File size: 2,146 Bytes
790f3b4 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 | ---
license: mit
library_name: mlx
tags: [mlx, knowledge-editing, retrieval, synthetic-data]
---
# Hemispheres: step-1 checkpoints
Weights for the step-1 synthetic-world runs of Hemispheres, a language model that keeps its knowledge in a separate, editable store and reads it inside the forward pass. Each run directory holds `config.json` (every training flag, seed and model shape) and MLX safetensors checkpoints.
The run records (metrics, per-question evaluation outputs, data fingerprints), the comparison report and the code to load and evaluate these weights are in the project repository, under `results/step1/`.
| run | arm | params | checkpoints | sha256 |
|---|---|---|---|---|
| `context-a` | context | 25.5M | `final`, `latest` | `ae48aaa6bc50` |
| `context-multi-bios` | context | 25.5M | `final`, `latest` | `8d97615b89a9` |
| `context-multi-bios-20k` | context | 25.5M | `final`, `latest` | `11424cc903c9` |
| `context-multi-bios-20k-s1` | context | 25.5M | `final`, `latest` | `4e9410c97d16` |
| `dense-a` | dense | 25.5M | `final`, `latest` | `81e8eb4ac912` |
| `dense-a-k100` | dense | 25.5M | `final`, `latest` | `c2535e281e08` |
| `dense-a-to-b` | dense | 25.5M | `final`, `latest` | `17c630e69033` |
| `latent-a` | latent | 29.2M | `final`, `latest` | `f4f3b1fbe96a` |
| `latent-a-all` | latent | 29.2M | `final`, `latest` | `57dc7d12714c` |
| `latent-multi` | latent | 29.2M | `final`, `latest` | `1b2490e51d93` |
| `latent-multi-nohop` | latent | 29.2M | `latest` | `dc2494c38336` |
| `lookup-a` | lookup | 25.5M | `final`, `latest` | `e06385a28712` |
```sh
pip install -e '.[hub]'
python -m hemispheres.records fetch latent-multi # into runs/latent-multi/, sha256-checked
python -m hemispheres.synth.build --name world-b --index 1 --edits 1,100,1000
python -m hemispheres.evaluate --run runs/latent-multi --data data/world-b --sets all --n 300
```
Models are 25.5M (dense, lookup, context) or 29.2M (latent) parameters, fp32, trained from scratch on generated worlds. They are research artifacts for the synthetic task, not general-purpose language models.
Released under the MIT license (see `LICENSE`).
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