README.md

metadata

license: apache-2.0
language:
  - en
tags:
  - creative
  - creative writing
  - fiction writing
  - plot generation
  - sub-plot generation
  - fiction writing
  - story generation
  - scene continue
  - storytelling
  - fiction story
  - science fiction
  - romance
  - all genres
  - story
  - writing
  - vivid prose
  - vivid writing
  - fiction
  - roleplaying
  - bfloat16
  - swearing
  - rp
  - llama3
  - llama-3
  - enhanced quants
  - max quants
  - maxcpu quants
  - horror
  - finetune
  - merge
pipeline_tag: text-generation
base_model:
  - DavidAU/L3-Dark-Planet-8B
  - Sao10K/L3-8B-Stheno-v3.2
  - NeverSleep/Llama-3-Lumimaid-8B-v0.1-OAS
  - Hastagaras/Jamet-8B-L3-MK.V-Blackroot
  - meta-llama/Meta-Llama-3-8B-Instruct

L3-Dark-Planet-8B-WORDSTORM-B4

This repo contains the full precision source code, in "safe tensors" format to generate GGUFs, GPTQ, EXL2, AWQ, HQQ and other formats. The source code can also be used directly.

Upload will be complete when the parameters show in the upper left side of this page.

This is a modified version of:

[ https://huggingface.co/DavidAU/L3-Dark-Planet-8B-GGUF ]

Please refer to this model card in the interm for usage, templates, settings and so on.

HOWEVER:

This model version's output will vary slightly to very significantly from the "source" model noted.

This model is one of ELEVEN "wordstorm" versions.

Likewise, for each "wordstorm" model in this series, output between versions will also be very different, even when using the same model "formula", as each version uses "random pruning" to alter the final model.

Each model is then evaluated, and the "winners" are uploaded.

A "winner" means new positive change(s) have occured in model instruction following and/or output generation.

You can see some of these wordstorm version "Dark Planets" in this model:

[ https://huggingface.co/DavidAU/L3-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-47B-GGUF ]

[ https://huggingface.co/DavidAU/L3-MOE-8X8B-Dark-Planet-8D-Mirrored-Chaos-47B ]

IMPORTANT: Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers

If you are going to use this model, (source, GGUF or a different quant), please review this document for critical parameter, sampler and advance sampler settings (for multiple AI/LLM aps).

This a "Class 1" (settings will enhance operation) model:

For all settings used for this model (including specifics for its "class"), including example generation(s) and for advanced settings guide (which many times addresses any model issue(s)), including methods to improve model performance for all use case(s) as well as chat, roleplay and other use case(s) (especially for use case(s) beyond the model's design) please see:

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

REASON:

Regardless of "model class" this document will detail methods to enhance operations.

If the model is a Class 3/4 model the default settings (parameters, samplers, advanced samplers) must be set for "use case(s)" uses correctly. Some AI/LLM apps DO NOT have consistant default setting(s) which result in sub-par model operation. Like wise for Class 3/4 models (which operate somewhat to very differently than standard models) additional samplers and advanced samplers settings are required to "smooth out" operation, AND/OR also allow full operation for use cases the model was not designed for.

BONUS - Use these settings for ANY model, ANY repo, ANY quant (including source/full precision):

This document also details parameters, sampler and advanced samplers that can be use FOR ANY MODEL, FROM ANY REPO too - all quants, and of course source code operation too - to enhance the operation of any model.

[ https://huggingface.co/DavidAU/Maximizing-Model-Performance-All-Quants-Types-And-Full-Precision-by-Samplers_Parameters ]

NOTE:

I strongly suggest you also visit the DavidAU GGUF (below) repo too for more details in using this model ; especially if it is "Class 3" or "Class 4" to get maximum performance from the model.

For full information about this model, including:

• Details about this model and its use case(s).
• Context limits
• Special usage notes / settings.
• Any model(s) used to create this model.
• Template(s) used to access/use this model.
• Example generation(s)
• GGUF quants of this model

Please go to:

[[ coming soon || left side menu under "quantizations" ]]