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Model: GuminiResearch/Gumini-1.5B-Base-i1-GGUF Source: Original Platform
2026-09-03 15:20:18 +08:00
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+---
+license: other
+license_name: qwen-research
+license_link: https://huggingface.co/Qwen/Qwen2.5-3B/blob/main/LICENSE
+library_name: gguf
+language:
+- ko
+- en
+tags:
+- text-generation
+- korean
+- bilingual
+- qwen2
+- built-with-qwen
+- inheritune
+- gguf
+- llama-cpp
+- quantized
+- imatrix
+base_model: GuminiResearch/Gumini-1.5B-Base
+quantized_by: Gumin Kwon
+pipeline_tag: text-generation
+---
+
+# 🐻 Gumini-1.5B-Base-i1-GGUF (구미니)
+
+<p align="center">
+  <img src="https://img.shields.io/badge/Parameters-1.54B-blue" style="display:inline-block; margin-right:6px;" />
+  <img src="https://img.shields.io/badge/Layers-16-green" style="display:inline-block; margin-right:6px;" />
+  <img src="https://img.shields.io/badge/Format-GGUF-orange" style="display:inline-block; margin-right:6px;" />
+  <img src="https://img.shields.io/badge/Quantization-imatrix-purple" style="display:inline-block; margin-right:6px;" />
+  <img src="https://img.shields.io/badge/Built%20with-Qwen-purple" style="display:inline-block; margin-right:6px;" />
+</p>
+
+<p align="center">
+  <a href="https://linkedin.com/in/devgumin" target="_blank">
+    <img src="https://img.shields.io/badge/LinkedIn-Gumin%20Kwon-0A66C2?logo=linkedin&logoColor=white" style="display:inline-block; margin-right:6px;" />
+  </a>
+
+  <a href="https://x.com/Gumini_Research" target="_blank">
+    <img src="https://img.shields.io/badge/X-@Gumini__Research-black?logo=x&logoColor=white" style="display:inline-block; margin-right:6px;" />
+  </a>
+
+  <a href="https://www.instagram.com/gumini_research/" target="_blank">
+    <img src="https://img.shields.io/badge/Instagram-gumini__research-E4405F?logo=instagram&logoColor=white" style="display:inline-block;" />
+  </a>
+</p>
+
+
+<p align="center"><b>Built with Qwen</b></p>
+
+## Model Description
+
+GGUF quantized versions of [GuminiResearch/Gumini-1.5B-Base](https://huggingface.co/GuminiResearch/Gumini-1.5B-Base) for use with [llama.cpp](https://github.com/ggerganov/llama.cpp) and compatible tools (Ollama, LM Studio, etc.).
+
+All quantizations were created using **importance matrix (imatrix)** calibration for optimal quality preservation.
+
+> This is a **BASE model**, not instruction-tuned.  
+> It produces text continuations rather than conversational responses.
+
+## Model Details
+
+| Attribute | Value |
+|-----------|-------|
+| **Original Model** | [Gumini-1.5B-Base](https://huggingface.co/GuminiResearch/Gumini-1.5B-Base) |
+| **Quantized by** | [Gumin Kwon (권구민)](https://linkedin.com/in/devgumin) |
+| **Parameters** | 1.54B |
+| **Layers** | 16 |
+| **Hidden Size** | 2048 |
+| **Base PPL (F16)** | 8.48 |
+
+## Quantization Results
+
+### Perplexity Comparison
+
+![PPL Comparison](ppl_comparison.png)
+
+### PPL vs Size Trade-off
+
+![PPL vs Size](ppl_vs_size.png)
+
+### Recommended Quantizations
+
+| Quant | PPL | Size | PPL Δ | Quality | Use Case |
+|-------|-----|------|-------|---------|----------|
+| **Q8_0** | 8.50 | 1.5G | +0.02 | Excellent | Maximum quality |
+| **Q6_K** | 8.52 | 1.2G | +0.04 | Excellent | High quality |
+| **Q5_K_M** | 8.61 | 1.1G | +0.13 | Excellent | Balanced (recommended) |
+| **Q4_K_M** | 8.72 | 956M | +0.24 | Very Good | Size optimized |
+| **IQ4_XS** | 8.79 | 876M | +0.31 | Very Good | imatrix 4-bit |
+| **IQ3_M** | 9.09 | 770M | +0.61 | Good | Mobile/Edge |
+
+### All Quantization Results
+
+![Quantization Results](quantization_results.png)
+
+## Comparison: 1B vs 1.5B
+
+| Model | Layers | Params | PPL (F16) | Improvement |
+|-------|--------|--------|-----------|-------------|
+| Gumini 1B | 10 | 1.08B | 15.36 | - |
+| **Gumini 1.5B** | 16 | 1.54B | **8.48** | **45% better** |
+
+The 1.5B model shows significant quality improvement with only 6 additional layers!
+
+## Usage
+
+### With llama.cpp
+
+```bash
+# Download
+huggingface-cli download GuminiResearch/Gumini-1.5B-Base-i1-GGUF Gumini-1.5B-Base.i1-Q4_K_M.gguf
+
+# Run
+./llama-cli -m Gumini-1.5B-Base.i1-Q4_K_M.gguf -p "저는 구미니입니다." -n 100
+```
+
+### With Ollama
+
+```bash
+echo 'FROM ./Gumini-1.5B-Base.i1-Q4_K_M.gguf' > Modelfile
+ollama create gumini-1.5b -f Modelfile
+ollama run gumini-1.5b
+```
+
+### With LM Studio
+
+1. Download any `.gguf` file from this repo
+2. Import into LM Studio
+3. Start generating!
+
+## Quantization Guide
+
+![Quantization Types Guide](quant_types_guide.png)
+
+### Tips
+- **Best quality**: Use Q8_0 or Q6_K
+- **Balanced**: Use Q5_K_M or Q4_K_M
+- **Mobile/Edge**: Use IQ4_XS or IQ3_M
+- **"i1" prefix**: Indicates imatrix was used during quantization
+
+## Original Model
+
+**Gumini-1.5B** (구미니) is a bilingual Korean-English base language model trained using the *Inheritune* methodology. Starting from **Qwen 2.5 3B**, the model progressively grew from 10 to 16 layers through 7 training stages.
+
+### Inheritune Progressive Layer Growing
+```
+Stage 0: 10 layers (1.08B) → 393M tokens
+Stage 1: 11 layers (1.15B) → 393M tokens
+Stage 2: 12 layers (1.23B) → 393M tokens
+Stage 3: 13 layers (1.31B) → 393M tokens
+Stage 4: 14 layers (1.39B) → 393M tokens
+Stage 5: 15 layers (1.47B) → 393M tokens
+Stage 6: 16 layers (1.54B) → 786M tokens ⭐
+────────────────────────────────────────────
+Total: 16 layers, 1.54B params, ~3.14B tokens
+```
+
+- **Training Data**: 80% Korean, 20% English
+
+See [GuminiResearch/Gumini-1.5B-Base](https://huggingface.co/GuminiResearch/Gumini-1.5B-Base) for full details.
+
+## License
+
+### Qwen Research License (Non-Commercial)
+
+This model is **Built with Qwen** and derived from Qwen 2.5 3B.
+
+```
+Qwen is licensed under the Qwen RESEARCH LICENSE AGREEMENT.
+Copyright (c) Alibaba Cloud. All Rights Reserved.
+```
+
+**This model is for NON-COMMERCIAL / RESEARCH use only.**  
+For commercial use, contact Alibaba Cloud.
+
+## References
+
+### Inheritune Paper
+```bibtex
+@inproceedings{Sanyal2024inheritune,
+  title={Inheritune: Training Smaller Yet More Attentive Language Models},
+  author={Sunny Sanyal and Ravid Shwartz-Ziv and Alexandros G. Dimakis and Sujay Sanghavi},
+  year={2024},
+  url={https://arxiv.org/abs/2404.08634}
+}
+```
+
+### Qwen 2.5
+```bibtex
+@misc{qwen2.5,
+  title={Qwen2.5: A Party of Foundation Models},
+  author={Qwen Team},
+  year={2024},
+  url={https://qwenlm.github.io/blog/qwen2.5/}
+}
+```
+
+## Citation
+
+```bibtex
+@misc{gumini2025,
+  title={Gumini-1.5B: Bilingual Korean-English Language Model via Inheritune},
+  author={Gumin Kwon},
+  year={2025},
+  note={Built with Qwen. Trained with Inheritune progressive layer growing.},
+  url={https://huggingface.co/GuminiResearch/Gumini-1.5B-Base-i1-GGUF}
+}
+```
+
+## Author
+
+**Gumin Kwon (권구민)**  
+- 🔗 LinkedIn: https://linkedin.com/in/devgumin  
+- 🤗 Hugging Face: https://huggingface.co/GuminiResearch  
+- 𝕏 X (Twitter): https://x.com/Gumini_Research  
+- 📸 Instagram: https://www.instagram.com/gumini_research/
+
+---
+
+<p align="center">
+  <b>Built with Qwen</b><br>
+  <i>Gumini - 작지만 똑똑한 AI</i>
+</p>
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