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---
library_name: transformers
tags:
- mergekit
- merge
- roleplay
- mistral
inference: false
license: other
---
This repository hosts GGUF-IQ-Imatrix quants for [Nitral-AI/Eris-Beach_Day-7b](https://huggingface.co./Nitral-AI/Eris-Beach_Day-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]](https://github.com/ggerganov/llama.cpp/discussions/5006) [[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384)
For imatrix data generation, kalomaze's `groups_merged.txt` with added roleplay chats was used, you can find it [here](https://huggingface.co./Lewdiculous/Datura_7B-GGUF-Imatrix/blob/main/imatrix-with-rp-format-data.txt). This was just to add a bit more diversity to the data.
**Steps:**
```
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
```
*Using the latest llama.cpp at the time.*
```python
quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_XS", "Q5_K_M", "Q5_K_S",
"Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XXS"
]
```
**Card image:**
## Original model information:
This model was merged using the SLERP merge method.
### Models Merged
The following models were included in the merge:
* [Nitral-AI/Infinity-RPool-7b](https://huggingface.co./Nitral-AI/Infinity-RPool-7b)
* [Nitral-AI/Eris-FloraLake-7b](https://huggingface.co./Nitral-AI/Eris-FloraLake-7b)
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: Nitral-AI/Eris-FloraLake-7b
layer_range: [0, 32]
- model: Nitral-AI/Infinity-RPool-7b
layer_range: [0, 32]
merge_method: slerp
base_model: Nitral-AI/Eris-FloraLake-7b
parameters:
t:
- filter: self_attn
value: [0, 0.5, 0.3, 0.7, 1]
- filter: mlp
value: [1, 0.5, 0.7, 0.3, 0]
- value: 0.5
dtype: bfloat16
``
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