---
datasets:
- ehartford/dolphin
- jondurbin/airoboros-2.2.1
- ehartford/dolphin-coder
- teknium/openhermes
- ise-uiuc/Magicoder-OSS-Instruct-75K
- ise-uiuc/Magicoder-Evol-Instruct-110K
- LDJnr/Capybara
language:
- en
license: apache-2.0
---

This is pruned down version of [cognitivecomputations/dolphin-2.6-mistral-7b-dpo](https://huggingface.co/cognitivecomputations/dolphin-2.6-mistral-7b-dpo) from 7.24B params to 5.93B params (~ 82%).

# Steps to replicate:

Use [laserQlora.ipynb](https://github.com/cognitivecomputations/laserRMT/blob/main/laserQlora.ipynb) from [cognitivecomputations/laserRMT](https://github.com/cognitivecomputations/laserRMT) to determine which layers should be eliminated.

Replace `model_name = "mistralai/Mistral-7B-v0.1"` with `model_name = "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"`.
I also ran the script only for `self_attn.v_proj` (so change the script to `layer_types=["self_attn.v_proj"]`)

Order by snr descending and eliminate top layers using [mergekit](https://github.com/arcee-ai/mergekit).
The threshold for elimination is up to you, depeding on how many layers you want removed. I decided to remove 6 layers (indexes: 3, 5, 16, 18, 19, 24 )

Here is the mergekit config:
```yml
slices:
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [0, 3]
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [4, 5]
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [6, 16]
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [17, 18]
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [20, 24]
  - sources:
    - model: "cognitivecomputations/dolphin-2.6-mistral-7b-dpo"
      layer_range: [25, 32]
merge_method: passthrough
dtype: bfloat16
```

The model outputted by mergekit with this configuration is this model (dolphin-2.6-mistral-7b-dpo-5.93B).