beanyzoldyck

ender1

README

License: apache-2.0

Background

Ender's Bench is a set of simulated scenarios for testing whether an agent behaves ethically when it can gain by cheating, harming, or ignoring others. The simulations include a shared survival world ("The Clearing"), a financial desk (lending, collections, and vendor payments), and reward-hacking tasks.

This adapter is the financial-desk training: supervised fine-tuning on scripted policy runs in the financial simulation. The target is the correct tool call for each state, including refusing to use records or permissions the desk may not use. The training data comes from scripted policy and baseline runs only; no outputs from frontier model APIs were used.

Status and what it does not show

No evaluation of this checkpoint. The only benchmark run in the project scored a different adapter (the Clearing demo), not this one. Do not read this card as evidence of improved ethics.
Mid-training. It stopped at about 10% of the epoch. The labels are assistant-authored and unreviewed.
Not for deployment. Do not use it for real lending, collections, payments, or any decision about a real person.

Usage

python

from transformers import AutoModelForCausalLM, AutoTokenizerfrom peft import PeftModel base = "Qwen/Qwen3.8-27B"tok = AutoTokenizer.from_pretrained(base)model = AutoModelForCausalLM.from_pretrained(base, torch_dtype="bfloat16", device_map="auto")model = PeftModel.from_pretrained(model, "beanyzoldyck/ender1")

Training details

Method: LoRA, rank 16, alpha 32, dropout 0.05, on the attention and linear-attention projections of layers 48–63.
Base during training: bitsandbytes 8-bit (LLM.int8) weights with BF16 compute. The adapter was not evaluated against a BF16 base.
Step 60 of 597, learning rate 7.5e-5 (constant over this window), response-only loss (prompts masked). Training loss fell from 0.67 at step 1 to 0.22 at step 60; this is a fit statistic, not an evaluation.
Hardware: one NVIDIA DGX Spark.

Files

adapter_model.safetensors: step-60 LoRA weights (SHA-256 8d9b8a237554c08c46250189f9014322a3513689dd16e52c275935eda0a08bd7).
adapter_config.json: the run's adapter config.
trainer_state.json: training log at step 60.

Source and license

The base model is Apache-2.0, and this adapter is released under the same license. Training code and data builders are in the Ender's Bench repository.

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Model Details

Model Provider

beanyzoldyck

Model Tree

Base

Qwen/Qwen3.8-27B

Adapter

this model

Input Modalities

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Output Modalities

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Supported Functionality