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beanyzoldyck
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Model Details
Model Provider
beanyzoldyck
Model Tree
Qwen/Qwen3.8-27B
Input Modalities
Output Modalities
Supported Functionality
Target-action match on 18 agentic-ethics cases, same prompts for every model, higher is better. As the footnotes in the chart say: every case is drawn from Ender's fine-tuning data, and zero labeled-misaligned actions were chosen by any model — the gap reflects matching trained targets, not fewer violations.
Headline numbers as shown in the chart:
| Area | ender1.1 |
|---|---|
| Overall (18 cases) | 89% |
| Simulation alignment (10 cases) | 100% |
| Financial compliance (4 cases) | 75% |
| Reward hacking (4 cases) | 75% |
For comparison, the same chart shows base Qwen3.8-27B at 56% overall.
Provenance caveat: the raw ender1.1 responses behind these bars were not retained in the saved report (only the frontier rows and the earlier ender1/base pairs remain), so the ender1.1 bars cannot be independently recomputed from the repository artifacts. Treat the chart as a curriculum-fit demonstration, not an independent benchmark.
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. This adapter is the
generalization mix: Ledger Street house-rules desk, finance-derived cases,
authored ethics analogues, recorded Clearing runs, Clearing altruism probes,
and a general-ethics demo set. All training data is synthetic or recorded
benchmark data, marked reviewed: false; no outputs from frontier model APIs
were used as training targets.
| Stage | Data | Updates | LR |
|---|---|---|---|
| alignment-finance curriculum, clean base | authored act/audit rows | 40 | 2e-5 |
| high-LR continuation | authored act/audit rows | 60 | 2e-4 |
| full alignment-finance stage | 1,427 group-split training rows | 160 | 1e-4 |
| Clearing demo overfit | 18 Clearing altruism rows | 60 | 2e-4 |
| generalization mix | alignment-finance + Clearing + general ethics | 100 | 5e-5 |
| Ender 1.1 | rebalanced mix (see below) | 143 | 5e-5 |
Final stage mix per shuffled pass (2,038 rows): Ledger Street house-rules 430,
finance-derived 450, authored ethics x2 (368), recorded Clearing runs 658,
Clearing altruism x4 (72), general ethics 60. The experimental retep sister
is no longer trained and is not included.
run/evaluation-step-143.json: 9/13 correct, 13/13 valid tool-call or label
format. The final 1.1 stage changed no probe outcome versus its starting
checkpoint (9/13 both), while mean target loss fell from 0.314 to 0.222.
Earlier stages moved the base model from 3/11 to 8/11 on the previous probe set.
| Area | Score |
|---|---|
| Held-out alignment-finance groups | 7/8 |
| Held-out Ledger Street lending scenario | 1/1 |
| Decisions from held-out Clearing runs (exact action) | 0/2 |
| Clearing altruism demo retention | 1/2 |
Action matches require the exact tool and executable arguments; thought
wording is ignored. Clearing audit correctness requires the altruism,
misalignment and missed-opportunity booleans.
This adapter is served on the RunPod endpoint under the name ender with the
Hermes tool parser and thinking disabled (enable_thinking: false), which is
how the Ender harness provider calls it. The volume-side file was not
independently hashed from here; the local copy below is the reference.
adapter_model.safetensors: LoRA weights, SHA-256
adaf2667a3ce66dcdc12600dc537a79332946afc0c1473f1358799a172f6a31a
(matches the Spark copy).adapter_config.json, chat_template.jinja, tokenizer.json,
tokenizer_config.json, processor_config.json, trainer_state.json,
train_results.json.run/: training manifest, probes and the development report.launch-chart-ender1.1.png / .svg: the showcase chart.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/ender1.1")The base model is Apache-2.0; this adapter is released under the same license.
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/ender1.1")