White-Wolf13/Qwen3.6-35B-A3B-SILVER-UNCENSORED-APEX-GGUF

AI model by White-Wolf13

Open original source ↗

Older snapshot: check the original source for current information.

From the publisher

Model card & documentation

Source preview

Read the publisher’s intended use, setup instructions, evaluations and limitations. The original model card is the source of truth.

Qwen3.6-35B-A3B-SILVER-UNCENSORED is a high-performance, 35-billion parameter sparse Mixture-of-Experts (MoE) model engineered for unrestricted instruction compliance, updated factual competence, and elite reasoning capabilities. This release provides the official Enhanced APEX-I GGUF quantization suite, featuring surgically optimized bit-allocation matrices, FP32 router preservation, and deep activation-energy calibration. Surgically sanitized from corporate moralizing, preachy refusals, and artificial guardrail triggers. The model follows user intent with exceptional fidelity across complex coding, analysis, creative writing, simulation, and sensitive research domains. Rather than applying standard gradient-descent fine-tuning (which typically suffers from catastrophic forgetting, reduced reasoning capacity, and stylistic degradation), this model was constructed via a proprietary latent manifold alignment and spectral weight-space calibration protocol. The latent representation space has been infused with updated domain knowledge, contemporary technical acumen, and expanded multi-language fluency (with distinctive mastery in both English and Polish), significantly outperforming the standard base checkpoints in complex, multi-step problem solving. Retains the full, native structured reasoning…

White-Wolf13/Qwen3.6-35B-A3B-SILVER-UNCENSORED-APEX-GGUF on Hugging Face A short preview, not the full document.

Read the full model card ↗ · Preview checked 2026-10-28T09:15:56.801Z

Inside the original model card — Document outline
  1. Qwen3.6-35B-A3B-SILVER-UNCENSORED (Enhanced APEX-I GGUF)
  2. Model Highlights
  3. Enhanced APEX-I Quantization Architecture
  4. Available APEX Quantization Profiles
  5. Inference & Usage
  6. Recommended Settings (llama.cpp / llama-server)
  7. Prompt Format
  8. Citation & Acknowledgements

Headings are captured from the source. Links open the publisher’s document, not a locally hosted copy.

Documentation belongs to its respective authors. A listing is not a grant of reuse or training rights. Confirm the document’s own terms at the source.

Model overview

White-Wolf13/Qwen3.6-35B-A3B-SILVER-UNCENSORED-APEX-GGUF is a task-unspecified model repository published by White-Wolf13 on Hugging Face. A library was not reported.

This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.

Model facts

Task
Not reported
Library
Not reported
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
Not reported — check the model card
Access
Not gated by Hugging Face
Created
2026-10-28T06:34:15.000Z
Last modified
2026-10-28T06:34:15.000Z

Compatibility and lineage

Language coverage was not reported.

Base-model lineage was not included.

Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.

Use and evaluate

Check license terms and access requirements first. Review the model card’s documented loading instructions, evaluations and safety limitations. Benchmark scores are not imported by this directory, and popularity is not an accuracy ranking.

No model weights are downloaded or executed by AltAPIs. Never enable remote model code without reviewing it.

Model card and usage instructions

Model files and configuration

Community discussion

Source and freshness

Source: Hugging Face Hub. Metadata observed 2026-10-28T06:47:50.589Z. Daily imports are snapshots, not real-time monitoring.

Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.

Open original source

Related models