---

license: apache-2.0

language:

• en

base_model: TokenRhythm/NeoHorse-1-9B

pipeline_tag: text-generation

library_name: gguf

tags:

• quantization
• gguf
• llama-cpp
• imatrix
• hybrid-quantization
• MERNIK
• humaneval
• coding

---

NeoHorse-1-9B — MERNIK Quantization

MERNIK ("the one who measures") quants of

TokenRhythm/NeoHorse-1-9B:

imatrix-driven priority-queue allocation, judged by **all three columns in

synergy — PPL (canary), KLD-vs-ref (rank), scored tasks (verdict)** — never

by one alone. That synergy is the method; any single metric crowns its own

artifacts.

> Note: the Q8 / F16 / Q4 / Q2 tags in file names are for your

> convenience (build character hints) — pick by size, verify by the table

> below. All files here are MERNIK quants.

> 🧪 MERNIK protocol. This card leads with the verdict column; every

> build below carries all three — PPL (canary), KLD-vs-ref (rank), scored

> tasks (verdict) — published win or lose. Method + protocol:

> wepiqx/MERNIK.

Results (all builds, all columns)

HE = HumanEval: 164 hand-written

Python tasks, model writes code, hidden tests run it. pass@1 = solved on the

first try — strict instruction-following plus real code, no partial credit.

Official BF16 posts 98.17% (context length undisclosed in the vendor

report — our runs below are -c 8192, so treat the gap as approximate).

llama-server --jinja, temp 1.0 / top_p 0.95 / top_k 20 / presence 0.0,

max_tokens 2048, fixed seeds. PPL/KLD: `llama-perplexity -ngl 99 -c 1024

-n 64 -b 512 --seed 7`, wikitext-2-raw, KLD vs Q8-proxy (no BF16 runs).

| Build | Size | PPL | KLD vs Q8 (proxy, not BF16) | pass@1 |

|:------|-----:|:---:|:---:|:------:|

| NeoHorse-1-9B-MERNIK-6500-Q8.gguf | 6.83 GB | 7.7695 | 0.0453 | 85.98% (141/164) 👑 |

| NeoHorse-1-9B-MERNIK-6500-F16.gguf 🔭 | 6.83 GB | 7.8184 | 0.0505 | 82.32% — F16 attention core, built for distance (see below) |

| Q6_K (stock) | 7.36 GB | 7.9419 | 0.0118 | 82.32% (135/164) |

| TD-MSE (tested, not uploaded) | 6.83 GB | 7.7701 | 0.0452 | 82.32% — direction doesn't move distribution, moves code |

| TDN-MSE norms-shield (tested) | 6.83 GB | 7.7701 | — | 83.54% — shield recovers +1.2pp |

| NeoHorse-1-9B-MERNIK-5100-Q4.gguf | ~5.1 GB | 7.8012 | 0.0586 | 82.93% — Q6-class code at −2.2 GB 👑, a machine: strict instructions, working code |

| Q4_K_M (stock) | 5.62 GB | 7.7824 | 0.0875 | 76.83% (126/164) — clean rerun; our pair beats stock both ways |

| MERNIK-5100-MSE | ~5.1 GB | 7.7962 | 0.0588 | 79.88% (131/164) — clean rerun; law holds by 3pp |

| Q2_K (stock) | 3.83 GB | 90.0284 💀 | 2.6354 💀 | 0.00% — stub completions, genuine collapse |

| NeoHorse-1-9B-MERNIK-3650-Q2.gguf | 3.65 GB | 10.3586 | 0.4706 | 23.78% (39/164) — beats stock Q2_K (0%) at the same weight: at this size distribution decides everything |

The budget law: SMAPE owns small budgets (+3pp at 5100), MSE owns big

ones (+3.7pp at 6500). Same PPL both times, opposite code. Full analysis:

wepiqx/MERNIK.

> ⚡ Fast ring: GPQA-recognition (non-standard). Same builds scored by

> first-token letter likelihood (no generation, ~3 min): Neo-MSE-6500 51.5%,

> Neo-SMAPE-5100 48.0%. Rank agrees with HE here (MSE > SMAPE) but punishes

> thinkers — see MERNIK for the method and its limits.

> 🗺️ Capability map (field-tested, no mercy). 6500 writes full working

> games from scratch. 5100-SMAPE writes a complete working snake, no

> stuttering. 3650-Q2 writes correct syntax and surprisingly good Russian,

> but a whole game file is beyond it — short functions yes, projects no.

> Every weight has its jobs; the table above tells which.

> 🔮 Possible next: smape_frag. SMAPE-5100 already matches stock Q6_K

> (+1 task) at −2.2 GB. The fragility-modulated version could take the

> outright crown — but its fragility table costs serious compute. Want the

> table flipped? Say so in the discussions — community demand decides.

> >

> > *What is FRAG? Plain SMAPE aims the budget by theory (relative error).

> > FRAG first measures every tensor group: each one is briefly dropped

> > a tier and the real damage is recorded — hundreds of test builds. Groups

> > that break easily ("fragile") get boosted, tough ones ("oak") get

> > discounted. Theory aims, measurement corrects. That's why it wins — and

> > why it costs.*

> ⚠️ Sampling note. The vendor-recommended presence_penalty 1.5

> breaks thinking models on this harness (HTTP 500 peg-native format —

> the penalty corrupts the thought template and the model stalls). All runs

> here use presence_penalty 0.0, verified clean across 3×164 tasks.

>

> ⚠️ Context note. These runs used -c 8192 / max_tokens 2048, which is

> NOT enough for a thinking model: long chains of thought trip on the

> generation ceiling and the quant can't fully open up. At 16384+ the gap

> above should widen — this table is the floor, not the ceiling. Re-runs at

> larger budgets are planned and will be logged here.

Usage

llama-server -m NeoHorse-1-9B-MERNIK-6500-Q8.gguf \
  -c 16384 --jinja -ngl 99 \
  --temp 0.6 --top-p 0.95 --top-k 20

> 📝 Sampling for code: temp 0.6, top_k 20, top_p 0.95, min_p 0 —

> verified in the field (a full working snake game, Russian UI, almost no

> thinking needed). Benchmarks run at temp 1.0 per vendor protocol; real

> work wants 0.6.

> 🧪 Long context — help test. The main build keeps attention at Q8

> almost everywhere (45×Q8 + 5×Q6 of 56); the TD-SMAPE file above holds a

> 949 MiB F16 attention core (qkv layers 20–30 + 3) — built for distance.

> Neither PPL nor KLD sees past short chunks, so we have not measured this.

> If you run needle-in-haystack or long-doc tasks, report your context

> length and score — real user numbers go on this card.

Quantization and tuning by wepiqx. Built with

llama.cpp.

> ⏳ 40+ GPU-hours on a GTX 1070. Every number on this card was measured,

> not estimated — eleven HumanEval batteries plus a full PPL/KLD grid on an

> 8 GB card from 2016. If these quants serve you, a like or a test report is

> the fuel for the next round.