README.md

---
library_name: transformers
license: other
language:
- en
tags:
- gguf
- quantized
- roleplay
- imatrix
- mistral
- merge
inference: false
# base_model:
# - ResplendentAI/Datura_7B
# - ChaoticNeutrals/Eris_Floramix_DPO_7B
---

> [!TIP]
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This repository hosts GGUF-Imatrix quantizations for [ChaoticNeutrals/BuRP_7B](https://huggingface.co./ChaoticNeutrals/BuRP_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)

**Steps:**
```
Base⇢ GGUF(F16)⇢ Imatrix-Data(F16)⇢ GGUF(Imatrix-Quants)
```
**Quants:**
```python
    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](https://huggingface.co./Lewdiculous/Datura_7B-GGUF-Imatrix/blob/main/imatrix-with-rp-format-data.txt).

**Alt-image:**

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/65d4cf2693a0a3744a27536c/CS8ltMyem_KeoSSVuuAdx.jpeg)

**Original model information:**

# BuRP

![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/626dfb8786671a29c715f8a9/RsiscU77BoQSzDUJkLtYc.jpeg)

So you want a model that can do it all? You've been dying to RP with a superintelligence who never refuses your advances while sticking to your strange and oddly specific dialogue format?

Well, look no further because BuRP is the model you need.

### Configuration

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
      - model: ErisLaylaSLERP
        layer_range: [0, 32]
      - model: ParadigmInfinitySLERP
        layer_range: [0, 32]
merge_method: slerp
base_model: ParadigmInfinitySLERP
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
```