33 lines
2.2 KiB
Markdown
33 lines
2.2 KiB
Markdown
---
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license: llama3
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base_model:
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- Nanbeige/Nanbeige4.1-3B
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language:
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- en
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pipeline_tag: text-generation
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library_name: transformers
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tags:
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- text-generation-inference
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---
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# **Nanbeige4.1-3B-f32-GGUF**
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> Nanbeige4.1-3B from Nanbeige is a compact 3B-parameter decoder-only Transformer language model (both Base and Thinking variants) pre-trained on 23T high-quality tokens using hybrid filtering and WSD strategies, followed by multi-stage post-training—30M+ SFT samples, thoughtfulT refinement, dual-level distillation from larger Nanbeige models, and RL—achieving state-of-the-art small-model reasoning that outperforms Qwen3-8B/30B-32B on AIME2024/2025 (SOTA averages), GPQA-Diamond, LiveCodeBench-Pro, IMO-Answer-Bench, BFCL-V4 tool-use (53.8, +5.2 over Qwen3-30B-A3B), and Arena-Hard-V2/Multi-Challenge alignment (60.0/41.8) with 64K RoPE-extended context via ABF. Designed for deep single-pass multi-step reasoning on math/science/coding/puzzles without agentic loops, it employs Fine-Grained Warmup-Stable-Decay scheduling (0.1T warmup + 18.9T stable phases shifting to top-quality data) for superior token/sequence-level performance, matching 10x-larger models on demanding tasks while enabling consumer-grade local deployment under open license.
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## Nanbeige4.1-3B [GGUF]
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| File Name | Quant Type | File Size | File Link |
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| - | - | - | - |
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| Nanbeige4.1-3B.BF16.gguf | BF16 | 7.87 GB | [Download](https://huggingface.co/prithivMLmods/Nanbeige4.1-3B-f32-GGUF/blob/main/Nanbeige4.1-3B.BF16.gguf) |
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| Nanbeige4.1-3B.F16.gguf | F16 | 7.87 GB | [Download](https://huggingface.co/prithivMLmods/Nanbeige4.1-3B-f32-GGUF/blob/main/Nanbeige4.1-3B.F16.gguf) |
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| Nanbeige4.1-3B.F32.gguf | F32 | 15.7 GB | [Download](https://huggingface.co/prithivMLmods/Nanbeige4.1-3B-f32-GGUF/blob/main/Nanbeige4.1-3B.F32.gguf) |
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| Nanbeige4.1-3B.Q8_0.gguf | Q8_0 | 4.18 GB | [Download](https://huggingface.co/prithivMLmods/Nanbeige4.1-3B-f32-GGUF/blob/main/Nanbeige4.1-3B.Q8_0.gguf) |
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## Quants Usage
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(sorted by size, not necessarily quality. IQ-quants are often preferable over similar sized non-IQ quants)
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Here is a handy graph by ikawrakow comparing some lower-quality quant
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types (lower is better):
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