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---
base_model: []
library_name: transformers
tags:
- mergekit
- merge
- llama
- not-for-all-audiences
---
# GGUF / IQ / Imatrix for [Silver-Sun-11B](https://huggingface.co/ABX-AI/Silver-Sun-11B)
**RE-UPLOAD: The configuration was wrong on the previous quantization. Fixed now! All quants are re-uploaded and Q8 is added**
**Why Importance Matrix?**
**Importance Matrix**, at least based on my testing, has shown to improve the output and performance of "IQ"-type quantizations, where the compression becomes quite heavy.
The **Imatrix** performs a calibration, using a provided dataset. Testing has shown that semi-randomized data can help perserve more important segments as the compression is applied.
Related discussions in Github:
[[1]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
The imatrix.txt file that I used contains general, semi-random data, with some custom kink.
# Silver-Sun-11B
> I'd like to experiment more with merging 11B, hopefully adding more options of this weight class.
> This model is good at writing and reasoning, with a preference for more profane NSFW language when the appropriate cards are used.
> I've been having fun with it so far, although at times it can be a bit blunt, although some may prefer that. It's also highly uncensored.
Works best with Alpaca instruction presets.
## Merge Details
This is a merge of pre-trained language models created using [mergekit](https://github.com/cg123/mergekit).
### Merge Method
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* ABX-AI/Solstice-FKL-11B
>[!NOTE]
>A mixture of [Sao10K/Solstice-11B-v1](https://huggingface.co/Sao10K/Solstice-11B-v1) and [saishf/Fimbulvetr-Kuro-Lotus-10.7B](https://huggingface.co/saishf/Fimbulvetr-Kuro-Lotus-10.7B)
* [Himitsui/Kaiju-11B](https://huggingface.co/Himitsui/Kaiju-11B)
### OpenLLM Eval Results
[Detailed Results + Failed GSM8K](https://huggingface.co/datasets/open-llm-leaderboard/details_ABX-AI__Silver-Sun-11B)
>[!NOTE]
>I had to remove GSM8K from the results and manually re-average the rest. GSM8K failed due to some issue with formatting, which is not experienced during practical usage.
>By removing the GSM8K score, the average is VERY close to upstage/SOLAR-10.7B-v1.0 (74.20), which would make sense.
>Feel free to ignore the actual average and use the other scores individually for reference.
| Metric |Value|
|---------------------------------|----:|
|Avg. |74.13|
|AI2 Reasoning Challenge (25-Shot)|69.80|
|HellaSwag (10-Shot) |87.91|
|MMLU (5-Shot) |66.90|
|TruthfulQA (0-shot) |61.89|
|Winogrande (5-shot) |84.14|
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: ABX-AI/Solstice-FKL-11B
layer_range: [0, 48]
- model: Himitsui/Kaiju-11B
layer_range: [0, 48]
merge_method: slerp
base_model: ABX-AI/Solstice-FKL-11B
parameters:
t:
- filter: self_attn
value: [0.6, 0.7, 0.8, 0.9, 1]
- filter: mlp
value: [0.4, 0.3, 0.2, 0.1, 0]
- value: 0.5
dtype: bfloat16
``` |