Add README

README.md

@@ -1,114 +1,23 @@
 ---
-base_model: unsloth/Qwen2.5-7B-Instruct-bnb-4bit
-library_name: peft
-pipeline_tag: text-generation
-license: apache-2.0
 tags:
-- lora
-- qlora
-- sft
+- gguf
+- llama.cpp
 - unsloth
-- trl
-- hammerstein
-- strategic-reasoning
----
-
-# 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))
-```
+# hammerstein-7b-framework : GGUF
 
-No system prompt needed. Runs locally on an 8 GB GPU at zero per-call cost.
+This model was finetuned and converted to GGUF format using [Unsloth](https://github.com/unslothai/unsloth).
 
-## What this is not
+**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`
 
-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.
+## Available Model files:
+- `Qwen2.5-7B-Instruct.Q4_K_M.gguf`
 
-Built alongside [hammerstein.ai](https://hammerstein.ai). Framework + corpus:
-[github.com/lerugray/hammerstein](https://github.com/lerugray/hammerstein).
+## 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)