L3-Rhaenys-8B-GGUF

3.0 Farewell model. Next i'm going to wait Sao10K to break the bank again with a new 3.1 RP base.

I prefer normal gguf quantization for Q8_0 & Q6_K, imatrix doesn't do any favors for those, quite the opposite. Q6_K is recommended.

Quants 5_K_M, 4_K_M, 3_K_M made using imatrix option with dataset provided by bartowski here

SillyTavern

Text Completion presets

temp 0.9
top_k 30
top_p 0.75
min_p 0.2
rep_pen 1.1
smooth_factor 0.25
smooth_curve 1

Advanced Formatting

Context & Instruct preset by Virt-io

Instruct Mode: Enabled

merge

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

This model was merged using the slerp merge method.

Models Merged

The following models were included in the merge:

• Sao10K/L3-8B-Stheno-v3.2
• Sao10K/L3-8B-Niitama-v1
• princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2

Configuration

The following YAML configuration was used to produce this model:

slices:
  - sources:
      - model: Sao10K/L3-8B-Niitama-v1
        layer_range: [0, 32]
      - model: Sao10K/L3-8B-Stheno-v3.2
        layer_range: [0, 32]
merge_method: slerp
base_model: Sao10K/L3-8B-Niitama-v1
parameters:
  t:
    - filter: self_attn
      value: [0.2, 0.4, 0.6, 0.2, 0.4]
    - filter: mlp
      value: [0.8, 0.6, 0.4, 0.8, 0.6]
    - value: 0.4
dtype: bfloat16


slices:
  - sources:
      - model: tannedbum/L3-Niitama-Stheno-8B
        layer_range: [0, 32]
      - model: princeton-nlp/Llama-3-Instruct-8B-SimPO-v0.2
        layer_range: [0, 32]
merge_method: slerp
base_model: tannedbum/L3-Niitama-Stheno-8B
parameters:
  t:
    - filter: self_attn
      value: [0.2, 0.4, 0.6, 0.2, 0.4]
    - filter: mlp
      value: [0.8, 0.6, 0.4, 0.8, 0.6]
    - value: 0.4
dtype: bfloat16

Want to support my work ? My Ko-fi page: https://ko-fi.com/tannedbum