HarmonicHarlequin_v5-20B

Elfrino/HarmonicHarlequin_v5-20B

HarmonicHarlequin_v5-20B is a text generation model from Elfrino. Use it when you need the model to write or continue text. It is set up for transformers.

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Parameters

33.3B

66.6 GB on disk

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Hugging Face

Repo makeup

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.safetensors66.6 GB · 100%

At a glance

Task: Text Generation
Library: transformers
Model type: llama
Access: Public
Created: Oct 1, 2024
Updated: Jan 6, 2025
SHA: 0798fc04

Try a prompt

Base models

Undi95/PsyMedRP-v1-20B

Model card

From the Hugging Face model README

Warning: Cute Harlequin Gal inside!

*NOTES: Oops, it's actually a 33B

The 5th iteration of Harmonic Harlequin and seems the most coherent and lucid of the lot (early days though), need to test for creativity, might be a solid model, fingers crossed..

RECOMMENDED SETTINGS:

(based on KoboldCPP):

Preset: Mayday

Temperature - 1.3

Max Ctx. Tokens - 4096

Top p Sampling - 0.99

Repetition Penalty - 1.1

Amount to Gen. - 280

New findings:

around 47 layers offloaded to GPU

Smartcontext enabled

Custom RoPe Config enabled (but left as default)

Prompt template: Alpaca

merge

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

Merge Details

Merge Method

This model was merged using the passthrough merge method.

Models Merged

The following models were included in the merge:

Undi95/PsyMedRP-v1-20B

Configuration

The following YAML configuration was used to produce this model:

# The amount to attenuate the Q and K matrices of the *FIRST COPY* of each layer.
# NOTE: This scales the score matrix values by QK_ATTENUATION_FACTOR^2 (eg: sqrt(1/2)^2 = 1/2).
const_tag: &QK_ATTENUATION_FACTOR 0.84  # ≈ sqrt(1/2) <- This was changed, v4 was 0.7071067812 )

# The amount to scale the contribution to the residual stream (to hopefully reduce overshoot).
const_tag: &RESIDUAL_SCALE_FACTOR 0.71  # ≈ sqrt(1/2) <- This was changed, v4 was 0.7071067812 )

# Make the first copy *ONLY* take a more "bird's eye view" (ie: pay attention to more of the context).
model1-filter-env: &MODEL1_FILTER_ENV
  parameters:
    scale:
      - filter: q_proj
        value: *QK_ATTENUATION_FACTOR
      - filter: k_proj
        value: *QK_ATTENUATION_FACTOR
      - filter: down_proj
        value: *RESIDUAL_SCALE_FACTOR
      - value: 1.0

# Make the scond copy pay attention to the context as before.
model2-filter-env: &MODEL2_FILTER_ENV
  parameters:
    scale:
      - filter: down_proj
        value: *RESIDUAL_SCALE_FACTOR
      - value: 1.0

slices:

  # The first 10 layers are not duplicated.
  - sources:
    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [0, 10]
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [10, 11]
      <<: *MODEL1_FILTER_ENV
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [10, 11]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [11, 12]
      <<: *MODEL1_FILTER_ENV
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [11, 12]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [12, 13]
      <<: *MODEL1_FILTER_ENV
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      layer_range: [12, 13]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [13, 14]
      <<: *MODEL1_FILTER_ENV
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      layer_range: [13, 14]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [14, 15]
      <<: *MODEL1_FILTER_ENV
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      layer_range: [14, 15]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [15, 16]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [16, 17]
      <<: *MODEL1_FILTER_ENV
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      layer_range: [16, 17]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [17, 18]
      <<: *MODEL1_FILTER_ENV
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      <<: *MODEL2_FILTER_ENV
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      <<: *MODEL1_FILTER_ENV
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      <<: *MODEL2_FILTER_ENV
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      layer_range: [22, 23]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [23, 24]
      <<: *MODEL1_FILTER_ENV
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      layer_range: [23, 24]
      <<: *MODEL2_FILTER_ENV
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      layer_range: [24, 25]
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      layer_range: [24, 25]
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      layer_range: [26, 27]
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      layer_range: [27, 28]
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      layer_range: [38, 39]
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      layer_range: [45, 46]
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      layer_range: [46, 47]
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      layer_range: [46, 47]
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      layer_range: [47, 48]
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      layer_range: [48, 49]
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      layer_range: [48, 49]
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      layer_range: [49, 50]
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [49, 50]
      <<: *MODEL2_FILTER_ENV
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [50, 51]
      <<: *MODEL1_FILTER_ENV
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [50, 51]
      <<: *MODEL2_FILTER_ENV
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    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [51, 52]
      <<: *MODEL1_FILTER_ENV
  - sources:
    - model: Undi95/PsyMedRP-v1-20B
      layer_range: [51, 52]
      <<: *MODEL2_FILTER_ENV
   
  # The last 10 layers are not duplicated.
  - sources:
    - model:  Undi95/PsyMedRP-v1-20B
      layer_range: [52, 62]

merge_method: passthrough
dtype: float16

Task: Text Generation
Library: transformers
Type: llama
Created: Oct 1, 2024
Updated: Jan 6, 2025