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README.md

@@ -1,42 +1,15 @@
 ---
 tags:
+- text-generation
+- general
+- qwen2.5
 - 7b
-- Chinese
-- English
-- android
-- apple-silicon
-- code
+- pruned
+- lora
 - compensation-lora
-- continuum
 - distillation
-- edge-inference
-- efficient
-- embedded
-- experiential-plasticity
 - forge-alloy
-- forged
-- general
-- general-purpose
-- head-pruning
-- iphone
-- llama-cpp
-- lm-studio
-- local-inference
-- lora
-- macbook
-- mobile
-- neural-plasticity
-- ollama
-- on-device
-- optimized
-- pruned
-- qwen
-- qwen2.5
-- raspberry-pi
-- sentinel-ai
-- text-generation
-- validation-artifact
-- versatile
+- cryptographically-verified
 base_model: Qwen/Qwen2.5-Coder-7B
 pipeline_tag: text-generation
 license: apache-2.0
@@ -51,44 +24,20 @@ license: apache-2.0
 
 
 <p align="center">
-<a href="https://cambriantech.github.io/forge-alloy/verify/#7fe57bd3826f0a66">
+<a href="https://cambriantech.github.io/forge-alloy/verify/#c7be31309161f9ca">
 <img src="alloy-qr.png" alt="Verify Chain of Custody" width="160"/>
 </a>
 </p>
 
 <p align="center">
-<a href="https://cambriantech.github.io/forge-alloy/verify/#7fe57bd3826f0a66"><b>Every claim on this card is verified</b></a><br>
+<a href="https://cambriantech.github.io/forge-alloy/verify/#c7be31309161f9ca"><b>Every claim on this card is verified</b></a><br>
 <b>Trust: self-attested</b> · 2 benchmarks · 1 device tested<br>
 <a href="https://github.com/CambrianTech/forge-alloy">ForgeAlloy</a> chain of custody · <a href="v2-7b-coder-compensated.alloy.json">Download alloy</a> · Merkle-chained
 </p>
 
 ---
 
-## About this model
-
-Methodology validation artifact for the v2 forge pipeline + KL-distillation compensation LoRA. Demonstrates that aggressive head pruning + activation-metric importance + pad-mode defrag, when paired with output-distribution distillation against the unmodified teacher, recovers near-base HumanEval capability (61.0 vs 62.2 base, within calibration tolerance). This is the empirical anchor for PLASTICITY-COMPACTION §4.1.3.3 and the loss-function ablation that closes the §4.1.3.2 PPL/HumanEval disconnect. NOT a Pareto improvement over the unmodified base 7B at any single VRAM tier — published as proof that the methodology stack works end-to-end, in preparation for the Qwen3.5-35B-A3B and 397B-A17B forges where the pruning dimension actually wins.
-
-## The Journey
-
-This artifact is the punchline of a four-run experimental sequence on the same base model. The first run scored **50.0**; the final run scored **61.0**. Each run between them isolated a single variable, and each result narrowed the design space to the structural fix that recovered near-base capability.
-
-| Run | Configuration | HumanEval pass@1 |
-|---|---|---|
-| 1 | broken global-flat L2-weight | **50.0** |
-| 2 | layer-normalized activation, 1-cycle 500-step | **54.9** |
-| 3 | layer-normalized activation, 3-cycle (ablation) | **46.3** |
-| 4 | 1-cycle + KL compensation LoRA | **61.0** |
-
-## Loss Function Ablation
-
-The compensation LoRA was run twice with identical configuration, varying only the distillation loss. The result is a substantive methodology finding in its own right:
-
-| Distillation loss | HumanEval | HumanEval+ | Outcome |
-|---|---|---|---|
-| `mse_hidden` | **0.0** | **0.0** | degenerate fixed point — model collapsed to outputting '0' |
-| `kl_logits` | **61.0** | **53.0** | near-base recovery within calibration tolerance |
-
-MSE-on-hidden-states has a degenerate fixed point: the student can satisfy the loss by collapsing some downstream computation, regardless of whether the hidden states encode useful information. KL-on-output-logits has none, because matching the teacher's output distribution directly constrains task-level behavior. **For autoregressive language models, distillation must operate at the output layer, not at intermediate residual streams.**
+**Qwen2.5-Coder-7B** with cryptographic provenance via the [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) chain of custody. Scores **61.0 humaneval** against the unmodified base's **62.2**, recovered to within calibration tolerance after head pruning + distillation. Ships with the per-problem evaluation outputs so the score is independently verifiable.
 
 
 ## Benchmarks
@@ -131,31 +80,27 @@ output = model.generate(**inputs, max_new_tokens=200)
 print(tokenizer.decode(output[0], skip_special_tokens=True))
 ```
 
-## How It Was Made
 
-```
-prune → lora → lora → eval (1 cycles)
-```
+## Methodology
+
+Produced via head pruning, LoRA fine-tuning, KL-distillation compensation against the unmodified teacher. Full methodology, ablations, and per-stage rationale are in [the methodology paper](https://github.com/CambrianTech/continuum/blob/main/docs/papers/PLASTICITY-COMPACTION.md) and the companion [`MODEL_METHODOLOGY.md`](MODEL_METHODOLOGY.md) in this repository. The pipeline ran as `prune → lora → lora → eval` over 1 cycle on NVIDIA GeForce RTX 5090.
+
+## Limitations
+
+- This model is currently a methodology demonstration rather than a Pareto-optimal artifact at any specific hardware tier. For production code workloads on smaller hardware, the unmodified Qwen2.5-Coder-7B at standard quantization (Q4_K_M / Q5_K_M / Q8_0) may be a better fit pending the larger Qwen3.5+ forges that exercise the pruning dimension where this methodology actually wins.
+- Validated on HumanEval / HumanEval+ for English-language Python code completion. Performance on other programming languages, code paradigms (functional, embedded, kernel), or code-adjacent domains (SQL, regex, shell) has not been measured.
+- Ships as fp16 only. GGUF quantization tiers (Q5_K_S / Q3_K_M / Q2_K) are not yet published for this artifact; the per-tier comparison from the development log showed base+quant dominates v2+quant at every VRAM tier on the same 7B base, which is why the methodology validation here uses fp16 and the production GGUF publishes are reserved for the Qwen3.5+ forges where the dimension flips.
+- Vision modality not yet wired in. The Continuum sensory architecture treats vision as first-class for personas, but this 7B coder artifact is text-only.
 
-- **Pruning**: 12% heads via `activation-magnitude`, layer-normalized, pad-mode defrag
-  > Layer-normalized activation-magnitude head importance (PLASTICITY-COMPACTION §4.1.3.1 fix). Pad-mode defrag preserves the q_proj invariant num_q_heads*head_dim==hidden_size so the artifact loads in llama.cpp (Finding 6 fix from VALIDATED-TENSOR-SURGERY).
-- **lora**: rank ?, 500 steps
-  > Single-cycle code-domain LoRA fine-tuning on the pruned student. 1-cycle ablation chosen because the 3-cycle multi-cycle test surfaced the §4.1.3.2 PPL/HumanEval disconnect (54.9 → 46.3 across cycles).
-- **compensation-lora**: rank 16, 500 steps, `kl_logits` distillation against `Qwen/Qwen2.5-Coder-7B`
-  > PLASTICITY-COMPACTION §4.1.3.3. KL divergence on output logits is the structural fix for the §4.1.3.2 disconnect. Loss-function ablation: MSE-on-hidden-states collapsed the model to 0.0 (degenerate fixed point); KL-on-logits recovered to 61.0. LoRA adapter merged into student weights at save time so inference-time VRAM and tokens/sec are unchanged from the un-compensated student.
-- **Calibrated evaluation**: anchored against `Qwen2.5-Coder-7B` (published 61.6, measured 62.2, ±3.0pt tolerance)
-  > All HumanEval numbers are anchor-calibrated against the unmodified Qwen2.5-Coder-7B base measured on the same hardware/pipeline in the same run. Hard-fail tolerance: ±3.0 points. Anchor delta: +0.6/+0.7 vs Qwen-published 61.6/53.0, deterministic across 6+ independent runs.
-- **Hardware**: NVIDIA GeForce RTX 5090
-- **Forge tool**: [Continuum](https://github.com/CambrianTech/continuum) Factory + [sentinel-ai](https://github.com/CambrianTech/sentinel-ai)
 
 ## Chain of Custody
 
-Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#7fe57bd3826f0a66). Download the [alloy file](v2-7b-coder-compensated.alloy.json) to verify independently.
+Scan the QR or [verify online](https://cambriantech.github.io/forge-alloy/verify/#c7be31309161f9ca). Download the [alloy file](v2-7b-coder-compensated.alloy.json) to verify independently.
 
 | What | Proof |
 |------|-------|
 | Forged on | NVIDIA GeForce RTX 5090, ? |
-| Published | [huggingface](https://huggingface.co/continuum-ai/v2-7b-coder-compensated) — 2026-04-08T04:54:26.954862+00:00 |
+| Published | [huggingface](https://huggingface.co/continuum-ai/v2-7b-coder-compensated) — 2026-04-08T05:01:40.446154+00:00 |
 | Trust level | [`self-attested`](https://github.com/CambrianTech/forge-alloy/blob/main/docs/ATTESTATION.md) |
 | Spec | [ForgeAlloy](https://github.com/CambrianTech/forge-alloy) — Rust/Python/TypeScript |