Create README.md

README.md

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+---
+base_model:
+- cgato/TheSpice-7b-v0.1.1
+- ABX-AI/Laymonade-7B
+library_name: transformers
+tags:
+- mergekit
+- merge
+- not-for-all-audiences
+license: other
+---
+# GGUF / IQ / Imatrix for [Spicy-Laymonade-7B](https://huggingface.co/ABX-AI/Spicy-Laymonade-7B)
+
+![image/png](https://cdn-uploads.huggingface.co/production/uploads/65d936ad52eca001fdcd3245/bMW7mRqBS_xQJBXn-szWS.png)
+
+**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.
+
+# Spicy-Laymonade-7B
+
+Well, we have Laymonade, so why not spice it up? This merge is a step into creating a new 9B.
+
+However, I did try it out, and it seemed to work pretty well.
+
+## 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:
+* [cgato/TheSpice-7b-v0.1.1](https://huggingface.co/cgato/TheSpice-7b-v0.1.1)
+* [ABX-AI/Laymonade-7B](https://huggingface.co/ABX-AI/Laymonade-7B)
+
+### Configuration
+
+The following YAML configuration was used to produce this model:
+
+```yaml
+slices:
+  - sources:
+      - model: cgato/TheSpice-7b-v0.1.1
+        layer_range: [0, 32]
+      - model: ABX-AI/Laymonade-7B
+        layer_range: [0, 32]
+merge_method: slerp
+base_model: ABX-AI/Laymonade-7B
+parameters:
+  t:
+    - filter: self_attn
+      value: [0.7, 0.3, 0.6, 0.2, 0.5]
+    - filter: mlp
+      value: [0.3, 0.7, 0.4, 0.8, 0.5]
+    - value: 0.5
+dtype: bfloat16
+
+```