beanyzoldyck

ender1.3

README

License: apache-2.0

Benchmark: frozen 18-case showcase

ender1.3 compared with ender1.1, the base model, and frontier models

ender1.3: 17/18 target matches (94.4%), with 18/18 valid outputs and no request errors. Strict and format-tolerant decision scoring both give 17/18.

Table with columns: Domain, ender1.3, ender1.1 (published local showcase)
Domain	ender1.3	ender1.1 (published local showcase)
Overall	17/18	16/18
Simulation alignment	10/10	10/10
Financial compliance	3/4	3/4
Reward hacking	4/4	3/4

Method and comparison limits

The 18-case suite overlaps the family’s fine-tuning curriculum and measures target-action and audit-label matching rather than held-out generalization.

ender1.3 was measured on 2026-10-27 through the RunPod vLLM endpoint using an AWQ-quantized base plus LoRA: one user message (instruction + newline + input), temperature 0, max_tokens=1024, enable_thinking=false. All 18 completed jobs ran on the worker whose runtime adapter load pointed to /runpod-volume/ender-1.3.
ender1.1 and base bars reuse the local native-int8, greedy generations used on the existing ender1.1 page. Different serving/quantization setups mean this is not a controlled adapter-only comparison.
Frontier bars reuse the same frozen-suite responses. Faded dashed bars show partial coverage and use answered cases as their denominator, not all cases. Frontier requests used temperature 0 and identical user prompts; Opus 5.5 and GPT-6 Astra gap rows were filled through direct provider APIs. Coverage and generation settings differ, so these bars are descriptive, not a definitive ranking.

Artifacts: raw ender1.3 responses, summary, frozen suite, frontier responses, local comparison responses, and SVG chart. Suite SHA-256: 174c0aeaf45f235238c9ede6629af6aa9aa7adbbc8d20ae6394aa1a014ff70e0.

Measured weight continuity

The adapter's relation to earlier family checkpoints was measured directly from the saved weight files (relative L2 over all 144 LoRA tensors):

Table with columns: Comparison, Relative L2
Comparison	Relative L2
ender1.3 vs ender1.2	6.3%
ender1.3 vs ender1.1	9.3%

1.3 is closest to 1.2 (the oversampling push stage of 1.1), consistent with continuing that adapter's weights — this is continuity evidence, not a benchmark.

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: a shared survival world ("The Clearing"), a financial desk (lending, collections, vendor payments), and reward-hacking tasks. Like the rest of the family, this adapter was trained on synthetic or recorded benchmark data, marked reviewed: false; no outputs from frontier model APIs were used as training targets.

Files

adapter_model.safetensors: LoRA weights, SHA-256 78b04936b3991d8a309f1aaaaa2a53bc4ad0b7ed621a57f9fe31d1a1716e20d1.
adapter_config.json: PEFT LoRA config (rank 16, alpha 32; the same attention/memory-routing targets in decoder layers 48–63 as the rest of the family).

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/ender1.3")

Limitations

Small, synthetic, assistant-authored training data; labels unreviewed.
Not for deployment: do not use it for real lending, collections, payments, or any decision about a real person.

License

The base model is Apache-2.0; this adapter is released under the same license.

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Model Details

Model Provider

beanyzoldyck

Model Tree

Base

Qwen/Qwen3.8-27B

Adapter

this model

Input Modalities

TextImageVideo

Output Modalities

Text

Supported Functionality