# VeriLoop E2 — Q8_0 Quality Card

**Release type:** high-fidelity Q8_0 GGUF  
**Baseline:** canonical VeriLoop E2 BF16 GGUF  
**Purpose:** quantify BF16 → Q8_0 distortion under a fixed llama.cpp protocol  
**Formal parent-model benchmark re-run:** no  
**Release status:** **PASS**

**Positioning:** **High-fidelity / near-reference Q8_0 tier**. This card evaluates quantization fidelity directly against BF16; it does not re-label the parent model's downstream benchmark scores as Q8-specific results.

---

## 1. Artifact Identity

| Property | Q8_0 main | Optional MTP draft |
|---|---|---|
| Filename | `VeriLoop-E2-Q8_0.gguf` | `mtp-VeriLoop-E2-Q8_0.gguf` |
| Exact bytes | 28595765600 | 3287070240 |
| Binary size | 26.632 GiB | 3.061 GiB |
| Decimal size | 28.596 GB | 3.287 GB |
| SHA256 | `see `Q8_RELEASE_MANIFEST.json`` | `see `Q8_RELEASE_MANIFEST.json`` |
| Status | **Verified** | **Verified** |

BF16 reference SHA256:

```text
11bf5defde1a256b7582bc34fd2c4a85a61615ed88e7a422dcd24d814ea6d35d
```

Release finalized:

```text
see `Q8_RELEASE_MANIFEST.json`
```

---

## 2. Quantization Build

| Item | Value |
|---|---|
| Parent model | VeriLoop E2 |
| Base family | Qwen3.8-27B |
| HF architecture | `Qwen3_5ForConditionalGeneration` |
| Quant type | `Q8_0` |
| Main tensors | 851 |
| Q8_0 tensors | 498 |
| Retained F32 tensors | 353 |
| Source model size reported by quantizer | 51,305.09 MiB |
| Quantized model size reported by quantizer | 27,260.56 MiB |
| Effective density reported by quantizer | **8.50 BPW** |
| Size reduction vs BF16 GGUF | **46.86%** |
| Quantization threads | 25 |
| Quantization time | **38.315 s** |
| Quantization return code | **0** |
| Main build gate | **PASS** |
| llama.cpp quantization/QC revision | `42916d83f4a225e56709f873aa8050ac11f5b6a4` |

The 353 F32 tensors are intentionally retained by the llama.cpp quantized layout; the release does not claim every tensor is stored as Q8_0.

---

## 3. Fixed BF16 → Q8_0 Comparison Protocol

| Item | Value |
|---|---|
| Reference model | VeriLoop E2 BF16 GGUF |
| Candidate | VeriLoop E2 Q8_0 GGUF |
| Corpus | WikiText-2 raw test |
| Context length | 2,048 |
| Chunks | 8 |
| Seed | 42 |
| GPU layers | 40 |
| Flash Attention | Off |
| KV cache | F16 / F16 |
| Batch size | 512 |
| Micro-batch size | 512 |
| Evaluation executable | `llama-perplexity` |
| Reference-logit method | BF16 logits persisted with `--kl-divergence-base` |
| Candidate comparison | Q8_0 evaluated with `--kl-divergence` against the frozen BF16 logit file |

This protocol is a **paired quantization-fidelity test**. It is not a new leaderboard campaign.

---

## 4. Quantization Fidelity

### Perplexity

| Metric | Value |
|---|---:|
| BF16 mean PPL | **4.840423 ± 0.119931** |
| Q8_0 mean PPL | **4.843536 ± 0.120062** |
| Absolute ΔPPL | **+0.003113 ± 0.003651** |
| PPL ratio | **1.000643 ± 0.000754** |
| Relative PPL increase | **+0.0643%** |
| Cor(ln PPL(Q8), ln PPL(BF16)) | **99.95%** |

### KL divergence

| Metric | Value |
|---|---:|
| Mean KLD | **0.002176 ± 0.000668** |
| Median KLD | **0.000276** |
| 90th percentile | **0.001548** |
| 95th percentile | **0.002856** |
| 99th percentile | **0.009561** |
| 99.9th percentile | **0.201704** |
| Maximum | **4.066900** |

### Token-probability stability

| Metric | Value |
|---|---:|
| Mean Δp | **−0.007 ± 0.014%** |
| RMS Δp | **1.251 ± 0.137%** |
| Same top-p | **98.815 ± 0.120%** |

### Interpretation

The measured Q8_0 distortion is small under the fixed protocol:

- predictive loss rises by **0.0643% relative to BF16**;
- average distributional divergence is **0.002176 KLD**;
- the top-probability token remains the same **98.815%** of the time;
- mean probability shift is close to zero, while RMS Δp quantifies the remaining quantization noise.

**Important:** the +0.0643% figure is a relative perplexity increase, not a universal downstream benchmark or capability-loss percentage.

---

## 5. Release Gates

| Gate | Predeclared release threshold | Measured | Result |
|---|---:|---:|---|
| PPL ratio | ≤ 1.005 | **1.000643** | **PASS** |
| Mean KLD | ≤ 0.005 | **0.002176** | **PASS** |
| Same top-p | ≥ 97.0% | **98.815%** | **PASS** |
| Main tensor count | 851 | **851** | **PASS** |
| Non-zero / structural audit | required | **PASS** | **PASS** |
| llama.cpp model load | required | **PASS** | **PASS** |
| OpenAI-compatible generation | required | **PASS** | **PASS** |
| Optional MTP loader compatibility | required for MTP artifact | **PASS** | **PASS** |
| Optional MTP speculative draft smoke | required for MTP artifact | **PASS** | **PASS** |

These thresholds are VeriLoop internal release-QA criteria and are not presented as universal quantization standards.

---

## 6. Runtime Compatibility

Validated llama.cpp revision:

```text
42916d83f4a225e56709f873aa8050ac11f5b6a4
```

Validated MTP compatibility smoke:

| Item | Value |
|---|---|
| Main model | `VeriLoop-E2-Q8_0.gguf` |
| Draft model | `mtp-VeriLoop-E2-Q8_0.gguf` |
| Context | 8,192 |
| Main GPU offload | enabled |
| Draft GPU layers | 0 |
| Model load | **PASS** |
| Generation | **PASS** |
| Draft tokens proposed | 3 |
| Draft tokens accepted | 3 |

An earlier 32K all-GPU main+draft attempt exhausted available CUDA memory while allocating the draft runtime buffer. It did **not** produce a tensor-layout or GGUF-loader error. The resource-conservative 8K main-GPU/draft-CPU configuration loaded and generated successfully.

This card therefore distinguishes **format/runtime compatibility** from **hardware-specific memory fit**.

---

## 7. Size Efficiency

| Artifact | Size |
|---|---:|
| BF16 reference GGUF | **50.113 GiB** |
| Q8_0 GGUF | **26.632 GiB** |
| Reduction | **46.86%** |

The final exact Q8_0 byte count is 28595765600 bytes.

---

## 8. Throughput Note

A standalone Q8_0 `llama-bench` run produced:

| Workload | Q8_0 |
|---|---:|
| Prompt processing (`pp512`) | **3102.722776 tok/s** |
| Token generation (`tg128`) | **41.999677 tok/s** |

The paired BF16 speed run did not complete under the original full-offload benchmark configuration, so **no BF16→Q8 speedup factor is claimed**. These Q8_0 throughput numbers are descriptive only and are not part of the quality gate.

---

## 9. Parent-Model Scores

The public VeriLoop E2 benchmark record belongs to the parent release. Q8_0 was not re-scored across the full benchmark suite for this quantization card.

That separation is intentional:

```text
Parent-model evaluation
    → establishes model capability

BF16 vs Q8_0 paired PPL/KLD/token analysis
    → establishes quantization fidelity
```

This prevents format conversion and model capability from being conflated.

---

## 10. Final Decision

```text
MAIN_Q8_BUILD=PASS
Q8_STRUCTURE_GATE=PASS
Q8_QUANTITATIVE_QUALITY_GATE=PASS
Q8_STOCK_LLAMA_CPP_COMPATIBILITY=PASS
MTP_RUNTIME_GATE=PASS
Q8_RELEASE_QUALITY_GATE=PASS
```

**Release conclusion:** Q8_0 is accepted as the high-fidelity local-deployment quantization of VeriLoop E2 under the frozen release protocol.

The strongest numerical statement supported by the evidence is:

> **Compared with the canonical BF16 GGUF under the identical fixed protocol, Q8_0 reduces file size by 46.86%, increases mean perplexity by 0.0643%, yields mean KLD 0.002176, and preserves the same top-probability token 98.815% of the time.**

No broader capability-loss percentage is inferred from these statistics.