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EmmaLeonhart/
reservoir-agent-gpt2-medium-batch

Name: reservoir-agent-gpt2-medium-batch
Availability: InStock

openai-community/gpt2-medium

finetune

mit

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Reservoir Agent batch — gpt2-medium

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.

Population

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

Use

Each seed_<n>/ is a complete loadable reservoir agent. Load the recommended one:

python

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.