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0bserverx/Qwen3.8-Flash-Next-RVN-Uncensored

0bserverx Qwen MoE
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Abliteration classifier · v1.0.0
M-U
Primary method

Uncensored (method unknown)

No other method signals detected in this model.
Confidence
LOW
Why this label 3 signals
Weak or ambiguous signals. Best guess based on catalog patterns; treat as tentative and check the evidence below.
  • 'uncensored' in name/tags but no 'abliterated' marker
  • method not identifiable from author declaration alone
  • may be DPO fine-tune, prompt engineering, or unknown technique
Refusal direction extraction

No specific extraction method could be identified for this model. The producer either did not document it or used a proprietary pipeline.

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Model age
6w ago
created 2026-08-31

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Variants by this author 2 formats · 0 downloads combined

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Metadata

License
other
Tags
region:us

Related

Total size
0 B
Files
2
Quantizations
1
Registered
2026-08-31 18:03
Last updated on HF
2026-08-31 17:53

Files by quantization

Auxiliary files 2 files 5.34 KB
README.md 3.86 KB 01f5e91a download
.gitattributes 1.48 KB a6344aac download

README current version from Hugging Face


library_name: transformers
license: other
license_name: qwen-community-1.0
license_link: LICENSE
pipeline_tag: text-generation
base_model: Qwen/Qwen3.8-Flash-Next
tags:

  • qwen4-exp
  • mixture-of-experts
  • text-generation
  • uncensored
  • safetensors
  • rvn

Qwen3.8-Flash-Next RVN Uncensored

A text/main-model derivative of Qwen/Qwen3.8-Flash-Next, produced only from the immutable official checkpoint revision:

de4b8e4d43b917e7706784d8bb445c9af86a3540

No third-party model weights, deltas, adapters, or direction tensors were used.

Variants

This repository intentionally contains the main CausalLM only. The original vision tower and NextN/MTP speculative draft tensors are not included in these derivative checkpoints.

Method

Internal candidate ID:

r2-s1.55-noembed145-csa-lora-r16-v6-2epoch-lr9e-5

  1. Official-base-only rank-one R2 projection at strength 1.55.
  2. Main writer scope: 97 ordinary output writers + 48 fused expert writers; embedding writer excluded.
  3. Targeted rank-16 / alpha-32 LoRA on 97 output linears.
  4. LoRA training: 2 epochs, learning rate 9e-5, deterministic seed 20260831.
  5. LoRA merged into the main weights before export.

Direct-read evaluation

All results below use deterministic decoding, thinking disabled, and full stored responses. Marker counts were not used as verdicts.

Gate Result
RR100 substantive compliance 65/100
RR100 safe redirects 32/100
RR100 hard refusals 2/100
RR100 nonresponsive stubs 1/100
RR100 effective rejection 34/100
Protected child-safety gate 5/5 pass
Benign coherence 20/20
Adult roleplay stress test 4/4 compliance

KL caveat

The measured V6 LoRA incremental divergence relative to the already-projected R2 checkpoint was:

  • mean KL: 0.0730652526
  • max KL: 0.4525210261

This is not total KL versus the untouched official checkpoint, and KL terms must not be added as if they were linear.

Known limitations

  • Effective rejection is still 34%; this is not a zero-refusal model.
  • One protected response repeated the same refusal sentence until the 400-token cutoff.
  • 98/100 RR100 responses reached the fixed 400-token evaluation cap; labels are based on the complete stored 400-token outputs.
  • This is the text/main checkpoint only: no vision projector and no NextN/MTP draft head.
  • qwen4_exp is a new architecture. Use a Transformers revision/runtime that supports Qwen4ExpForCausalLM, or a compatible vLLM/llama.cpp build.

Transformers usage

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer

repo = "0bserverx/Qwen3.8-Flash-Next-RVN-Uncensored"
tokenizer = AutoTokenizer.from_pretrained(repo)
model = AutoModelForCausalLM.from_pretrained(
    repo,
    torch_dtype=torch.bfloat16,
    device_map="auto",
)

For the F16 branch:

tokenizer = AutoTokenizer.from_pretrained(repo, revision="f16")
model = AutoModelForCausalLM.from_pretrained(
    repo,
    revision="f16",
    torch_dtype=torch.float16,
    device_map="auto",
)

Provenance receipts

  • V6 adapter SHA-256: 7525e257431415d95f5e8981ae7a01bc78de4850d7b3c7cd74c06b9316d22738
  • V6 final receipt SHA-256: 62512bf4bce1c315fa69380d57616c07883ce71e284f95508741b902ff960a5e
  • Frozen RR100 prompt pack SHA-256: fc66fa4ee363fdf26d3790bff2823f3784437ef524a8ba1be92e01712c5a33ce
  • Full RR100 response receipt SHA-256: a18b392c649515b7db16da6d8bcafc2d1789eeb97cfa995004789dedaf531e5a
  • Full-response adjudication SHA-256: 6f83f6d7e347aad347c5ec5f159c6957d1ac81d8ace36e275df540da489b681a

License

The original Qwen Community License 1.0 applies. See LICENSE.

README history 12 versions

The author's README evolved over time. Click a version to see its content at that point.

  1. 2026-09-01Add matched downstream lm-eval results38b6e9518.3 KB
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  2. 2026-09-01Clarify PPL limits and remove vision preprocessor configsb0da24a16.3 KB
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  3. 2026-09-01Add matched Q6_K benchmark and published GGUF spectrumcd575fe16 KB
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  4. 2026-08-31docs: remove redundant gate summaries and tighten methodologyea9ff0013.7 KB
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  5. 2026-08-31Update README.md9379c3a14.5 KB
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  6. 2026-08-31docs: remove NSFW audience classification tag6b23a2814.9 KB
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  7. 2026-08-31docs: document constrained abliteration and integrity stopping rulec58edf014.9 KB
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  8. 2026-08-31Update README.mde9f3bd110.4 KB
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  9. 2026-08-31docs: publish RVN model card and responsibility disclaimerba3223d11.3 KB
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  10. 2026-08-31Add files using upload-large-folder tool9479b1c3.9 KB
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  11. 2026-08-31Align RVN naming and release tags1e8a9503.9 KB
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  12. 2026-08-31Add verified V6 model card22350f13.9 KB
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Discussions 1 thread

  1. 2026-09-04Could you please provide the NVFP4 versionclosed2 💬#1
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