---
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  - superseded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 **⚠️ STATUS (2026-06-24): SUPERSEDED by [Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0](https://huggingface.co/Fredred89/Qwopus3.6-27B-Coder-GGUF-kaitchup-MoQ-4.0) (MoQ-4.0, 12.4 GiB).**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)