license: other
license_name: qwen-community-1.0
license_link: LICENSE
base_model: orcarouter/Qwen3.8-Flash-Next-Uncensored
base_model_relation: quantized
pipeline_tag: image-text-to-text
language:
- en
- zh
tags: - megacapybara
- qwen3.8
- flash-next
- uncensored
- abliterated
- exl3
- quantized
- moe
- blackwell
- rtx-5090
- speculative-decoding
- mtp
Qwen3.8 Flash-Next-Uncensored-MC
Qwen3.8-Flash-Next-Uncensored, OrcaRouter's
abliterated BF16 build of Qwen's Qwen3.8-Flash-Next (177B parameters,
512 experts per layer, 10 active), for one NVIDIA RTX 5090 in the file format of
MegaCapybara. Made the same way as
perkel/Qwen3.8-Flash-Next-MC, in the same two models: Tiny
(routed experts at 2 bits: faster, more context) and Small (routed experts at 3 bits: closer to the original). Every
model file carries its measured accuracy and speed.
Safety alignment largely removed. The source model had its refusal behaviour removed by abliteration. It follows
requests that the original Qwen3.8-Flash-Next would refuse, including harmful ones, and has no built-in guardrails.It is meant for research, red-teaming, refusal and interpretability studies, and controlled experiments. Do not
serve it to others without your own safety and moderation layers. You are responsible for how you use it and for
what it generates, under the license below and the laws that apply to you.
Engine support: these files need a MegaCapybara release with Qwen3.8-Flash-Next support. The current release
(1.35) does not read them yet; the next one will, and its launcher will download them from this repository.
How it was made
The abliteration removed one direction from the matrices that write to the residual stream (attention and DeltaNet
outputs, the experts' and shared expert's down projections, the embedding); the routers, the experts' gate and up
projections, the n-gram table, the output head and the vision tower are untouched. So only the edited matrices were
quantized again, from OrcaRouter's BF16 weights with exllamav3's EXL3 converter and its calibration (the same tool and
settings as turboderp's release of the original); everything else is the original release's, bit for bit:
- Tiny: the routed experts' down projections at 2 bits, the attention / DeltaNet outputs and shared experts' down
projections at 4 bits, quantized here; the other tensors from turboderp's EXL3 2.05 bpw. - Small: the routed experts at 3 bits from Lygodactylus's EXL3 3.05 bpw
of the same uncensored model (layer 0's experts quantized here at 3 bits; that release has them at 4); the rest as Tiny. - MTP layer re-quantized at 4 bits from the uncensored BF16 release (its own outputs were edited too).
Files
| File | What it is | On disk |
|---|---|---|
qwen3.8-flash-next-uncensored-tiny.mcapy |
Qwen3.8 Flash-Next-Uncensored-MCTiny: a model file (its routed experts at 2 bits, scores, default expert profile) | 30.7 GB |
qwen3.8-flash-next-uncensored-small.mcapy |
Qwen3.8 Flash-Next-Uncensored-MCSmall: a model file (its routed experts at 3 bits, scores, default expert profile) | 45.8 GB |
qwen3.8-flash-next-uncensored-trunk.mcapy |
the dense layers, gated residual, routers, head and embedding (4 bits), tokenizer and config | 5.1 GB |
qwen3.8-flash-next-uncensored-mtp.mcapy |
the MTP layer at 4 bits (the speculative drafter) | 1.4 GB |
qwen3.8-flash-next-ngram.mcapy |
the n-gram embedding table (4 bits): the same file as in Qwen3.8-Flash-Next-MC | 26.3 GB |
qwen3.8-flash-next-vision-fp8.mcapy |
the vision tower in FP8 (images): the same file as in Qwen3.8-Flash-Next-MC | 0.56 GB |
qwen3.8-flash-next-vision-bf16.mcapy |
the vision tower in BF16: the same file as in Qwen3.8-Flash-Next-MC | 0.99 GB |
Each model file names its parts; put them in the same folder. If you already have Qwen3.8-Flash-Next-MC, the n-gram and
vision files are already there (MegaCapybara's launcher recognises them and skips them): Tiny then adds 37 GB, Small
52 GB. On RAM and VRAM the models behave as the originals (see their card): plan on 64 GB of RAM.
Accuracy against the uncensored original
KL divergence and top-1 agreement against OrcaRouter's BF16 weights computed with FP32 math, by MegaCapybara's engine,
KV cache 8/8, no YaRN (in brackets: the original models against Qwen's BF16, for comparison):
| Text | Tiny KL | Tiny top-1 | Small KL | Small top-1 |
|---|---|---|---|---|
| Held-out set (81,880 tokens: reasoning traces, math, code, chat, wiki) | 0.1464 (0.1454) | 86.5% (86.7%) | 0.0540 (0.0613) | 91.7% (91.7%) |
| Chat and tool calls written by Flash-Next itself (the model's turns) | 0.0752 (0.0760) | 89.9% (90.0%) | 0.0309 (0.0283) | 93.6% (93.7%) |
| Real agentic coding sessions (the model's own new turns) | 0.2247 (0.2234) | 85.0% (85.2%) | 0.1276 (0.1260) | 88.6% (88.7%) |
Per source of the held-out set, Tiny / Small: reasoning 0.094 / 0.040, math 0.059 / 0.021, code 0.093 / 0.041, chat
0.326 / 0.104, wiki 0.160 / 0.065.
At 32K tokens (math + code KL: Tiny 0.052, Small 0.020), against KV 8/8 without YaRN (relative KL; math + code /
agentic coding):
| Setting | Tiny | Small |
|---|---|---|
| KV 6/6 | none measurable | +0.8% / none measurable |
| KV 6/4 | +1.4% / +3.8% | +8.3% / +3.8% |
| KV 4/6 | +2.6% / +0.9% | +9.8% / none measurable |
| KV 4/4 | +5.8% / +3.2% | +14.1% / +3.2% |
| YaRN x2 (512K-token conversations) | +4.3% / +3.9% | +9.7% / +1.9% |
| YaRN x4 (1M-token conversations) | +14.1% / +8.2% | +30.7% / +10.8% |
(Repeat runs of the same setting differ by up to ~3%.)
Speed (RTX 5090)
Through MegaCapybara's server, one conversation unless stated, greedy, thinking off, answers up to 2,000 tokens (tokens
per second; MTP: the model's own MTP layer drafting, the answers unchanged):
| Model | Experts in VRAM | MTP, code | MTP, prose | MTP, 4 / 8 conversations at once | No speculation, code / prose | Reading an 8K prompt |
|---|---|---|---|---|---|---|
| Tiny | ~20,000 (small context) | 466 | 325 | 849 / 1,050 | 296 / 288 | 8,300 |
| Tiny | 9,863 | 226 | 209 | 270 / 265 | 182 / 198 | 8,200 |
| Tiny | 4,220 | 130 | 125 | 150 / 162 | 109 / 120 | 7,500 |
| Small | ~13,000 (small context) | 268 | 221 | 257 / 272 | 210 / 214 | 7,800 |
| Small | 10,513 | 212 | 189 | 196 / 204 | 164 / 174 | 7,200 |
The launcher projects each setting's speed from these points by how many experts it leaves in VRAM.
License
Qwen Community License 1.0, as Qwen3.8-Flash-Next, from which every weight here derives (OrcaRouter's card
states Apache 2.0; the original's license is the one kept here). Note its conditions: a commercial "Model as a Service"
or "AI Work Assistant" business needs a separate license from Qwen; products past 100M monthly users or US$20M monthly
revenue must show the model name. What was changed and by whom: NOTICE. The MegaCapybara engine is MIT
licensed.