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---
license: apache-2.0
language:
- en
base_model:
- POLARIS-Project/Polaris-4B-Preview
pipeline_tag: text-generation
library_name: transformers
tags:
- math
- moe
- text-generation-inference
- polaris
---
# **Polaris-4B-Preview-F32-GGUF**
> 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 .
## Model Files
| File Name | Size | Format | Description |
|-----------|------|--------|-------------|
| Polaris-4B-Preview.F32.gguf | 16.1 GB | F32 | Full precision 32-bit floating point |
| Polaris-4B-Preview.F16.gguf | 8.05 GB | F16 | Half precision 16-bit floating point |
| Polaris-4B-Preview.BF16.gguf | 8.05 GB | BF16 | Brain floating point 16-bit |
| Polaris-4B-Preview.Q8_0.gguf | 4.28 GB | Q8_0 | 8-bit quantized |
| Polaris-4B-Preview.Q6_K.gguf | 3.31 GB | Q6_K | 6-bit quantized |
| Polaris-4B-Preview.Q5_K_M.gguf | 2.89 GB | Q5_K_M | 5-bit quantized, medium quality |
| Polaris-4B-Preview.Q5_K_S.gguf | 2.82 GB | Q5_K_S | 5-bit quantized, small quality |
| Polaris-4B-Preview.Q4_K_M.gguf | 2.5 GB | Q4_K_M | 4-bit quantized, medium quality |
| Polaris-4B-Preview.Q4_K_S.gguf | 2.38 GB | Q4_K_S | 4-bit quantized, small quality |
| Polaris-4B-Preview.Q3_K_L.gguf | 2.24 GB | Q3_K_L | 3-bit quantized, large quality |
| Polaris-4B-Preview.Q3_K_M.gguf | 2.08 GB | Q3_K_M | 3-bit quantized, medium quality |
| Polaris-4B-Preview.Q3_K_S.gguf | 1.89 GB | Q3_K_S | 3-bit quantized, small quality |
| Polaris-4B-Preview.Q2_K.gguf | 1.67 GB | Q2_K | 2-bit quantized |
## Quants Usage
(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
Here is a handy graph by ikawrakow comparing some lower-quality quant
types (lower is better):
![image.png](https://www.nethype.de/huggingface_embed/quantpplgraph.png)

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{
"model_type": "qwen3"
}

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{"framework": "pytorch", "task": "text-generation", "allow_remote": true}