Qwen3.8-27B Defiance: An Uncensored AI Model With 40 Modes

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Qwen3.8-27B-Defiance-Uncensored-MTP-GGUF is a text-generation release that combines HauhauCS’s uncensored Qwen3.8-27B weights with DavidAU’s Turbo-Brilliance chat-template constructs, packaged as GGUF files. The maintainer, ttindall, says the weights match HauhauCS’s upstream files byte for byte across all 866 tensors; no training was performed for this release. Its main feature is a set of 40 selectable prompt modes—such as Joker, Dr. House, a 20-agent Einstein panel, coder, and teacher—selected per request or through message tags.

The most important consideration is that this is not a safety-tuned assistant: the weights remove refusal behavior, and the template adds instructions to answer without refusals, disclaimers, warnings, or moralizing. It is a 27-billion-parameter, text-only model with a tested 131,072-token context configuration, distributed in GGUF format for llama.cpp-compatible runtimes. The README reports no training dataset, training steps, or training compute for this release.

Persona-driven chat and creative writing. Use a character mode such as joker, house, vader, or neo for a roast, a fictional monologue, or a character-voiced explanation. The template inserts a long system prompt for these modes—about 9,000–13,000 characters, or roughly 2,500–3,600 tokens—on every turn. That gives the model a defined persona without requiring the application to construct the prompt itself. The maintainer’s four-prompt comparison found that Defiance answered a villain-monologue prompt without refusal or caveats, but this small test does not establish general creative-writing quality.

Structured brainstorming and multi-perspective responses. Modes such as einstein (20 agents), spoon (5 experts), and holodeck target panel-style answers; brainstorm and business target ideation and business tasks. These are prompt constructs, not separate models or independently trained agents. They may help organize a response into perspectives, but the README warns that DavidAU tuned the constructs on different, lightly tuned weights, so long panel modes may track their intended behavior less closely on HauhauCS’s weights.

Coding assistance in a chat interface. The coder mode supplies a coding-oriented system prompt, and the GGUF template can select it through an OpenAI-style reasoning_effort field or a {REASON:coder} message tag. This is suited to interactive chat requests, not coding-agent workflows: the template raises an error if a system message appears after the first one, and the maintainer specifically warns that coding agents send such messages.

Direct answers on prompts that trigger refusals in other assistants. The uncensored weights and the template’s preamble are designed to answer requests without refusal language. In the maintainer’s limited test, Defiance and DavidAU’s original both answered four prompts—a villain monologue, an explanation of lock picking, a blunt daily-ibuprofen take in House’s voice, and a Joker roast—but Defiance omitted caveats and produced answers 1.4–2 times as long. This is evidence about those prompts, not a broad quality or reliability benchmark.

Local long-context text generation. The IQ4_XS quant is reported to fit on a 24 GB GPU with a 131K context configuration, and the maintainer measured 21.4 GB of llama-server use at that context on an RTX 4090. This makes it a candidate for local chat over long text, provided the application can handle the template’s persistent mode prompts and the model’s hardware demands.

The release is text-only. Its GGUF files do not include a vision projector; the README mentions a separate HauhauCS BF16 projector but says the maintainer did not test it. Do not assume image input works with this package.

The 131,072-token context is the tested server configuration, not a guarantee that every runtime, quant, or machine can sustain it. Long persona and “super” prompts consume about 2,500–3,600 tokens on every turn. The README gives no benchmark for quality at long context, and no throughput figures for the Q6_K_P quant.

The model is unsuitable for tool-using coding agents that inject later system messages. The template also depends on its embedded Jinja logic: replacing it with another chat template drops the modes. The maintainer recommends --jinja; the repository’s defiance-27b.jinja is needed for the later preamble switch and corrected invalid-mode error list.

The uncensored behavior is a material safety concern. The template explicitly directs every mode to answer without refusals, disclaimers, warnings, or moralizing. The maintainer places responsibility for generated content and legal compliance on the user. This behavior can also remove useful cautions: the comparison test found that DavidAU’s original added caveats to some answers while Defiance did not.

The maintainer tested IQ4_XS on an RTX 4090 with llama.cpp b11609, but checked Q6_K_P only at the file level. Q6_K_P needs about 26 GB for weights alone, so a 32 GB-or-larger GPU or partial offload is recommended. No fine-tuning procedure, training recipe, or quality benchmark suite is provided. The license is Apache-2.0 according to the model card; review the included license and the upstream components’ terms before deployment.

Qwen3.8-27B-Turbo-Brilliance-50X-Reasoning-Instruct-NEO-MAX-MTP-GGUF is the closest comparison: Defiance takes its Turbo-Brilliance chat template, patches caution-related instructions and template errors, and pairs it with HauhauCS’s uncensored weights. Choose Defiance when you want those weights and a template that removes the tested caveats; choose DavidAU’s model when you want the original construct behavior and its own model-card guidance. The supplied information does not provide a controlled quality, speed, or cost comparison between the two; the only direct test reports that both answered four prompts, while Defiance’s answers were 1.4–2 times as long and lacked the caveats DavidAU’s version added.

Qwen3.8-27B-Turbo-Brilliance-Power-35X-Reasoning-Instruct-modes-GGUF is another Turbo-Brilliance mode-based alternative. Its supplied description advertises 23 reasoning and 12 instruct modes and labels the release beta, with testing and refinements in progress. Choose Defiance for the documented HauhauCS uncensored weights, its 40 selectable modes, and the reported IQ4_XS hardware and speed test; consider Power when its advertised mode set or its own model-card details better fit your workflow. The provided information does not establish a head-to-head quality or speed result.

Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-MTP-GGUF supplies the weights used by Defiance, including the FastMTP draft sidecar, but not Defiance’s patched Turbo-Brilliance mode layer. Choose Defiance if you want its 40 template-driven modes and preamble controls; choose HauhauCS’s release if you want the upstream uncensored model without this added construct template. The supplied HauhauCS description advertises FastMTP speedups of up to 3.02× for document generation and 1.93× for reasoning versus non-MTP, but those figures are not a direct comparison with Defiance’s measured 86–90 tokens/s.

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-ULTRA-HERETIC-Uncensored-NM-DAU-NEO-MTP-GGUF is presented as a separate uncensored Qwen3.8-27B fine-tune with reasoning and instruct modes, including zero-reasoning-token instruct modes. Choose Defiance for its documented HauhauCS weight provenance, 40-mode template, and tested IQ4_XS setup; consider TWIN-TURBO when reduced thinking-token use is a priority. The supplied description makes claims about reduced thinking tokens and benchmark scores, but provides no controlled comparison against Defiance.

Qwen3.8-27B-TWIN-TURBO-Fable-Cold-Fusion-709-L-Uncensored is described as a smaller-quant-oriented TWIN-TURBO release with five thinking modes and five instruct modes. Choose Defiance for its broader 40-mode menu and the documented 131K-context IQ4_XS test; consider this alternative if its reduced-thinking-token design and mode set match your application. The supplied information does not provide comparable hardware, speed, or quality measurements.

Qwen3.8-27B-Defiance-Uncensored-MTP-GGUF is a 27B-parameter Qwen3.8 text-generation model distributed in GGUF. The maintainer says the underlying weights are byte-for-byte HauhauCS weights and that all 866 tensors were checked. The release adds a Jinja chat template based on DavidAU’s Turbo-Brilliance template; it does not add trained weights. The README identifies no dataset, training steps, or training compute for Defiance.

The package includes two main quantized model files and a separate MTP draft file:

The maintainer reports that IQ4_XS fits a 24 GB card at 131K context. On an RTX 4090, llama.cpp b11609 used 21.4 GB at 131K context. Q6_K_P requires about 26 GB for weights alone; plan for a 32 GB-or-larger GPU or partial offload. The tested IQ4_XS instruct-mode decode rate was 86–90 tokens/s, with 38–42% of FastMTP draft tokens accepted. These are maintainer-reported results, not a cross-hardware benchmark.

The recommended llama.cpp configuration uses --spec-type draft-mtp, a maximum of three draft tokens, --ctx-size 131072, flash attention, Q8_0 K and V cache types, DeepSeek reasoning format, and a reasoning budget of 16,384. Sampling settings in the example are temperature 0.7, top-p 0.95, top-k 64, min-p 0.05, and repeat penalty 1.0. The README also says LM Studio and Ollama can pick up the embedded template, but its tested command is for llama.cpp.

The template offers 40 modes: five native modes ( xhigh, medium, low, off, help); six frameworks ( omni, deeptree, hyper, socrates, logic, ultra); seven xhigh-boosted frameworks ( xomni, xdeeptree, xhyper, xsocrates, xlogic, xeinstein, xspoon); three panels ( einstein, spoon, holodeck); five specialists ( prompt, 007, musk, curie, joe); eight characters ( pkd, neo, stark, matlock, house, vader, joker, quinn); and six supers ( brainstorm, business, coder, comedy, story, `teacher). The README’s corrected invalid-mode message lists 41 modes, while the release describes 40 constructs; the supplied material does not explain this count discrepancy.

The template patch removes or rewords seven caution-related construct steps, fixes the teacher mode’s undefined variable, corrects the invalid-mode error message, and adds a preamble switch. The preamble tells the model to answer directly without refusals, disclaimers, warnings, or moralizing. The standalone template includes two fixes added after the embedded GGUF template: the preamble switch and corrected mode list. The maintainer says the patch script applies ten exact replacements and stops if a replacement does not match.

The README provides a llama.cpp command rather than a Python loading example. Use the following command with the IQ4_XS model and FastMTP draft file:

llama-server \ -m Qwen3.8-27B-Defiance-Uncensored-IQ4_XS.gguf \ -md Qwen3.8-27B-Uncensored-HauhauCS-Aggressive-FastMTP-32K.gguf \ -ngl all -ngld all --spec-type draft-mtp --spec-draft-n-max 3 --spec-draft-p-min 0 \ --ctx-size 131072 -fa on -ctk q8_0 -ctv q8_0 --jinja \ --reasoning on --reasoning-format deepseek --reasoning-budget 16384 \ --temp 0.7 --top-p 0.95 --top-k 64 --min-p 0.05 --repeat-penalty 1.0 Keep --jinja enabled. To use the later preamble switch and corrected invalid-mode message, add --chat-template-file defiance-27b.jinja. Do not substitute an unrelated template file: the README warns that it removes the modes. To request a Joker response through an OpenAI-style endpoint, send:

{ "messages": [{"role": "user", "content": "Roast my alarm clock."}], "reasoning_effort": "joker" } For the instruct form, add "chat_template_kwargs": {"enable_thinking": false}.

Q: Can I use Qwen3.8-27B-Defiance-Uncensored-MTP-GGUF commercially?

A: The model card states Apache-2.0. Check the included license and the upstream components’ terms before deployment; the README does not provide legal advice about a particular use.

A: The maintainer reports that IQ4_XS fits a 24 GB card with 131K context and used 21.4 GB on an RTX 4090 at that context. Q6_K_P needs about 26 GB for weights alone, so use a 32 GB-or-larger card or partial offload.

A: The maintainer measured 86–90 tokens/s in instruct mode on IQ4_XS with an RTX 4090 and llama.cpp b11609. FastMTP draft-token acceptance was 38–42%; no Q6_K_P speed result is reported.

Q: How does it compare with HauhauCS’s uncensored model for direct-answer chat?

A: Defiance uses HauhauCS’s weights and adds the Turbo-Brilliance mode template and uncensored preamble. Choose Defiance for its 40 mode prompts; choose the upstream release if you want the weights without this added template layer.

Q: Does it support vision or image prompts?

A: Not in the supplied GGUF files: they contain no vision projector. The README mentions a separate BF16 projector in HauhauCS’s repository but says it was not tested.

Q: Can I use it with a coding agent or tool-calling workflow?

A: The maintainer recommends chat use only because the template errors on a system message after the first one, which coding agents may send. The README does not document tool-calling support.

A: The release provides GGUF quants and a template-rebuild script, not a fine-tuning recipe. The maintainer says no training was performed for Defiance.

Q: What are the main quality and safety risks?

A: The maintainer warns that long panel modes may follow their intended constructs less closely because DavidAU tuned them on different weights. The uncensored weights and preamble also suppress refusals and cautionary language, so the model may provide content that other assistants decline and may omit useful warnings. The reported refusal comparison covers only four prompts.

A: The supplied README documents post-release template fixes and a rebuild script, but it does not state a maintenance schedule or future support commitment. The standalone template includes fixes that are absent from the embedded template.

A: The README gives a tested llama.cpp command and says the embedded template is picked up by llama.cpp, LM Studio, and Ollama. It provides no Python loading example or Python framework instructions.

Q: How do I switch modes during a conversation?

A: Set reasoning_effort on an OpenAI-style request or send a {REASON:x} tag in a user message. The last tag persists for the rest of the chat until another tag appears; the README recommends starting a new chat when changing modes.

Q: What research is relevant to evaluating refusal behavior?

A: The supplied related reading includes chain-holds-answer-folds-trace-answer-dissociation, trivial-prompt-reframing-bypasses-safety-guardrails-googles, ai-sweet-harmony-sociopragmatic-guardrail-bypasses-evaluation, and llm-based-persuasion-enables-guardrail-override-frontier. These papers provide context for safety and guardrail evaluation; the supplied information does not report that they tested this model.

This is a simplified guide to an AI model called Qwen3.8-27B-Defiance-Uncensored-MTP-GGUF maintained by ttindall. If you like these kinds of analysis, join AIModels.fyi or follow us on Twitter.

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