CALIBRATION DATA INFORMATION
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This model was quantized using importance matrix (imatrix) generation.
The imatrix captures which weights in the model are most important for
maintaining output quality during extreme compression (2-bit quantization).

WHAT IS CALIBRATION?
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Calibration is the process of running sample inputs through the model to
measure which tensors (weight matrices) contribute most to the output.
These measurements create an "importance matrix" that guides the quantizer
to preserve precision where it matters most.

CALIBRATION DATA CHARACTERISTICS
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Good calibration data should be:

1. REPRESENTATIVE
   - Matches the domain the model will operate in
   - Similar vocabulary and complexity to expected inputs
   - Reflects actual use case scenarios

2. DIVERSE
   - Multiple topics, subjects, and writing styles
   - Mix of common and rare tokens
   - Varied sentence structures and lengths

3. SUFFICIENT
   - 100-500 text chunks of typical document length
   - More chunks = better quality (diminishing returns beyond ~500)
   - Each chunk processed independently

4. NATURAL
   - Real-world text (not synthetic or random)
   - Domain-appropriate (code for code models, medical for medical models)
   - Representative token distribution

CALIBRATION PROCESS PARAMETERS
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Typical settings for this quantization:

  Chunks Processed:  200-500 (production quality)
  Chunk Size:        Typical document/paragraph length
  GPU Acceleration:  Enabled (99 layers offloaded)
  Thread Count:      Auto-detected based on CPU

QUALITY IMPACT
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The importance matrix generated from quality calibration data enables:

  - 3-8% perplexity increase (vs 10-20% without imatrix)
  - Preservation of critical weights
  - Intelligent bit allocation per tensor
  - 16x compression with minimal quality loss

CALIBRATION DATA SOURCES
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Common sources for high-quality calibration data:

  - Wikitext-2-raw (general language models)
  - Domain-specific corpora (medical, legal, code)
  - The Pile subset (diverse web text)
  - Custom curated datasets matching expected use

VERIFICATION
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Quantized models are tested for:
  ✓ Perplexity measurement vs baseline
  ✓ Sample inference quality
  ✓ Token prediction accuracy
  ✓ Model file integrity

NOTES
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- Calibration is performed once per source model
- Same imatrix can be reused for different target formats
- Domain-specific calibration yields better results
- GPU acceleration significantly speeds up generation

For questions about the calibration methodology used for this model,
please open a discussion on the model's Hugging Face page.