chargoddard_-_piano-medley-7b-8bits

Quantization made by Richard Erkhov.

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piano-medley-7b - bnb 8bits

• Model creator: https://huggingface.co/chargoddard/
• Original model: https://huggingface.co/chargoddard/piano-medley-7b/

Original model description:

license: cc-by-nc-4.0 datasets:

• pankajmathur/orca_mini_v1_dataset
• openai/summarize_from_feedback
• PygmalionAI/PIPPA
• chargoddard/rpguild
• lemonilia/LimaRP
• PKU-Alignment/PKU-SafeRLHF
• Intel/orca_dpo_pairs
• allenai/ultrafeedback_binarized_cleaned tags:
• merge
• mergekit

---

Another experiment in the line of loyal-piano-m7.

Steps taken to produce this model:

• Train loyal-piano-m7
• cDPO with HuggingFaceH4/ultrafeedback_binarized to produce loyal-piano-m7-cdpo
• Train another model with different sampling of the same source datasets as loyal-piano, let's call it servile-harpsichord
• cDPO servile-harpsichord with allenai/ultrafeedback_binarized_cleaned, Intel/orca_dpo_pairs, and a helpfulness-only version of PKU-Alignment/PKU-SafeRLHF
• TIES merge several checkpoints of servile-harpsichord-cdpo with loyal-piano-m7-cdpo

Local benchmarks show the result to be better than any of the individual components. Let's see if that holds up!

Trained using the Alpaca prompt format.

Configuration for final merge:

models:
  - model: chargoddard/loyal-piano-m7-cdpo
    parameters:
      density: 1.0
      weight: 1.0
  - model: /home/ubuntu/servile-harpsichord-cdpo/checkpoint-4186
    parameters:
      weight: 0.1
  - model: /home/ubuntu/servile-harpsichord-cdpo/checkpoint-5796
    parameters:
      weight: 0.2
  - model: /home/ubuntu/servile-harpsichord-cdpo/checkpoint-6118
    parameters:
      weight: 0.3
  - model: /home/ubuntu/servile-harpsichord-cdpo/final
    parameters:
      weight: 0.4
merge_method: ties
base_model: mistralai/Mistral-7B-v0.1
dtype: bfloat16
parameters:
  density: 0.4
  normalize: true
  int8_mask: true