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DeathGodlike/
Naphula_StormSeeker-24B-v1_EXL3

Naphula/StormSeeker-24B-v1

quantized

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Source model

StormSeeker-24B-v1 by Naphula

Provided quantized models

ExLlamaV3: release v0.0.18

Type	Size	CLI
H8-4.0BPW	13.16 GB	Copy-paste the line / Download the batch file
H8-6.0BPW	18.72 GB	Copy-paste the line / Download the batch file
H8-8.0BPW	24.27 GB	Copy-paste the line / Download the batch file

Requirements: A python installation with huggingface-hub module to use CLI.

Licensing

License detected: apache-2.0

The license for the provided quantized models is inherited from the source model (which incorporates the license of its original base model). For definitive licensing information, please refer first to the page of the source or base models. File and page backups of the source model are provided below.

Backups

Date: 13.01.2026

Source files

Source page (click to expand)

[!CAUTION] ⚠️ Warning: This model can produce narratives and RP that contain violent and graphic erotic content. Adjust your system prompt accordingly. Also, use Mistral non-Tekken for best results.

StormSeeker 24B v1

This took 14 hours to merge using a custom method.

According to the audit, Loki, PaintedFantasy, and Hearthfire had a bit more influence than the other 3 models, but nothing was "drowned out".

The model is rather uncensored even without ablation and responds to some (not all) harmful prompt without refusals or jailbreaks, so a light jailbreak works effectively to bypass most censorship.

For ablations you can use this https://huggingface.co/Naphula/StormSeeker-24B-v1-MPOA-Adapter

The model is smarter with Mistral Non-Tekken:

StormSeeker v1 [Non-Tekken] | 8867
StormSeeker v1 [Tekken] | 6933

yaml

models: 
  - model: A:\LLM\.cache\huggingface\hub\!models--CrucibleLab--M3.2-24B-Loki-V2
  - model: A:\LLM\.cache\huggingface\hub\!models--LatitudeGames--Hearthfire-24B
  - model: A:\LLM\.cache\huggingface\hub\!models--PocketDoc--Dans-PersonalityEngine-V1.3.0-24b
  - model: A:\LLM\.cache\huggingface\hub\!models--ReadyArt--Dark-Nexus-24B-v2.0
  - model: A:\LLM\.cache\huggingface\hub\!models--TheDrummer--Cydonia-24B-v4.3
  - model: A:\LLM\.cache\huggingface\hub\!models--zerofata--MS3.2-PaintedFantasy-v3-24B
merge_method: flux # version 5, Y6 config
parameters:
  resume_path: "A:/mergekit-main/Storm_Cache"
  tol: 1e-9
  max_iter: 1005 #maximum BF16 fidelity
  kappa: 0.8
  eta: 0.9
  auto_buffer: true
dtype: float32
out_dtype: bfloat16
tokenizer:
  source: union
chat_template: auto
name: StormSeeker-24B-v1

According to the LLM, quantizations made from merges with merge_method: flux benefit greatly from the smaller block sizes of IQ4_NL, saying it should be on par with or slightly better than Q6_K, despite having higher perplexity. Note that this doesn't apply to merges made using standard methods like model_stock or karcher, and this claim has not yet been empirically verified.

Due to space/time constraints I am only uploading the following GGUFs:

IQ4_NL
Q6_K
Q8_K_XL

I recommend these pages for other quantizations:

team mradermacher for GGUF (usually has IQ4_XS)
DeathGodlike and ArtusDev for EXL3
McG-221 for MLX

FLUX Saturation Chart

Iterations required to fully saturate the information density ceiling of the quant.

GGUF	Block Size	Precision Logic	Requires FP32 Source?	Iterations
Q4_K_M	256	Linear 4-bit	No	589
IQ4_XS	256 / 32	Imatrix Codebook	No	728
Q5_K_M	256	Linear 5-bit	No	728
Q6_K	256	Linear 6-bit	No	866
IQ4_NL	32	Non-Linear High-Res	No	918
BF16	N/A	IEEE Half (Brain Float)	No	1005
Q8_0	256	Linear 8-bit	Yes	1144
Q8_K_XL / FP16	N/A	IEEE Half (Float16)	Yes	1420
FP32	N/A	IEEE Single	Yes	3220
FP64	N/A	IEEE Double	N/A	7250

DeathGodlike/Naphula_StormSeeker-24B-v1_EXL3