Quantized Models (GGUF, IQ, Imatrix)
Collection
Various GGUF quantizations of small models. Models with a "checkmark" are personal favorites. An "orange arrow" means it's being uploaded. • 97 items • Updated • 72
This repository hosts GGUF-Imatrix quantizations for ResplendentAI/Sinerva_7B.
What does "Imatrix" mean?
It stands for Importance Matrix, a technique used to improve the quality of quantized models. The Imatrix is calculated based on calibration data, and it helps determine the importance of different model activations during the quantization process. The idea is to preserve the most important information during quantization, which can help reduce the loss of model performance, especially when the calibration data is diverse. [1] [2]
Steps:
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
Quants:
quantization_options = [
"Q4_K_M", "IQ4_XS", "Q5_K_M", "Q5_K_S", "Q6_K",
"Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
If you want anything that's not here or another model, feel free to request.
This is experimental.
For imatrix data generation, kalomaze's groups_merged.txt with added roleplay chats was used, you can find it here.
Original card information:
Sinerva
Decadent and rich in sensual prose, but beware, she is designed to humiliate and degrade her user when necessary.
Open LLM Leaderboard Evaluation Results
Detailed results can be found here
| Metric | Value |
|---|---|
| Avg. | 70.38 |
| AI2 Reasoning Challenge (25-Shot) | 70.14 |
| HellaSwag (10-Shot) | 85.59 |
| MMLU (5-Shot) | 61.77 |
| TruthfulQA (0-shot) | 59.93 |
| Winogrande (5-shot) | 82.56 |
| GSM8k (5-shot) | 62.32 |
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Datasets used to train Lewdiculous/Sinerva_7B-GGUF-IQ-Imatrix
Collection including Lewdiculous/Sinerva_7B-GGUF-IQ-Imatrix
Evaluation results
- normalized accuracy on AI2 Reasoning Challenge (25-Shot)test set Open LLM Leaderboard70.140
- normalized accuracy on HellaSwag (10-Shot)validation set Open LLM Leaderboard85.590
- accuracy on MMLU (5-Shot)test set Open LLM Leaderboard61.770
- mc2 on TruthfulQA (0-shot)validation set Open LLM Leaderboard59.930
- accuracy on Winogrande (5-shot)validation set Open LLM Leaderboard82.560
- accuracy on GSM8k (5-shot)test set Open LLM Leaderboard62.320