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.
This model contains Brainstorm 20x, combined with POLARIS-Project's 4B General / Coder (instruct model):
https://huggingface.co/POLARIS-Project/Polaris-4B-Preview
Information on the 4B model below, followed by Brainstorm 20x adapter (by DavidAU) and then a complete help section for running LLM / AI models.
The Brainstorm adapter improves code generation, and unique code solving abilities.
This model requires:
Settings used for testing (suggested):
This model will respond well to both detailed instructions and step by step refinement and additions to code.
As this is an instruct model, it will also benefit from a detailed system prompt too.
For simpler coding problems, lower quants will work well; but for complex/multi-step problem solving suggest Q6 or Q8.
Polaris is an open-source post-training method that uses reinforcement learning (RL) scaling to refine and enhance models with advanced reasoning abilities. Our research shows that even top-tier models like Qwen3-4B can achieve significant improvements on challenging reasoning tasks when optimized with Polaris. By leveraging open-source data and academic-level resources, Polaris pushes the capabilities of open-recipe reasoning models to unprecedented heights. In benchmark tests, our method even surpasses top commercial systems, including Claude-4-Opus, Grok-3-Beta, and o3-mini-high (2025/01/03).
The details of our training recipe and analysis can be found in our blog post. The code and data for reproducing our results can be found in our github repo.
| Models | AIME24 avg@32 | AIME25 avg@32 | Minerva Math avg@4 | Olympiad Bench avg@4 | AMC23 avg@8 |
|---|---|---|---|---|---|
| DeepScaleR-1.5B | 43.1 | 27.2 | 34.6 | 40.7 | 50.6 |
| Qwen3-1.7B | 48.3 | 36.8 | 34.9 | 55.1 | 75.6 |
POLARIS-1.7B-Preview |
66.9 | 53.0 | 38.9 | 63.8 | 85.8 |
| Deepseek-R1-Distill-Qwen-7B | 55.0 | 39.7 | 36.7 | 56.8 | 81.9 |
| AReal-boba-RL-7B | 61.9 | 48.3 | 39.5 | 61.9 | 86.4 |
| Skywork-OR1-7B-Math | 69.8 | 52.3 | 40.8 | 63.2 | 85.3 |
POLARIS-7B-Preview |
72.6 | 52.6 | 40.2 | 65.4 | 89.0 |
| Deepseek-R1-Distill-Qwen-32B | 72.6 | 54.9 | 42.1 | 59.4 | 84.3 |
| qwen3-32B | 81.4 | 72.9 | 44.2 | 66.7 | 92.4 |
| qwen3-4B | 73.8 | 65.6 | 43.6 | 62.2 | 87.2 |
POLARIS-4B-Preview |
81.2 | 79.4 | 44.0 | 69.1 | 94.8 |
The training and evaluation codebase is heavily built on Verl. The reward function in polaris is from DeepScaleR. Our model is trained on top of Qwen3-4B and DeepSeek-R1-Distill-Qwen-7B. Thanks for their wonderful work.
See more here:
https://huggingface.co/POLARIS-Project/Polaris-4B-Preview
Brainstorm 20x
The BRAINSTORM process was developed by David_AU.
Some of the core principals behind this process are discussed in this scientific paper : Progressive LLaMA with Block Expansion .
However I went in a completely different direction from what was outlined in this paper.
What is "Brainstorm" ?
The reasoning center of an LLM is taken apart, reassembled, and expanded.
In this case for this model: 20 times
Then these centers are individually calibrated. These "centers" also interact with each other. This introduces subtle changes into the reasoning process. The calibrations further adjust - dial up or down - these "changes" further. The number of centers (5x,10x etc) allow more "tuning points" to further customize how the model reasons so to speak.
The core aim of this process is to increase the model's detail, concept and connection to the "world", general concept connections, prose quality and prose length without affecting instruction following.
This will also enhance any creative use case(s) of any kind, including "brainstorming", creative art form(s) and like case uses.
Here are some of the enhancements this process brings to the model's performance:
Other "lab" observations:
The process to modify the model occurs at the root level - source files level. The model can quanted as a GGUF, EXL2, AWQ etc etc.
For more information / other Qwen/Mistral Coders / additional settings see:
[ https://huggingface.co/DavidAU/Qwen2.5-MOE-2x-4x-6x-8x__7B__Power-CODER__19B-30B-42B-53B-gguf ]
CHANGE THE NUMBER OF ACTIVE EXPERTS:
See this document:
https://huggingface.co/DavidAU/How-To-Set-and-Manage-MOE-Mix-of-Experts-Model-Activation-of-Experts
Settings: CHAT / ROLEPLAY and/or SMOOTHER operation of this model:
In "KoboldCpp" or "oobabooga/text-generation-webui" or "Silly Tavern" ;
Set the "Smoothing_factor" to 1.5
: in KoboldCpp -> Settings->Samplers->Advanced-> "Smooth_F"
: in text-generation-webui -> parameters -> lower right.
: In Silly Tavern this is called: "Smoothing"
NOTE: For "text-generation-webui"
-> if using GGUFs you need to use "llama_HF" (which involves downloading some config files from the SOURCE version of this model)
Source versions (and config files) of my models are here:
OTHER OPTIONS:
Increase rep pen to 1.1 to 1.15 (you don't need to do this if you use "smoothing_factor")
If the interface/program you are using to run AI MODELS supports "Quadratic Sampling" ("smoothing") just make the adjustment as noted.
Highest Quality Settings / Optimal Operation Guide / Parameters and Samplers
This a "Class 1" 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) please see:
You can see all parameters used for generation, in addition to advanced parameters and samplers to get the most out of this model here:
