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mh-ec2-ttomd-K4-jk
rat-lab/mh-ec2-ttomd-K4-jk
mh-ec2-ttomd-K4-jk 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, jackknife 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
1.6 GB
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Repo makeup
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.safetensors1.6 GB · 96%
At a glance
- Library
- peft
- Access
- Public
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026
- SHA
- f59dac4b
Base models
Model card
From the Hugging Face model README
mh-ec2-ttomd-K4-jk
LoRA adapters for the K=4, jackknife cell of the coverage x debiasing table in the risk-sensitive preference-learning experiments (entropic risk, tau = 10).
Provenance
NOT trained by Max -- pulled from an existing rat-lab repo.
Pulled unchanged from rat-lab/rlj-ec2-fig9-K4-jk.
Trained earlier by the RLJ/EC2 effort, not by Max. Verified byte-identical to the source repo (sha256 on checkpoints 250 / 2250 / 4000). NOTE: this run stops at step 4000 -- the source repo has no later checkpoints, so this cell has 16 checkpoints where the others have 19.
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, leave-one-out jackknife 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 | 16 (250 ... 4000, 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-jk", subfolder="checkpoint-4000")
- Library
- peft
- Created
- Sep 23, 2026
- Updated
- Sep 23, 2026