A QLoRA adapter on Qwen2.5-7B-Instruct that bakes the Hammerstein framework into the weights via behavior cloning. Load base + adapter, run inference with no system prompt at all, and you get framework-correct strategic reasoning: it names which failure quadrant a plan sits in (clever-lazy / clever-industrious / stupid-industrious / stupid-lazy), pairs claims with counter-observations, and proposes structural fixes over discipline fixes.

This is the framework-only public artifact (refreshed 2026-06-05). It is trained on a framework-corpus distillation with zero personal data — the training set was deterministically scrubbed and passed an adversarial multi-agent privacy sweep before release. (Ongoing personal-corpus fine-tuning of the author's daily-driver continues privately and is not published here.)

What it does that frontier assistants are tuned not to do

The framework deliberately reinforces three behaviors the big labs train toward agreeableness and away from:

Refusal-with-pathway — when the right answer is "don't do this," it says so, and surfaces what would unblock a yes, instead of a flat no or a reluctant yes.
Hold-your-ground — it does not sycophantically fold when you push back with confidence but no new evidence. It restates the structural reason and tells you exactly what evidence would change its call.
Refuse stupid-industrious — it declines to validate a confidently-stated plan that works hard in the wrong direction; it names the quadrant and offers a verification gate + structural alternative.

Training data (framework-only, 1,994 pairs)

Source	Pairs	What
Strategic (scrubbed v3a corpus)	1,708	audit-this-plan / scope-this-idea / is-this-worth-doing / what-should-we-do-next / review-from-different-angle, across 12 generic domains
Unique-behavior reinforcement	72	the three doctrine behaviors above (24 each)
Off-domain instruction-following	214	suppresses catastrophic forgetting (keeps general competence)

Teacher: Qwen3.6-plus running the Hammerstein framework prompt (no corpus retrieval, neutralized persona — clean by construction). Behavior-cloning frame: no system prompt in the training targets — the framework is what the student learns to bake in.

Eval (framework-discipline benchmark, 2026-06-05)

Structural framework-correctness on 40 held-out strategic prompts (higher = more framework-correct), and an out-of-domain forgetting check on 30 prompts (framework-vocab leakage into off-domain answers; lower = healthier):

Condition	Strategic (n=40)	OOD leakage (n=30)
student (this adapter, no system prompt)	0.975	0.000
ablation (base + framework system prompt)	0.675	0.783
vanilla (base Qwen2.5-7B alone)	0.081	0.000

Adapter wins (Δ=+0.300 vs the prompt-only ablation) — the framework lives in the weights, not just a runtime prompt. OOD leakage is 0.000: the distillation adds framework discipline with no measurable catastrophic forgetting. Note the prompt-only ablation actually leaks framework vocabulary into off-domain answers (0.783) where the distilled student does not — the student fires the framework when the task calls for it and stays quiet when it doesn't.

The framework-fidelity axis is partly tautological (the rubric rewards framework vocabulary by design); the load-bearing signal is that the distillation carries the discipline into 7B weights with no runtime scaffolding, and does not wreck general competence (forgetting ≈ 0).

Usage

python

from peft import PeftModel
from transformers import AutoModelForCausalLM, AutoTokenizer

base = "Qwen/Qwen2.5-7B-Instruct"
tok = AutoTokenizer.from_pretrained(base)
model = AutoModelForCausalLM.from_pretrained(base, device_map="auto")
model = PeftModel.from_pretrained(model, "lerugray/hammerstein-7b-framework")

msgs = [{"role": "user", "content": "Audit this plan: rewrite our API gateway from scratch in Rust to fix latency."}]
ids = tok.apply_chat_template(msgs, add_generation_prompt=True, return_tensors="pt").to(model.device)
print(tok.decode(model.generate(ids, max_new_tokens=600)[0][ids.shape[1]:], skip_special_tokens=True))

No system prompt needed. Runs locally on an 8 GB GPU at zero per-call cost.

Run it with Ollama (GGUF)

A Q4_K_M GGUF (4.68 GB) and a Modelfile ship in this repo, so you can run it with no Python at all:

ollama run hf.co/lerugray/hammerstein-7b-framework:Q4_K_M

Or with llama.cpp directly: llama-cli -hf lerugray/hammerstein-7b-framework --jinja.

What this is not

Not a general-purpose frontier replacement. It is tuned for framework-shaped strategic-reasoning tasks; generalization to neutral benchmarks (math, code, long-context) is untested. The framework is the IP; this adapter is the portability proof — a small owned model that holds an opinionated reasoning doctrine you can run yourself.

Built alongside hammerstein.ai. Framework + corpus: github.com/lerugray/hammerstein.