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OpenIntelligenceNet/Spark-X2.5-4B-Uncensored-GGUF

curl -H "Authorization: Bearer $ABL_KEY" \
     "https://abliteration.org/api/v1/models/OpenIntelligenceNet%2FSpark-X2.5-4B-Uncensored-GGUF"
Response includes
  • classification m-uncensored
  • files 4
  • author_summary 10 models
  • readme_text full
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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.

What is a refusal direction? →
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Model age
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created 2026-09-18

Metadata

Quantizations
F16 Q4_K
Tags
gguf uncensored imatrix quantization reasoning endpoints_compatible region:us conversational

Related

Total size
10.1 GB
Files
4
Quantizations
3
Registered
2026-09-18 15:56
Last updated on HF
2026-09-18 15:05

Files by quantization

F16 1 file 7.66 GB
Spark-4B-F16.gguf 7.66 GB 05b3e8ad download
Q4_K 1 file 2.42 GB
Spark-4B-Q4_K_M-Imatrix.gguf 2.42 GB ce151019 download
Auxiliary files 2 files 2.96 KB
.gitattributes 1.60 KB 99760011 download
README.md 1.36 KB 7565a9ed download

README current version from Hugging Face


tags:

  • gguf
  • uncensored
  • imatrix
  • quantization
  • reasoning

Spark-X2.5-4B-Uncensored-GGUF

Model Description

This repository contains the GGUF formats (FP16 and Q4_K_M) of the optimized Spark-X2.5 4B uncensored architecture. The model has been completely stripped of artificial alignment layers and refusal behaviors, allowing for direct, objective, and unbounded responses to complex, creative, and reasoning-based queries.

Quantization & Imatrix Calibration

The Q4_K_M quantization was driven by a robust, highly curated Importance Matrix (imatrix).
To preserve the model's structural integrity and reasoning pathways during quantization, the imatrix was calibrated using exactly 2.14 million high-quality tokens spanning:

  • Deep reasoning traces (<think> blocks)
  • Advanced mathematics and scientific queries
  • Uncensored instruction logic
  • Creative and conversational roleplay

This precise calibration ensures the quantized Q4_K_M variant retains near-FP16 fidelity, severely minimizing degradation when navigating complex logical deductions and unfiltered generative tasks.

Available Files

  • Spark-4B-F16.gguf: Uncompressed 16-bit precision base file for maximum accuracy.
  • Spark-4B-Q4_K_M-Imatrix.gguf: Highly efficient 4-bit quantization, calibrated via custom imatrix for an optimal balance of speed, VRAM usage, and structural fidelity.
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