commit f8c2e6ec13d11528347ccd3b2c0caed2ca715c4c Author: ModelHub XC Date: Fri Oct 16 19:18:17 2026 +0800 初始化项目，由ModelHub XC社区提供模型 Model: prithivMLmods/Polaris-4B-Preview-F32-GGUF Source: Original Platform diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..53d7257 --- /dev/null +++ b/.gitattributes @@ -0,0 +1,47 @@ +*.7z filter=lfs diff=lfs merge=lfs -text +*.arrow filter=lfs diff=lfs merge=lfs -text +*.bin filter=lfs diff=lfs merge=lfs -text +*.bin.* filter=lfs diff=lfs merge=lfs -text +*.bz2 filter=lfs diff=lfs merge=lfs -text +*.ftz filter=lfs diff=lfs merge=lfs -text +*.gz filter=lfs diff=lfs merge=lfs -text +*.h5 filter=lfs diff=lfs merge=lfs -text +*.joblib filter=lfs diff=lfs merge=lfs -text +*.lfs.* filter=lfs diff=lfs merge=lfs -text +*.model filter=lfs diff=lfs merge=lfs -text +*.msgpack filter=lfs diff=lfs merge=lfs -text +*.onnx filter=lfs diff=lfs merge=lfs -text +*.ot filter=lfs diff=lfs merge=lfs -text +*.parquet filter=lfs diff=lfs merge=lfs 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+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) \ No newline at end of file diff --git a/config.json b/config.json new file mode 100644 index 0000000..d1b8b99 --- /dev/null +++ b/config.json @@ -0,0 +1,3 @@ +{ + "model_type": "qwen3" +} \ No newline at end of file diff --git a/configuration.json b/configuration.json new file mode 100644 index 0000000..bbeeda1 --- /dev/null +++ b/configuration.json @@ -0,0 +1 @@ +{"framework": "pytorch", "task": "text-generation", "allow_remote": true} \ No newline at end of file