---

base_model: Qwen/Qwen3.8-Flash-Next

base_model_relation: quantized

license: apache-2.0

library_name: llama.cpp

pipeline_tag: text-generation

quantized_by: spiritfather

tags:

• gguf
• imatrix
• creative-writing
• llama.cpp

---

cyberneurova-Qwen3.8-Flash-Next — GGUF (imatrix (i1) quants)

<p align="center">

🧬 Source model: <a href="https://huggingface.co/cyberneurova/cyberneurova-Qwen3.8-Flash-Next" target="_blank">cyberneurova/cyberneurova-Qwen3.8-Flash-Next</a>

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📦 Static quants: <a href="https://huggingface.co/spiritfather/cyberneurova-Qwen3.8-Flash-Next-GGUF" target="_blank">spiritfather/cyberneurova-Qwen3.8-Flash-Next-GGUF</a>

<br>

🛠️ Runs on <a href="https://github.com/ggml-org/llama.cpp" target="_blank">llama.cpp</a> (master)

<br>

📊 Scored on <a href="https://caliperbench.com" target="_blank">CaliperBench</a>

<br>

💬 <a href="https://discord.gg/rhE9zdKyE4" target="_blank">Discord</a>

</p>

> [!Note]

> All credit for the model goes to cyberneurova, who produced cyberneurova-Qwen3.8-Flash-Next. These are quantizations only.

> [!Tip]

> Benchmarked on CaliperBench — a creative-writing benchmark scoring prose craft, roleplay and willingness rather than general intelligence. See this model's scores: caliperbench.com

> [!Important]

> Requantized with PLE (per-layer expert) mixed precision. The IQ2_M / IQ3_XXS / IQ3_M / IQ4_XS quants have been rebuilt with mixed quantization: trunk (attention, shared experts, embeddings) stays at Q8_0 while routed expert FFN tensors are quantized per-layer — up/gate projections at the named precision, down projections at IQ4_NL. This gives significantly better quality-per-byte than flat imatrix quants on MoE architectures. Each rung pairs with the external MTP draft model from the static repo. IQ4_XS is near-lossless (+0.28% PPL vs Q8_0).

> This is an uncensored/unrestricted version of Qwen3.8-Flash-Next. Only the refusal behavior is removed; general model capabilities are preserved. IQ1/IQ2 quants are not available for this architecture (qwen4exp hyper-connection tensors are not covered by the imatrix calibration). Use the static Q2_K from the static repo as the lowest available quant.

> imatrix (importance matrix) computed over bartowski calibration_datav3 on this model's own bf16. Weighted quants spend their bits where the calibration text says the model is sensitive; they are usually preferable to a static quant of the same size below Q5.

PLE mixed quants (new)

Requantized from bf16 with per-layer expert mixed precision. Trunk at Q8_0; routed expert tensors overridden per rung. All rungs use the same external MTP draft model (Q8_0) from the static repo.

| Quant | Size | Mixture (default / up / gate / down) | PLE | MTP pair | PPL (wikitext-2, ctx 512) | vs Q8_0 | KLD vs Q8_0 |

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

| IQ2_M | 100.9 GiB | Q8_0 / IQ2_S / IQ2_S / IQ4_NL | Q8_0 | ✓ | 5.1252 ± 0.032 | +5.97% | 0.150323 ± 0.000859 |

| IQ3_XXS | 105.6 GiB | Q8_0 / IQ3_XXS / IQ3_XXS / IQ4_NL | Q8_0 | ✓ | 4.9843 ± 0.031 | +3.05% | 0.093681 ± 0.000580 |

| IQ3_M | 109.1 GiB | Q8_0 / IQ3_S / IQ3_S / IQ4_NL | Q8_0 | ✓ | 4.9869 ± 0.031 | +3.11% | 0.072916 ± 0.000464 |

| IQ4_XS | 116.7 GiB | Q8_0 / IQ4_XS / IQ4_XS / IQ4_NL | Q8_0 | ✓ | 4.8502 ± 0.030 | +0.28% | 0.039888 ± 0.000269 |

Q8_0 baseline PPL: 4.8366 ± 0.030. Trunk (non-expert Q8_0): 55.8 GiB constant across all rungs.

Flat imatrix quants

| Quant | Size | Notes |

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

| Q2_K | 80.4 GB | |

| IQ3_S | 88.9 GB | |

| Q3_K_S | 88.8 GB | |

| Q3_K_M | 94.2 GB | IQ3_S probably better |

| Q3_K_L | 96.6 GB | |

| Q4_K_S | 106.2 GB | optimal size/speed/quality |

| Q4_K_M | 119.2 GB | fast, recommended |

| Q5_K_S | 127.8 GB | |

| Q5_K_M | 134.1 GB | |

| Q6_K | 167.6 GB | practically like static Q6_K |

Loading (any current llama.cpp)

llama-server -m cyberneurova-Qwen3.8-Flash-Next-IQ4_XS.gguf -ngl 99 -fa on -c 32768 -ctk q8_0 -ctv q8_0 --jinja