Restore full model card (the GGUF push had overwritten it with the Unsloth stub)

README.md

@@ -1,23 +1,127 @@
 ---
+base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
+library_name: peft
+pipeline_tag: text-generation
+license: apache-2.0
 tags:
-- gguf
-- llama.cpp
+- lora
+- qlora
+- sft
 - unsloth
-
+- trl
+- hammerstein
+- strategic-reasoning
+- gguf
+- ollama
 ---
 
-# hammerstein-7b-framework : GGUF
+# Hammerstein-7B Framework — a small, opinionated strategic-reasoning model
+
+A QLoRA adapter on `Qwen2.5-7B-Instruct` that bakes the
+[Hammerstein framework](https://github.com/lerugray/hammerstein) 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
+```
 
-This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
+Or with llama.cpp directly: `llama-cli -hf lerugray/hammerstein-7b-framework --jinja`.
 
-**Example usage**:
-- For text only LLMs:    `llama-cli -hf lerugray/hammerstein-7b-framework --jinja`
-- For multimodal models: `llama-mtmd-cli -hf lerugray/hammerstein-7b-framework --jinja`
+## What this is not
 
-## Available Model files:
-- `Qwen2.5-7B-Instruct.Q4_K_M.gguf`
+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.
 
-## Ollama
-An Ollama Modelfile is included for easy deployment.
-This was trained 2x faster with [Unsloth](https://github.com/unslothai/unsloth)
-[<img src="https://raw.githubusercontent.com/unslothai/unsloth/main/images/unsloth%20made%20with%20love.png" width="200"/>](https://github.com/unslothai/unsloth)
+Built alongside [hammerstein.ai](https://hammerstein.ai). Framework + corpus:
+[github.com/lerugray/hammerstein](https://github.com/lerugray/hammerstein).