Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF
A Llama 3.2 mixture-of-experts model (18.4B parameters from 8×3B experts) optimized for creative writing, fiction, and roleplay. Runs at 50+ tokens/sec on low-end 16GB GPU. Uncensored output. Apache 2.0 licensed, not gated. Requires explicit sampling tuning (temperature, samplers) and selection of expert count (1–8) to control output quality and variety.
Key facts
Objective fields from the source. Values we can't verify are shown as “Unknown” rather than guessed.
| Field | Value |
|---|---|
| Developer | DavidAU |
| Parameters | Unknown |
| Context window | Unknown |
| License | apache-2.0 — OSI-approved |
| Modality / task | text-generation |
| Gated on HuggingFace | No |
| Downloads | 39.7k |
| Likes | 578 |
| Last updated | 2026-04-28 |
| Source | DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF |
What Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF is
MOE architecture combining 8 Llama 3.2 3B expert models (including abliterated and uncensored variants) via Mergekit. Default 2-expert gating; user-configurable 1–8 experts. Quantized to GGUF; multiple quants available (IQ4XS tested). 128k context. Operates at bfloat16 internally; stable across sampling parameters (temp 0–5). Different expert combinations yield different outputs per generation. Requires llama.cpp-compatible inference (LMStudio, KoboldCpp, text-generation-webui, llama-server).
Run Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF locally
Load the open weights with 🤗 Transformers and generate — the same model, self-hosted.
from transformers import pipelinepipe = pipeline("text-generation", model="DavidAU/Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF")out = pipe("Explain retrieval-augmented generation in one sentence.", max_new_tokens=128)print(out[0]["generated_text"])Swap in vLLM or Ollama for production-grade serving. DEV.co can stand up the inference stack.
How you'd run it
A typical self-hosted path — open weights, an inference server, your application.
DEV.co builds each layer — from GPU infrastructure to the application.
Best use cases
Running & fine-tuning it
Estimated 8–12 GB VRAM for inference at IQ4XS quantization on low-end GPU (50+ t/s); 6–8 GB on mid-range GPU (100+ t/s estimated). Full model unquantized: ~18.4B parameters in bfloat16 ≈ 36 GB. Requires llama.cpp-compatible runtime (CPU inference possible but slow). Exact VRAM/precision trade-off requires verification with target inference framework.
Unknown. Model card does not discuss LoRA, QLoRA, or fine-tuning feasibility. Derived from base Llama 3.2 and merged via Mergekit; standard Llama 3.2 fine-tuning techniques may apply, but stability and expert routing behavior under gradient updates is not documented. Requires testing.
When to avoid it — and what to weigh
- Requirement for Consistent, Deterministic Output — MOE gating selects different expert pairs per generation even for identical prompts. Model card notes 2–4 regenerations may be needed to achieve desired quality. Not suitable if reproducibility is critical.
- Safety-Critical or Production System Requiring Content Moderation — Model is explicitly abliterated and uncensored. Model card warns of NSFW, visceral details, swearing, and light horror. Contains uncensored submodels that may interfere with safety guarantees.
- General-Purpose Chat or Instruction-Following at Scale — While model supports general use, primary design and tuning is for creative writing/roleplay. Unknown parameter count and no general-purpose benchmarks provided. Base Llama 3.2 models may be more reliable for diverse tasks.
- Production Deployment Without Hands-On Tuning — Model requires manual configuration of expert count, temperature, sampler choice (Dry, Dynamic Temp, Smooth), and quant level. No 'out-of-box' defaults stated for non-creative use cases.
License & commercial use
Apache 2.0 (Apache License 2.0). This is a permissive OSI-approved open-source license. Permits commercial use, modification, and distribution, provided the license and copyright notice are retained.
Apache 2.0 permits commercial use, modification, and redistribution. However, the model is a merge of 8 base models, including Meta Llama 3.2 (which requires acceptance of Meta's Llama 3.2 community license) and third-party uncensored/abliterated variants. Review the licenses and terms of all constituent models before commercial deployment. The Apache 2.0 license on this specific merge repo does not override upstream license obligations. Recommend legal review.
DEV.co evaluation signals
Editorial assessment — not user reviews. Directional, with an explicit confidence level.
| Signal | Assessment |
|---|---|
| Maintenance | Active |
| Documentation | Adequate |
| License clarity | Needs review |
| Deployment complexity | Moderate |
| DEV.co fit | Good |
| Assessment confidence | Medium |
Model is explicitly uncensored and contains abliterated submodels designed to bypass safety training. Output may include NSFW, visceral, or harmful content. Suitable only for contexts where such content is acceptable (private creative writing). Deployment in customer-facing or moderated systems requires additional content filtering. No adversarial robustness, prompt injection, or jailbreak evaluations provided. Treat as high-risk for safety-sensitive applications.
Alternatives to consider
Meta Llama 3.2 3B / 8B / 1B (base models)
Official, safety-aligned alternatives with broader benchmarking and production support. Slower for creative writing but more predictable and moderation-friendly.
Mistral 7B Instruct (or variants)
Widely deployed, well-documented, similar parameter range, better general-purpose instruction-following. Less specialized for creative writing but more stable.
OpenAI GPT-4 or Claude 3.5 (API)
If consistency, safety, and multi-task capability are required and local inference is not mandatory. Higher cost but reduced tuning and deployment complexity.
Ship Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF with senior software developers
Evaluate this MOE model for private fiction, roleplay, and story generation. Test quantization, expert count, and sampling parameters on your hardware. For production safety requirements or multi-task deployment, consider safety-aligned alternatives. Contact us for custom fine-tuning or integration support.
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Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF FAQ
Can I use this model for commercial applications?
What GPU do I need to run this?
Why does the output differ every time I run the same prompt?
Is this model safe for production chatbots?
Work with a software development agency
DEV.co is a software development agency delivering custom software development services to companies building on open source. Our software developers and web developers design, integrate, and ship production systems — spanning web development, APIs, AI, data, and cloud. If Llama-3.2-8X3B-MOE-Dark-Champion-Instruct-uncensored-abliterated-18.4B-GGUF is part of your open-source llms roadmap, our team can implement, customize, migrate, and maintain it.
Ready to Deploy a Creative-Writing LLM Locally?
Evaluate this MOE model for private fiction, roleplay, and story generation. Test quantization, expert count, and sampling parameters on your hardware. For production safety requirements or multi-task deployment, consider safety-aligned alternatives. Contact us for custom fine-tuning or integration support.