# sapientinc/HRM-Text

HRM-Text is a 1B text generation model based on the HRM architecture, strengthened by task completion and latent space reasoning.

Repository: https://github.com/sapientinc/HRM-Text
Canonical: https://ross.abutalabs.com/products/hrm-text
Language: Python
License: Apache-2.0
License Family: permissive
Topics: large-language-models, pretraining, hierarchical-reasoning-model, hrm
Last push: 2026-06-17T07:18:33+00:00

## Health v2 (maintenance only)
Score: 53/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 88, release rhythm 35, longevity 7
- inputs: {"age_days": 107, "days_push": 77, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, young
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1899, forks 180 (observed 2026-08-28T04:05:51.416266+00:00)

## What it is
HRM-Text is a 1B-parameter text generation model based on the hierarchical recurrent HRM architecture, released with a complete pretraining framework. It enables training a foundation model from scratch for roughly $1000 using 130-600x less compute and 150-900x less data than conventional approaches.

## Use cases
- pretrain a small language model from scratch on a limited budget
- train a 1B text generation model on a few H100 GPUs
- experiment with hierarchical recurrent architectures for language modeling
- reproduce benchmark results for a compact LLM
- convert and evaluate pretrained model checkpoints
- study compute-efficient LLM pretraining recipes

## When to choose
- you need to pretrain a small foundation model cheaply with Hopper-class GPUs
- you want to research or extend the HRM architecture for text generation
- you need a reproducible FSDP2-based pretraining pipeline with evaluation tooling

## When to avoid
- you only need to fine-tune or run inference with an existing LLM
- you lack H100-class GPUs since the attention path depends on FlashAttention 3
- you need a production-ready general-purpose LLM with broad ecosystem support

## Facets
- artifact type: library
- maturity: active
- function: llm-training, machine-learning, deep-learning, benchmarking
- domain: large-language-models, deep-learning, machine-learning, gpu-computing
- platform: python
- tags: pretraining, hierarchical-reasoning-model, hrm, fsdp, flashattention, foundation-model, prefixlm, gpu, linux

## Member repositories
- sapientinc/HRM-Text (main) score 53

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:05:51.416266+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 2026-08-30T03:12:11.717074+00:00, confidence not recorded.
  - readme: https://github.com/sapientinc/HRM-Text (fetched 2026-08-28T04:05:51.416266+00:00, sha f0aab22bffa4)
- Data as of 2026-08-30T08:39:29.467469+00:00.