Escarda-86M-Identity — Anti-DEG

A tuned build of Escarda-86M-Identity (86M parameters, SpikeWhaleLM) that follows a change of subject in conversation while keeping the Escarda identity.

Ships as a fully merged standalone model — no adapters or base model needed.

Why this build exists

The Escarda-86M-Base-JL-Anti-DEG build scores higher on factual questions but has no persona — asked "who are you" it answers "I am a young woman." This build applies the same treatment to the identity-tuned checkpoint instead, so the model still knows what it is:

"Who are you?" → "I'm Escarda, a helpful AI assistant created by Quazim0t0 (Dean Byrne)."

Scores

Loops measured on 6 prompt stems; topic adherence on 6 held-out multi-turn topic switches; factual accuracy on 73 unambiguous questions. All at effort n=2 with a 24-token minimum length.

Read the factual row honestly: the difference is −0.042 with a 0.048 standard error on the difference — inside one standard error, i.e. not a measurable change. This build buys topic adherence at no detectable factual cost; it does not improve factual accuracy. For that, Escarda-86M-Base-JL-Anti-DEG scores 0.260 ± 0.051 (2.4σ better) but loses the persona entirely.

At 86M parameters this model is not a source of reliable facts either way.

Decoding matters

The scores above are measured with effort decoding (effort_decode.py, included) plus a 24-token minimum reply length. Greedy decoding does not reach them. Effort levels trade compute for quality: low draws a single sample, high draws six and returns the best.

python effort_decode.py --model Quazim0t0/Escarda-86M-Identity-Anti-DEG \
  --prompt "<|im_start|>user\nHow do I change a car tyre?<|im_end|>\n<|im_start|>assistant\n" \
  --question "How do I change a car tyre?" --level high

Usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

tok = AutoTokenizer.from_pretrained(
    "Quazim0t0/Escarda-86M-Identity-Anti-DEG", trust_remote_code=True)
model = AutoModelForCausalLM.from_pretrained(
    "Quazim0t0/Escarda-86M-Identity-Anti-DEG",
    trust_remote_code=True, dtype=torch.float32).eval()

Prompt format is ChatML:

<|im_start|>user
{question}<|im_end|>
<|im_start|>assistant

Limitations

• 86M parameters, and not instruction-tuned for factual accuracy. Output is often confidently wrong; do not rely on it for facts.
• Native 4096-token context (no Jet-Long on this checkpoint).
• Custom architecture and tokenizer — trust_remote_code=True is required.