Access: public
Publication detail:
- Token Issuer: https://token.actions.githubusercontent.com
- Runner Environment: github-hosted
- Publication workflow: marin-release-libs-wheels.yaml@b838ce1c0d2fa741335bab1ad812aae8d85e0bd5
- Trigger Event: schedule