Xnizzorg

pondllm-qwen3.5-0.8b-base-v4-simulator-native

README

License: apache-2.0

Evaluation

Greedy held-out evaluation produced:

100% strict JSON and 100% observation-legal actions;
98% useful signalling, 1% redundant signalling, and 98% correct signal payloads;
98% informed-recipient accuracy and 99.5% visible-food override accuracy;
100% safe-share accuracy, 0.4% unsafe-reserve sharing, and no unnecessary sharing;
99.7% exact accuracy on the 1,000-example survival counterfactual suite.

In 16 matched simulator layouts, the recipient foraged in 93.75% of normal-channel worlds, 0% with delivery blocked, and 6.25% with corrupted coordinates. The normal-minus-blocked causal effect was +93.75 percentage points.

The complete methodology, gates, baselines, hashes, limitations, and commands are in the V4 results report.

Use

python
from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base_id = "Qwen/Qwen3.5-0.8B-Base"
adapter_id = "Xnizzorg/pondllm-qwen3.5-0.8b-base-v4-simulator-native"

tokenizer = AutoTokenizer.from_pretrained(base_id)
base = AutoModelForCausalLM.from_pretrained(base_id, device_map="auto")
model = PeftModel.from_pretrained(base, adapter_id)

Use PondLLM's prompt builder and strict action parser rather than treating this as a general chat adapter. Generation evaluation used greedy decoding.

Training

The adapter was trained fresh for two epochs on 27,014 records with seed 7, 4-bit NF4 base-model loading, BF16 compute, completion-only loss, LoRA rank 8 and alpha 16 over all linear layers, learning rate 2e-4, micro-batch size 16, and gradient accumulation 2. Training took 10,399 seconds on an NVIDIA GeForce RTX 5070 Ti.

Dataset SHA-256: 9BDE22C42DC62B66B26449857C8005E41CE5ABEBF9BEA6C16ED72467CBAEF7EE

Adapter SHA-256: E0B76547781DB57F94D19175A9791430362BC9B5355EA7B221F67B5C4477E82B

Limitations

This is a narrow research action policy, not a general-purpose language model. Controlled two-agent communication succeeds, but an ordinary four-seed probe found no spontaneous signals in 13 richer multi-agent opportunities. Two low-energy offspring also pursued food they could not reach before exhaustion. These are explicit V4.1 targets. Results cover short fixed-seed simulations and do not establish population fitness, open-ended evolution, or emergent language.

This is supervised adapter training only. Population-scale reinforcement learning remains intentionally deferred.

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Model Details

Model Provider

Xnizzorg

Model Tree

Base

Qwen/Qwen3.5-0.8B-Base

Adapter

this model

Input Modalities

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Output Modalities

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Supported Functionality