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EmmaLeonhart/reservoir-agent-gpt2-medium-batch
reservoir-agent-gpt2-medium-batch is a machine learning model from EmmaLeonhart. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for reservoir-agent. The card lists the license as mit.
A batch of 10 reservoir agents (different fixed-random reservoir seeds) trained on the cross-pass recall task. A reservoir agent is a new model type: a pretrained transformer with a fixed reservoir brain-surgeried in…
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Updated May 31, 2026
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From the Hugging Face model README
A batch of 10 reservoir agents (different fixed-random reservoir seeds)
trained on the cross-pass recall task. A reservoir agent is a new model type: a pretrained
transformer with a fixed reservoir brain-surgeried in (attended, cross-pass-stateful,
RNN-like) — see the project and
RESERVOIR_AGENTS.md.
The whole population is published, not just the winner. Reservoir performance is
stochastic in the seed; the suboptimal models are kept as signal for learning which
reservoir properties survive selection. The recommended model is seed_9.
| rank | seed | recall | loss_end | pr_frac | recommended |
|---|---|---|---|---|---|
| 0 | seed_9 | 0.17 | 1.269 | 0.113 | yes |
| 1 | seed_1 | 0.17 | 1.362 | 0.114 | |
| 2 | seed_3 | 0.17 | 1.625 | 0.112 | |
| 3 | seed_8 | 0.17 | 1.683 | 0.114 | |
| 4 | seed_5 | 0.17 | 1.738 | 0.114 | |
| 5 | seed_2 | 0.17 | 1.902 | 0.113 | |
| 6 | seed_6 | 0.17 | 2.283 | 0.111 | |
| 7 | seed_4 | 0.17 | 2.345 | 0.114 | |
| 8 | seed_7 | 0.17 | 2.442 | 0.114 | |
| 9 | seed_0 | 0.17 | 2.524 | 0.114 |
Each seed_<n>/ is a complete loadable reservoir agent. Load the recommended one:
from huggingface_hub import snapshot_download
from reservoir.persist import load_reservoir_model
path = snapshot_download("EmmaLeonhart/reservoir-agent-gpt2-medium-batch")
lm = load_reservoir_model(f"{path}/seed_9")
batch_manifest.json records the ranking + each seed's score and reservoir-dynamics signal.