MS3-Tantum-24B-v0.1

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Tantum

Everything is edible if you are brave enough

Overview

It's kind of hard to judge a 24B model after using a 70B for a while. From some tests, I think it might be better than my ms-22B and qwen-32B merges.

It has some prose, some character adherence, and... <think> tags! It will consistently think if you add <think> tag as prefill, tho I think it will obviously not think as well as an actual thinking model distill.

People also like RP-Whole(RP-Broth). You can find it here

Settings:

Samplers: Weird preset | Forgotten-Safeword preset

Prompt format: Mistral-V7 (?)

ChatML and Llama3 give better results imo. In the case of ChatML, there are Dans-PersonalityEngine and Redemption-Wind models that have been trained on it. But Llama3? No clue.

I use this lorebook for all chats instead of a system prompt for mistal models.

Quants

Static | Imatrix

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Merge Details

Merging steps

MS3-test-Merge-1

models:
  - model: unsloth/Mistral-Small-24B-Base-2501
  - model: unsloth/Mistral-Small-24B-Instruct-2501+ToastyPigeon/new-ms-rp-test-ws
    parameters:
        select_topk:
          - value: [0.05, 0.03, 0.02, 0.02, 0.01]
  - model: unsloth/Mistral-Small-24B-Instruct-2501+estrogen/MS2501-24b-Ink-ep2-adpt
    parameters:
        select_topk: 0.1
  - model: trashpanda-org/MS-24B-Instruct-Mullein-v0
    parameters:
        select_topk: 0.4
base_model: unsloth/Mistral-Small-24B-Base-2501
merge_method: sce
parameters:
  int8_mask: true
  rescale: true
  normalize: true
dtype: bfloat16
tokenizer_source: base

dtype: bfloat16
tokenizer_source: base
merge_method: della_linear
parameters:
  density: 0.55
base_model: Step1
models:
  - model: unsloth/Mistral-Small-24B-Instruct-2501
    parameters:
      weight:
        - filter: v_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: o_proj
          value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
        - filter: up_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - filter: gate_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: down_proj
          value: [1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
        - value: 0
  - model: Step1
    parameters:
      weight:
        - filter: v_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: o_proj
          value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
        - filter: up_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - filter: gate_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: down_proj
          value: [0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1]
        - value: 1

Some early MS3 merge. Not really worth using on its own. Just added it for fun.

RP-half1

models:
  - model: ArliAI/Mistral-Small-24B-ArliAI-RPMax-v1.4
    parameters:
      weight: 0.2
      density: 0.7
  - model: trashpanda-org/Llama3-24B-Mullein-v1
    parameters:
      weight: 0.2
      density: 0.7
  - model: TheDrummer/Cydonia-24B-v2
    parameters:
      weight: 0.2
      density: 0.7
merge_method: della_linear
base_model: Nohobby/MS3-test-Merge-1
parameters:
  epsilon: 0.2
  lambda: 1.1
dtype: bfloat16
tokenizer:
 source: base

RP-half2

base_model: Nohobby/MS3-test-Merge-1
parameters:
  epsilon: 0.05
  lambda: 0.9
  int8_mask: true
  rescale: true
  normalize: false
dtype: bfloat16
tokenizer:
 source: base
merge_method: della
models:
  - model: estrogen/MS2501-24b-Ink-apollo-ep2
    parameters:
      weight: [0.1, -0.01, 0.1, -0.02, 0.1]
      density: [0.6, 0.4, 0.5, 0.4, 0.6]
  - model: huihui-ai/Mistral-Small-24B-Instruct-2501-abliterated
    parameters:
      weight: [0.02, -0.01, 0.02, -0.02, 0.01]
      density: [0.45, 0.55, 0.45, 0.55, 0.45]
  - model: ToastyPigeon/ms3-roselily-rp-v2
    parameters:
      weight: [0.01, -0.02, 0.02, -0.025, 0.01]
      density: [0.45, 0.65, 0.45, 0.65, 0.45]
  - model: PocketDoc/Dans-DangerousWinds-V1.1.1-24b
    parameters:
      weight: [0.1, -0.01, 0.1, -0.02, 0.1]
      density: [0.6, 0.4, 0.5, 0.4, 0.6]

RP-whole

base_model: ReadyArt/Forgotten-Safeword-24B-V2.2
merge_method: model_stock
dtype: bfloat16
models:
  - model: mergekit-community/MS3-RP-half1
  - model: mergekit-community/MS3-RP-RP-half2

INT

merge_method: della_linear
dtype: bfloat16
parameters:
  normalize: true
  int8_mask: true
tokenizer:
 source: base
base_model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
models:
    - model: PocketDoc/Dans-PersonalityEngine-V1.2.0-24b
      parameters:
        density: 0.55
        weight: 1
    - model: Undi95/MistralThinker-e2
      parameters:
        density: 0.55
        weight: 1
    - model: d-rang-d/ignore_MS3-Reasoner-mergekit
      parameters:
        density: 0.55
        weight: 1
    - model: arcee-ai/Arcee-Blitz
      parameters:
        density: 0.55
        weight: 1

Tantumv00

output_base_model: "SicariusSicariiStuff/Redemption_Wind_24B"
output_dtype: "bfloat16"
finetune_merge:
  - { "model": "mergekit-community/MS3-INT", "base": "unsloth/Mistral-Small-24B-Instruct-2501", "alpha": 1.0, "is_input": true }
  - { "model": "mergekit-community/MS-RP-whole", "base": "unsloth/Mistral-Small-24B-Instruct-2501", "alpha": 0.7, "is_output": true }
output_dir: "output_model"
device: "cpu"
clean_cache: false
cache_dir: "cache"
storage_dir: "storage"

Doesn't look like a mergekit recipe, right? Well, it's not. It's for a standalone merge tool: https://github.com/54rt1n/shardmerge

If you want to use it for something non-qwen you can replace index.py with this and writer.py with that. A much better solution is possible, ofc, but I'm a dumdum and can't code. The creator knows about this issue and will fix it... Someday, I guess.

You also need to know that this thing is really slow, and it took me 5 hours to cram 3 24B models together.

Tantumv01

dtype: bfloat16
tokenizer:
 source: unsloth/Mistral-Small-24B-Instruct-2501
merge_method: della_linear
parameters:
  density: 0.55
base_model: d-rang-d/MS3-megamerge
models:
  - model: unsloth/Mistral-Small-24B-Instruct-2501
    parameters:
      weight:
        - filter: v_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: o_proj
          value: [1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1]
        - filter: up_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - filter: gate_proj
          value: [0, 0, 1, 1, 1, 1, 1, 1, 1, 0, 0]
        - filter: down_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - value: 0
  - model: d-rang-d/MS3-megamerge
    parameters:
      weight:
        - filter: v_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: o_proj
          value: [0, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0]
        - filter: up_proj
          value: [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
        - filter: gate_proj
          value: [1, 1, 0, 0, 0, 0, 0, 0, 0, 1, 1]
        - filter: down_proj
          value: [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]
        - value: 1