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---
library_name: transformers
tags:
- mistral
- quantized
- text-generation-inference
- roleplay
# - rp
# - uncensored
pipeline_tag: text-generation
inference: false
# language:
# - en
# FILL THE INFORMATION:
# Reference: ChaoticNeutrals/Eris_Remix_7B
# Author: ChaoticNeutrals
# Model: Eris_Remix_7B
# Llama.cpp version: b2343
---
```python
quantization_options = [
"Q4_K_M", "Q4_K_S", "IQ4_NL", "IQ4_XS", "Q5_K_M",
"Q5_K_S", "Q6_K", "Q8_0", "IQ3_M", "IQ3_S", "IQ3_XS", "IQ3_XXS"
]
```
## GGUF-Imatrix quantizations for [ChaoticNeutrals/Eris_Remix_7B](https://huggingface.co/ChaoticNeutrals/Eris_Remix_7B/).
All credits belong to the author.
If you liked these, check out the work with [FantasiaFoundry's GGUF-IQ-Imatrix-Quantization-Script](https://huggingface.co/FantasiaFoundry/GGUF-Quantization-Script).
## What does "Imatrix" mean?
It stands for **Importance Matrix**, a technique used to improve the quality of quantized models. <br>
[[1]](https://github.com/ggerganov/llama.cpp/discussions/5006/) <br>
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 and lead to better quality preservation, especially when the calibration data is diverse. <br>
[[2]](https://github.com/ggerganov/llama.cpp/discussions/5263#discussioncomment-8395384/)
For --imatrix data, included `imatrix.dat` was used.
Using [llama.cpp-b2343](https://github.com/ggerganov/llama.cpp/releases/tag/b2343/):
```
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
```
The new **IQ3_S** quant-option has shown to be better than the old Q3_K_S, so I added that instead of the later. Only supported in `koboldcpp-1.59.1` or higher.
If you want any specific quantization to be added, feel free to ask.
<!-- ## Model image: -->
## Original model information:
# Remix
### Configuration
The following YAML configuration was used to produce this model:
```yaml
slices:
- sources:
- model: SpecialEdition
layer_range: [0, 32]
- model: Remix
layer_range: [0, 32]
merge_method: slerp
base_model: SpecialEdition
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
``` |