JallyAI/Nomi-2-Mini-GGUF overview
<p align="center" <img src="https://cdn uploads.huggingface.co/production/uploads/6921fa6332f7fb129563d495/aR36SrpWzksbcGbcp84pE.png" width="128" </p Nomi 2.0 …
Runs locally from ~637.3 MB disk (4 GB VRAM class GPUs with llama.cpp / guIDE).
Repository Files & Downloads
Model Details
| Model ID | JallyAI/Nomi-2-Mini-GGUF |
|---|---|
| Author | JallyAI |
| Pipeline | image-text-to-text |
| License | apache-2.0 |
| Base model | JallyAI/Nomi-2-Mini |
| Last modified | 2026-08-16T09:28:08.000Z |
Model README
---
license: apache-2.0
base_model:
- JallyAI/Nomi-2-Mini
pipeline_tag: image-text-to-text
library_name: transformers
tags:
- efficient
- qwen
- qwen3.5
- nomi
- lazyloopstudio
- unsloth
- nomi2
---
<p align="center">
<img src="https://cdn-uploads.huggingface.co/production/uploads/6921fa6332f7fb129563d495/aR36SrpWzksbcGbcp84pE.png" width="128">
</p>
Nomi 2.0 Mini
Introduction
Introducing Nomi 2 Mini, it was fine tuned on the same data as Nomi 2 and has a very short and efficient reasoning thanks to the RASV reasoning style. Nomi 2 Mini has only 2B parameters, half the parameters of the normal Nomi 2.
If you want to know more about Nomi 2 or RASV, checkout the Nomi 2 model card
https://huggingface.com/JallyAI/Nomi-2
🌟 Key Features & Improvements
- Architecture: Qwen-3.5-2B (requires just ~1.5 GB VRAM).
- Multilingual Support: Can understand and generate text English and many other languages.
- Efficiency: Get 100+ tokens/s on consumer hardware, like an RTX 4060. You can use Nomi 2 Mini with an context window of almost 200k tokens
🧠 Training Details
- Base Model:
Qwen/Qwen3.5-2B - Fine-tuning: SFT (Supervised Fine-Tuning).
- Training Tool: Unsloth (for 4-bit optimized training).
😎 Cool License
Feel free to use or improve Nomi! Benchmark results are always welcome.
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Run JallyAI/Nomi-2-Mini-GGUF with guIDE
Download guIDE — the AI-native code editor with local LLM inference and 69 built-in tools.
Source: Hugging Face · Compare models