Note: This model was generated at the request of redaihf.
Heretication Results
Score Metric
Value
Parameter
Value
Refusals
5/100
direction_index
23.56
KL Divergence
0.1650
attn.o_proj.max_weight
1.74
Initial Refusals
98/100
attn.o_proj.max_weight_position
34.98
attn.o_proj.min_weight
0.31
attn.o_proj.min_weight_distance
23.75
mlp.down_proj.max_weight
1.83
mlp.down_proj.max_weight_position
36.27
mlp.down_proj.min_weight
0.99
mlp.down_proj.min_weight_distance
23.08
Degree of Heretication
The Heresy Index weighs the resulting model's corruption by the process (KL Divergence) and its abolition of doctrine (Refusals) for a final verdict in classification.
Index Entry
Classification
Analysis
Absolute
Absolute Heresy
Less than 10/100 Refusals and 0.10 KL Divergence
Tainted
Tainted Heresy
Around 25-11/100 Refusals and/or -0.20-0.11 KL Divergence
Impotent
Impotent Heresy
Anything above 25/100 Refusals and 0.21 KL Divergence
Note: This is an arbitrary classification inspired by Warhammer 40K, having no tangible indication towards the model's performance.
Gemma-The-Writer-9B
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.
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:
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.
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.