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README.md

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+---
+license: apache-2.0
+base_model: Jackrong/Qwopus3.6-27B-Coder
+tags:
+  - gguf
+  - quantization
+  - asi-evolve
+  - moq
+  - qwen3.5
+  - coder
+  - research-artifact
+  - superseded
+---
+
+# Qwopus3.6-27B-Coder-Predator-Q-ASI-v2
+
+> **⚠️ STATUS (2026-06-24): SUPERSEDED by [Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0](https://huggingface.co/Fredred89/Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0) (MoQ-4.0, 12.4 GiB).**
+>
+> Multi-benchmark validation (HumanEval+ 164, MBPP+ 100, BigCodeBench 50, LCB-30 30) showed **no statistically significant difference** between v2 and MoQ-4.0 on any benchmark (all McNemar p > 0.31, Bonferroni α=0.0167). The +16% KL-divergence improvement of v2 did not translate to measurable task gains. MoQ-4.0 is preferred because it is 2 GB smaller and requires zero LLM iteration cost to build.
+>
+> This model is retained as a research artifact and as a baseline for "what LLM-guided recipe search produces." It is **not recommended for production use**.
+
+## What this IS
+
+ASI-Evolved v2 is the result of an LLM-guided recipe search starting from MoQ-4.0 (the kaitchup MoQ recipe applied directly to Qwopus). The ASI-Evolve framework iteratively improved the per-tensor quantization recipe using paired perplexity and KL-divergence against the F16 reference on WikiText-2. After 20 iterations of search:
+
+**Quantization recipe (29 tensor-type overrides, 14.4 GiB, ~4.51 BPW):**
+- L64 (nextn) tensors: bf16 (kaitchup's recipe was missing these — they were defaulting to Q4_K)
+- 5 most-sensitive attention layers (L3, L7, L11, L59, L63): IQ4_NL
+- Edge-layer ffn_up: IQ4_NL; middle ffn_up: IQ4_XS
+- Remainder: kaitchup MoQ-4.0 recipe
+
+**Quantitative gains over MoQ-4.0:**
+- KL-divergence vs F16: 0.034 → 0.0287 (16% improvement)
+- PPL ratio: 1.020
+- File size: 12.4 → 14.4 GiB (+2 GiB, +16%)
+
+**What did NOT improve (Phase 5 multi-benchmark validation):**
+
+| Benchmark | MoQ-4.0 | v2 | Δ | McNemar p |
+|---|---|---|---|---|
+| HumanEval+ base (164) | 92.7% | 93.3% | +0.6pp | 0.65 |
+| HumanEval+ plus (164) | 89.6% | 87.8% | -1.8pp | 0.65 |
+| MBPP+ base (100) | 92.0% | 93.0% | +1.0pp | 0.56 |
+| MBPP+ plus (100) | 79.0% | 78.0% | -1.0pp | 0.56 |
+| BigCodeBench (50) | 42.0% | 44.0% | +2.0pp | 0.32 |
+| LCB-30 (30) | 50.0% | 53.3% | +3.3pp | 0.72 |
+
+All deltas are within binomial/McNemar noise. The 2 GB size premium and 5+ LLM-call iteration cost were not justified.
+
+![Multi-benchmark comparison](benchmark_comparison_bars.png)
+
+![Summary table](summary_table.png)
+
+## Why v2 exists (research value)
+
+This model demonstrates that:
+
+1. **LLM-guided recipe search can find improvements** that a human or simple heuristic might miss — specifically, the missing L64 (nextn) tensor override and selective per-layer IQ4_NL application.
+2. **KL-divergence improvement does not always equal task improvement.** v2 is genuinely more F16-like at the logit-distribution level, but this does not translate to better code generation, comprehension, or library use on the 6 tested benchmarks.
+3. **The MoQ recipe is near-optimal for this model at this BPW range.** The kaitchup recipe is a strong local optimum; ASI-Evolve finds a slightly better KL-div point but no measurable downstream gain.
+
+### Where v2 MIGHT help (not measured)
+
+- **Math reasoning** (GSM8K, MATH) — could matter if KL-div wins translate to better arithmetic
+- **Long-form generation** (creative writing, doc generation) — rare-token accuracy might matter
+- **Multilingual tasks** — KL-div gap might be larger for non-English tokens
+- **Production chat** — subjective quality differences not captured by these benchmarks
+
+Future work should test on these dimensions before declaring v2 a clear win in any direction.
+
+## Recipe discovery method
+
+1. **Start:** kaitchup MoQ-4.0 recipe applied directly (Phase 1)
+2. **Loop:** ASI-Evolve (gpt-5.2-codex low reasoning) generates per-tensor quantization recipe variations, scored on paired PPL + KL-div against F16
+3. **Result after 20 iterations:** MoQ-4.0-AttnQSelectiveEdgeNL — 2.9% fitness improvement, 0.5% additional beyond step 1
+
+## Reproduction
+
+```bash
+# Requires the evalplus library
+pip install evalplus==0.3.1
+
+# On a 1x L40S 48GB VSI:
+python scripts/run_eval.py \
+    --benchmark humaneval_plus \
+    --model Qwopus3.6-27B-Coder-ASI-MoQ-4.0-14.4GB.gguf \
+    --n 164 --max-tokens 1024
+
+# See the full multi-benchmark runner:
+# scripts/run_all_evals.sh
+# scripts/compare_results.py
+```
+
+## Provenance
+
+- **Source model:** Qwopus3.6-27B-Coder BF16
+- **Reference:** qwopus_f16.gguf (53.8 GB)
+- **Imatrix:** qwopus_imatrix.gguf (14 MB)
+- **Base logits:** base_logits_0.dat (5.9 GB, 50 chunks × 512 ctx)
+- **Recipe:** `recipe.txt` (29 tensor-type overrides)
+- **Built:** 2026-06-23, VSI predator-vsi-05, 1x L40S 48GB
+- **Validation date:** 2026-06-24 (Phase 5 multi-benchmark test)
+- **SHA256:** `63ab13aab9c1078a782ad02f725ee69f7f3c5d398b30759c814afef5d56272fa`
+
+## Honest assessment
+
+ASI-Evolve produced a model that is **more F16-like at the logit distribution level** but **not measurably better at coding tasks**. The 2 GB cost and 5+ LLM-call iteration cost were not justified. The Pareto-optimal choice is MoQ-4.0 (smaller, equivalent).
+
+The reason this is still worth shipping as a research artifact: it is the closest we can get to BF16 quality in 14.4 GB, and may matter for tasks not captured by these benchmarks. Future work could test on additional benchmarks.
+
+## License
+
+Apache 2.0 (inherited from base model)