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GuminiResearch/
Gumini-1.5B-Base-i1-GGUF

Name: Gumini-1.5B-Base-i1-GGUF
Availability: InStock

GuminiResearch/Gumini-1.5B-Base

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🐻 Gumini-1.5B-Base-i1-GGUF (구미니)

Built with Qwen

Model Description

GGUF quantized versions of GuminiResearch/Gumini-1.5B-Base for use with 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
Quantized by	Gumin Kwon (권구민)
Parameters	1.54B
Layers	16
Hidden Size	2048
Base PPL (F16)	8.48

Quantization Results

Perplexity Comparison

PPL vs Size Trade-off

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

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

Download any .gguf file from this repo
Import into LM Studio
Start generating!

Quantization Guide

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 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/

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