--- base_model: google/gemma-3-1b-it tags: - gemma3 - instruct - 32k context - all use cases - maxed quants - Neo Imatrix license: apache-2.0 pipeline_tag: text-generation --- (quants uploading, examples to be added)

Gemma-3-1b-it-MAX-NEO-Imatrix-GGUF

Google's newest Gemma-3 model with "Neo Imatrix" and "Maxed out" quantization to improve overall performance. "MAXED" This means the embed and output tensor are set at "BF16" (full precision) for all quants. This enhances quality, depth and general performance at the cost of a slightly larger quant. "NEO IMATRIX" A strong, in house built, imatrix dataset built by David_AU which results in better overall function, instruction following, output quality and stronger connections to ideas, concepts and the world in general. This combines with "MAXing" the quant to improve preformance. This chart shows the order in terms of "BPW" for each quant (mapped below with relative "strength" to one another) with "IQ1_S" with the least, and "Q8_0" (F16 is full precision) with the most: 
IQ1_S 	| IQ1_M
IQ2_XXS | IQ2_XS | Q2_K_S 	| IQ2_S 	| Q2_K  	| IQ2_M		
IQ3_XXS | Q3_K_S | IQ3_XS  	| IQ3_S 	| IQ3_M	    | Q3_K_M	| Q3_K_L
Q4_K_S	| IQ4_XS | IQ4_NL  	| Q4_K_M	
Q5_K_S	| Q5_K_M	
Q6_K  
Q8_0
F16
Recommend quants IQ3s / IQ4XS / IQ4NL / Q4s for best results for creative. IQ4XS/IQ4NL quants will produce different output from other "Q" and "IQ" quants. Recommend q5s/q6/q8 for general usage. Q8 is a maxed quant only, as imatrix has no effect on this quant. Note that IQ1s performance is low, whereas IQ2s are passable. More information on quants is in the document below "Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers". Optional : System Prompt This is an optional system prompt you can use to enhance operation. Copy and paste exactly as shown, including line breaks. You may want to adjust the "20" (both) to increase/decrease the power of this prompt. You may also want to delete the line: 'At the end of the task you will ask the user: "Do you want another generation?"' 
For every user task and instruction you will use "GE FUNCTION" to ponder the TASK STEP BY STEP and then do the task. For each and every line of output you will ponder carefully to ensure it meets the instructions of the user, and if you are unsure use "GE FUNCTION" to re-ponder and then produce the improved output.

At the end of the task you will ask the user: "Do you want another generation?"

GE FUNCTION: Silent input → Spawn 20 agents Sternberg Styles → Enhance idea → Seek Novel Emergence NE:unique/significant idea/concept → Ponder, assess, creative enhance notions → Refined idea => IdeaArray[].size=20 elements,  else → Interesting? Pass to rand. agent for refinement, else discard.=>output(IdeaArray)
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 will also link to a "How to" section on "Reasoning Models" tips and tricks too. 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 ] ---

EXAMPLES:

Examples are created using quant IQ4XS, minimal parameters and Standard template. Temp range .8, Rep pen 1.1 , TopK 40 , topP .95, minP .05 Rep pen range: 64-128 (helps keep reasoning on track / quality of output) Below are the least creative outputs, prompt is in BOLD. --- WARNING: MAYBE: NSFW. Graphic HORROR. Swearing. UNCENSORED. NOTE: Some formatting was lost from copy/paste HTML. ---