metadata

base_model:
  - flammenai/Mahou-1.3-mistral-nemo-12B
library_name: transformers
tags:
  - mergekit
  - merge

MN-Tiramisu-12B

This is a really yappity-yappy yapping model that's good for long-form RP. Tried to rein it in with Mahou and give it some more character understanding with Pantheon. Feedback is always welcome.

Native Context Length: 16K/16384 (can be extended using RoPE, YMMY)

Prompt Template: Chat

<|im_start|>system
{system prompt}<|im_end|>
<|im_start|>user
{message}<|im_end|>
<|im_start|>assistant
{response}

Recommended Settings:

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 (maybe less, a little bit goes a long way with Nemo)
Min-P: 0.1 to 0.2
(all other samplers disabled)

Merge Details

This is a merge of pre-trained language models created using mergekit.

Merge Method

This model was merged using the linear DARE merge method using flammenai/Mahou-1.3-mistral-nemo-12B as a base.

Models Merged

The following models were included in the merge:

nbeerbower/mistral-nemo-gutenberg-12B-v4
Sao10K/MN-12B-Lyra-v1
Gryphe/Pantheon-RP-1.5-12b-Nemo
flammenai/Mahou-1.3-mistral-nemo-12B

Configuration

The following YAML configuration was used to produce this model:

base_model: flammenai/Mahou-1.3-mistral-nemo-12B
dtype: bfloat16
merge_method: dare_linear
slices:
- sources:
  - layer_range: [0, 40]
    model: Gryphe/Pantheon-RP-1.5-12b-Nemo
    parameters:
      weight: [0.45, 0.35, 0.35, 0.2, 0.2]
  - layer_range: [0, 40]
    model: Sao10K/MN-12B-Lyra-v1
    parameters:
      weight: [0.25, 0.3, 0.35, 0.3, 0.2]
  - layer_range: [0, 40]
    model: nbeerbower/mistral-nemo-gutenberg-12B-v4
    parameters:
      weight:
      - filter: mlp
        value: [0.1, 0.2, 0.1, 0.4, 0.5]
      - value: [0.1, 0.2, 0.1, 0.2, 0.2]
  - layer_range: [0, 40]
    model: flammenai/Mahou-1.3-mistral-nemo-12B
    parameters:
      weight:
      - filter: mlp
        value: [0.2, 0.15, 0.2, 0.1, 0.1]
      - value: [0.2, 0.15, 0.2, 0.3, 0.4]
tokenizer_source: union

And as always, have a great day!