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mh-ec2-ttomd-K4-delta
rat-lab/mh-ec2-ttomd-K4-delta
mh-ec2-ttomd-K4-delta is a machine learning model from rat-lab. 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 peft.
LoRA adapters for the K=4, delta cell of the coverage x debiasing table in the risk-sensitive preference-learning experiments (entropic risk, tau = 10).
Access
Public
Updated Sep 23, 2026
Repo size
1.9 GB
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.safetensors1.9 GB · 96%
At a glance
- Library
- peft
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026
- SHA
- c3e999ea
Base models
Model card
From the Hugging Face model README
mh-ec2-ttomd-K4-delta
LoRA adapters for the K=4, delta cell of the coverage x debiasing table in the risk-sensitive preference-learning experiments (entropic risk, tau = 10).
Provenance
Trained locally by Max Horwitz (resumed mid-run).
Trained locally by Max on the UW Hyak cluster, in two parts: SLURM job 40151500 ran to ~step 2300, then job 40268231 resumed the same run directory (--load_dir) and carried it to 4680 (2026-09-18).
Same run directory and hyperparameters throughout; the resume restores the optimizer and TT tracker state, so the step numbering is continuous.
What this run is
| algorithm | online IPO (--alg oipo1), risk_egpo/tt_omd.py |
| coverage | K = 4 (--ypp_samples 4) |
| risk | entropic, tau = 10 (--risk entropic --risk_c 10.0) |
| debiasing | two-timescale bias correction, delta-method estimator |
| TT step size | gamma = 0.1 |
| init | warm start from ipo-e-c10.0/checkpoint-936, 100 warmup steps |
| generation | 64 max new tokens |
| seed | 42 |
| base model | vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT |
| dataset | PKU-Alignment/PKU-SafeRLHF |
| checkpoints | 19 (250 ... 4680, every 250 steps) |
Contents
checkpoint-<step>/ holds the LoRA adapter (adapter_model.safetensors,
adapter_config.json) and tokenizer files. DeepSpeed resume state is not included.
from peft import PeftModel
from transformers import AutoModelForCausalLM
m = AutoModelForCausalLM.from_pretrained("vectorzhou/gemma-2-2b-it-alpaca-cleaned-SFT")
m = PeftModel.from_pretrained(m, "rat-lab/mh-ec2-ttomd-K4-delta", subfolder="checkpoint-4680")
- Library
- peft
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026