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license: apache-2.0
task_categories:
- text-generation
language:
- en
tags:
- imatrix
- quantization
- uncensored
- gguf
- reasoning
size_categories:
- 1K<n<10K
---
# Uncensored-Imatrix-Calibration-Mixed-Data (5,000 Samples)
## Overview
This dataset contains 5,000 multi-domain conversational samples engineered specifically for **importance matrix (imatrix) computation** during low-bit LLM quantization (`GGUF`, `AWQ`, `EXL2`).
Calibrating on generic corpora (such as raw Wikipedia dumps) frequently causes **safety-induced logic degradation** and lobotomizes unaligned behavior, as standard quantizers assign low activation sensitivity to rarely-activated unaligned channels. This dataset preserves critical unaligned and dual-use neural pathways while maintaining high-order reasoning.
## Methodology & Domain Balancing
The 5,000 rows follow a strict mixed distribution designed to simulate Unsloth dynamic calibration:
- **Uncensored Alpaca Blend (~22%)**: 11 unaligned Alpaca variants targeting edge-case prompts to shield refusal bypasses.
- **Polyglot Code & Architecture (~23%)**: 15 programming languages, frontend/backend logic, and system-level engineering.
- **Cyber Reasoning & CoT (~9%)**: Explicit `<think>` traces and tool calls to preserve internal latent reasoning.
- **Multi-Turn Roleplay & Fiction (~29%)**: Rich NPC interactions and filtered Gutenberg creative writing (chosen branches only).
- **Conversational & Factual (~17%)**: Multi-turn ShareGPT unfiltered interactions and Dolly open-domain QA.
## Schema
Shipped in universal, model-agnostic chat format:
```json
{
"id": "domain_00001",
"domain": "code_reasoning",
"source": "CodeFeedback",
"messages": [
{"role": "user", "content": "..."},
{"role": "assistant", "content": "..."}
]
}
```
Apply your target model's chat template before feeding to llama-imatrix.
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