README.md

---
base_model: []
library_name: transformers
tags:
- mergekit
- merge

---
![cute](https://huggingface.co/matchaaaaa/Chaifighter-Latte-14B/resolve/main/chaifighter-latte-cute.png)

**Thanks again to [@Brooketh](https://huggingface.co/brooketh) for the [GGUFs](https://huggingface.co/backyardai/Chaifighter-Latte-14B-GGUF)!!

# Chaifighter Latte 14B

Finally here, Chaifighter Latte is the successor to the Chaifighter 20B models. Like its predecessors, it is Mistral-based, but now it is dramatically reduced in size. Chaifighter Latte is formulated for creative, rich, verbose writing without sacrificing intelligence, awareness, and context-following abilities. Chaifighter Latte retains the great taste of the original, and despite being significantly lighter at 14 billion parameters, it performs even better. Try it for yourself!

## Prompt Template: Alpaca 

```
Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:
```

## Recommended Settings: Universal-Light

Here are some settings ranges that tend to work for me. They aren't strict values, and there's a bit of leeway in them. Feel free to experiment a bit!

* Temperature:        **1.0** *to* **1.25** (adjust to taste, but keep it low. Chaifighter is creative enough on its own)
* Min-P:              **0.1** (increasing might help if it goes cuckoo, but I suggest keeping it there)
* Repetition Penalty: **1.05** *to* **1.1** (high values aren't needed and usually degrade output)
* Rep. Penalty Range: **256** *or* **512**
* *(all other samplers disabled)*

## The Deets

### Mergekit

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 passthrough merge method.

### Models Merged

* [SanjiWatsuki/Kunoichi-7B](https://huggingface.co/SanjiWatsuki/Kunoichi-7B)
* [Sao10K/Fimbulvetr-11B-v2](https://huggingface.co/Sao10K/Fimbulvetr-11B-v2)
* [Sao10K/Frostwind-v2.1-m7](https://huggingface.co/Sao10K/Frostwind-v2.1-m7)
* [Gryphe/MythoMist-7b](https://huggingface.co/Gryphe/MythoMist-7b)


### The Sauce

The following YAML configuration was used to produce this model:

```yaml
slices:
  - sources:
    - model: SanjiWatsuki/Kunoichi-7B
      layer_range: [16, 24]
merge_method: passthrough
dtype: float32
name: Kuno-splice
---
slices:
  - sources:
    - model: Sao10K/Fimbulvetr-11B-v2
      layer_range: [8, 16]
merge_method: passthrough
dtype: float32
name: Fimbul-splice
---
models: 
  - model: Kuno-splice
    parameters:
      weight: [1, 1, 0.75, 0.625, 0.5, 0.375, 0.25, 0, 0] # 0.125 / 0.875 values removed here - "math gets screwy" 
  - model: Fimbul-splice
    parameters:
      weight: [0, 0, 0.25, 0.375, 0.5, 0.625, 0.75, 1, 1] # 0.125 / 0.875 values removed here - "math gets screwy" 
merge_method: dare_linear # according to some paper, "DARE is all you need"
base_model: Kuno-splice
dtype: float32
name: Kuno-Fimbul-splice
---
models: 
  - model: Sao10K/Frostwind-v2.1-m7
  - model: Gryphe/MythoMist-7b
    parameters:
      weight: 0.37
      density: 0.8
merge_method: dare_ties
base_model: Sao10K/Frostwind-v2.1-m7
dtype: float32
name: Frosty-Mytho
---
slices:
  - sources:
    - model: Sao10K/Fimbulvetr-11B-v2
      layer_range: [32, 40]
merge_method: passthrough
dtype: float32
name: Fimbul-splice-2
---
slices:
  - sources:
    - model: Frosty-Mytho
      layer_range: [8, 16]
merge_method: passthrough
dtype: float32
name: Frosty-Mytho-splice
---
models: 
  - model: Fimbul-splice-2
    parameters:
      weight: [1, 1, 0.75, 0.625, 0.5, 0.375, 0.25, 0, 0] # 0.125 / 0.875 values removed here - "math gets screwy" 
  - model: Frosty-Mytho-splice
    parameters:
      weight: [0, 0, 0.25, 0.375, 0.5, 0.625, 0.75, 1, 1] # 0.125 / 0.875 values removed here - "math gets screwy" 
merge_method: dare_linear # according to some paper, "DARE is all you need"
base_model: Fimbul-splice-2
dtype: float32
name: Fimbul-Frosty-Mytho-splice
---
slices:
  - sources: # kunoichi
    - model: SanjiWatsuki/Kunoichi-7B
      layer_range: [0, 16]
  - sources: # kunoichi gradient fimbul splice
    - model: Kuno-Fimbul-splice
      layer_range: [0, 8]
  - sources: # fimbulvetr
    - model: Sao10K/Fimbulvetr-11B-v2
      layer_range: [16, 32]
      # insert splice here
  - sources: # fimbulvetr gradient fwmm splice
    - model: Fimbul-Frosty-Mytho-splice
      layer_range: [0, 8]
  - sources: # frostwind + mythomist
    - model: Frosty-Mytho
      layer_range: [16, 32]
merge_method: passthrough
dtype: float32
name: Chaifighter-Latte-14B
```

### The Thought Process

So, I wanted the first layers to be Kunoichi. Kunoichi was chosen for its strong context and instruct following abilities, as well as being a really smart model overall. Plus, it's not sloutch at RP. I think this is partly what gave previous Chaifighter models the awareness that many people liked. To best harness its stellar prompt processing performance, I put Kunoichi at the head of the stack. 
Next, I applied a gradient merge that I call a "splice". Splicing models like this solves what I believe has significantly hurt the earlier Chaifighter models and many other frankenmerges, which is layer dissimilarity. Splicing the end of one stack from model A with the beginning of another stack of model B in theory helps smoothen over those differences and help bring everything together.
The second model I introduced is Fimbulvetr-v2. This should be no surprise, as it's also a well-established ingredient of the Chaifighter recipe. Boasting incredibly strong coherence, it is the glue that can hold a story together, even with multiple characters and over longer contexts. I felt like the best place for Fimbulvetr was right after Kunoichi.
Another splice.
Lastly, I picked Frostwind and MythoMist as the final layers in this merge. I wanted to introduce MythoMist into the merge as I felt like it was what gave Chaifighter its flavorful writing. I paired it with Frostwind, as it's a very creative writer as well, and I felt like the two (with more emphasis on Frostwind for consistency) produced high quality outputs up to my standards.



Thanks for looking at my model, and have a fantastic day! :)