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---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- mergekit
- merge
base_model: Nohobby/MS3-Tantum-24B-v0.1
quantized_by: waldie
---
***
## 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.
**Settings:**
Samplers: [Weird preset](https://files.catbox.moe/ccwmca.json) | [Mullein preset](https://files.catbox.moe/0pkv2j.json)
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](https://files.catbox.moe/daluze.json) lorebook for all chats instead of a system prompt for mistal models.
### Quants
[Static](https://huggingface.co/mradermacher/MS3-Tantum-24B-v0.1-GGUF) | [Imatrix](https://huggingface.co/mradermacher/MS3-Tantum-24B-v0.1-i1-GGUF)
***
## Merge Details
### Merging steps
## MS3-test-Merge-1
```yaml
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
```
```yaml
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
```yaml
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
```yaml
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
```yaml
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
```yaml
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
```yaml
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](https://files.catbox.moe/bgxmuz.py) and writer.py with [that](https://files.catbox.moe/ewww39.py). 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
```yaml
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
``` |