Qwen3.8-27B-Unleashed-GGUF

Model overview

> ### ✅ All 9 tiers verified and live (2026-08-21) > > `UD-IQ1_M` and `UD-IQ2_S` were briefly published broken, then withdrawn, and are now > **rebuilt, load-tested, and back**. If you downloaded either before 2026-08-21 22:00 UTC, > re-download. No other tier was ever affected. > > **If you are quantizing this model yourself, this is the part worth reading.** Upstream > builds low-bit tiers with a *different architecture*, not just different bit widths: > > | | Q3_K_XL and above | IQ1_M / IQ2_S | > |---|---|---| > | tensors | 506 | 498 | > | highest block | 64 (MTP head at `q6_k`) | 63 (MTP pruned) | > | `block_count` | 65 | **64** | > | `nextn_predict_layers` | 1 | **0** | > > Their imatrix has 496 entries because it matches that pruned topology. Quantizing a > 65-block f16 against it fails with > `Missing importance matrix for tensor blk.64.attn_k.weight`. > > `--prune-layers 64` alone is **not** enough — it removes the tensors but leaves the model > declaring an MTP head, so loading dies on `blk.63.nextn.eh_proj.weight not found`: a > structurally valid file containing an unloadable model. You also have to rewrite the > metadata: > > > > Every file here is now gated on exit code → magic bytes → **an actual load test** before > upload. Header checks alone let two broken builds through; only loading the model catches it. Most uncensored GGUFs are uniform quants — every layer gets the same treatment. These use a **per-tensor type map**, so sensitive tensors keep precision while tolerant ones compress hard. The result: a **Q3 that outperforms a Q4 3.3 GB larger**, with full 250k-token retrieval. > **Provenance, stated plainly.** The per-tensor quant map was **extracted from > unsloth/Qwen3.8-27B-GGUF** > (Unsloth Dynamic 3.0) by reading the > tensor-type table from their published files, then applied to > JonathanColetti/Qwen3.8-27B-Uncensored > weights with that repo's imatrix. **The recipe is Unsloth's work, not ours** — we did not > independently derive it. All upstream projects are Apache 2.0. See Credits.

Key features

特点摘要待依据来源补齐，不凭模型名称推断能力。

Limitations & considerations

Sources are insufficient to establish specific limitations. Verify task performance, latency, data policies, licensing and version lifecycle.

Source prices & cost analysis

Prices vary by channel, region and configuration. Channel quotes are listed independently and are not treated as a single official provider price.

Public benchmarks & run configuration

暂无已采集的可核验公开评分。模型未上榜不代表能力较低。

Frequently asked questions

Sources & traceability

许可证：apache-2.0。模型介绍与第三方评测均以原来源的具体版本和方法为准。