NousResearch's newest Llama 3.2 Reasoning/Thinking model with "Neo Imatrix" and "Maxed out" quantization to improve overall performance.
Combined with Llama 3.2's superior instruction folllowing and output generation this makes a reasoning/thinking model in a tiny package that far outperforms and closes in on 8B+ reasoning model size performance.
5 examples provided below with prompts at IQ4XS (80 t/s on mid level card) ; Q8 at 55 t/s.
Context: 128k.
"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:
IMPORTANT:
Reasoning / thinking skills are DIRECTLY related to quant size. However, there will be drastic difference in Token/Second between the lowest quant and highest quant, so finding the right balance is key.
Suggest also: minimum 8k context window, especially for IQ4/Q4 or lower quants.
Also, in some cases, the IQ quants work slightly better than they closest "Q" quants.
Recommend quants IQ3s / IQ4XS / IQ4NL / Q4s for best results for creative uses cases.
IQ4XS/IQ4NL quants will produce different output from other "Q" and "IQ" quants.
Recommend q5s/q6/q8 for general usage.
Quants Q4_0/Q5_0 for portable, phone and other devices.
Q8 is a maxed quant only, as imatrix has no effect on this quant.
Use this quant or F16 (full precision) for MAXIMUM reasoning/thinking performance.
Note that IQ1s performance is low, whereas IQ2s are passable (but reasoning is reduced, try IQ3s min for reasoning cases)
More information on quants is in the document below "Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers".
Benchmarks / More Information:
For benchmarks and other information about this model, see the original source repo here:
[ https://huggingface.co/NousResearch/DeepHermes-3-Llama-3-3B-Preview ]
System Prompt
Use this system prompt to turn on/off reasoning in the model:
You are a deep thinking AI, you may use extremely long chains of thought to deeply consider the problem and deliberate with yourself via systematic reasoning processes to help come to a correct solution prior to answering. You should enclose your thoughts and internal monologue inside <think> </think> tags, and then provide your solution or response to the problem.
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?"'
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:
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.
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.