Mistral-11B-AirOmniMix

Replaced Zephyr by Airoboros 2.2 in the mix.

Description

This repo contains fp16 files of Mistral-11B-AirOmniMix.

Model used

• Mistral-7B-OpenOrca
• Mistral-7B-v0.1-Open-Platypus
• CollectiveCognition-v1.1-Mistral-7B
• airoboros-mistral2.2-7b

Prompt template

The best one after further testing is this one, since Zephyr is out of the merge:

USER: <prompt>
ASSISTANT:

But this one work too:

Below is an instruction that describes a task. Write a response that appropriately completes the request.

### Instruction:
{prompt}

### Response:

Or use any prompting system from one of the 4 source model, should work.

The secret sauce

Mistral-11B-OpenOrcaPlatypus :

slices:
  - sources:
    - model: Open-Orca/Mistral-7B-OpenOrca
      layer_range: [0, 24]
  - sources:
    - model: akjindal53244/Mistral-7B-v0.1-Open-Platypus
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Mistral-11B-CC-Airo :

slices:
  - sources:
    - model: "/content/drive/MyDrive/CC-v1.1-7B-bf16"
      layer_range: [0, 24]
  - sources:
    - model: "/content/drive/MyDrive/Mistral-7B-Airoboros-2.2-bf16"
      layer_range: [8, 32]
merge_method: passthrough
dtype: bfloat16

Mistral-11B-AirOmniMix :

slices:
  - sources:
      - model: Mistral-11B-OpenOrcaPlatypus
        layer_range: [0, 48]
      - model: Mistral-11B-CC-Airo
        layer_range: [0, 48]
merge_method: slerp
base_model: Mistral-11B-OpenOrcaPlatypus
parameters:
  t:
    - filter: lm_head 
      value: [0.75]
    - filter: embed_tokens
      value: [0.75]
    - filter: self_attn
      value: [0.75, 0.25]
    - filter: mlp
      value:  [0.25, 0.75]
    - filter: layernorm
      value: [0.5, 0.5]
    - filter: modelnorm
      value: [0.75]
    - value: 0.5 # fallback for rest of tensors
dtype: bfloat16

I use mergekit for all the manipulation told here.

Some scoring I done myself

hf-causal-experimental (pretrained=/content/drive/MyDrive/Mistral-11B-AirOmniMix), limit: None, provide_description: False, num_fewshot: 0, batch_size: 4

Others

Special thanks to Sushi, Henky for the machine he give me for big task, and Charles Goddard for his amazing tool.

If you want to support me, you can here.