Kolyadual/rumeni-1-nano

Rumeni 1 nano

🚀 Technical Highlights

• Frankenstein Merge Architecture - Modular component integration
• Memory-Efficient - Runs on 16GB RAM with CPU inference
• GGUF Compatible - Can be quantized to Q5_K_M (~4-6GB)
• Extensible - Easy to add new LoRA adapters
• Privacy-First - All processing happens locally

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Component Breakdown

Component	Weight	Source	Purpose
vision_tower	100%	Google Gemma 4 E2B	Image processing
audio_tower	100%	Google Gemma 4 E2B	Audio processing
embed_tokens	100%	Google Gemma 4 E2B	Token embeddings
language_model.layers	60% Rumeni + 40% Opus	HuiHui + Claude Opus	Text generation + reasoning
language_model.norm	70% Rumeni + 30% Opus	HuiHui + Claude Opus	Layer normalization
lm_head	50% Rumeni + 50% Opus	HuiHui + Claude Opus	Output projection

1. Feature Comparison

Feature	Google Gemma 4 E2B	HuiHui Abliterated	Opus Reasoning	Rumeni	GPT-4o	Gemini Pro
Vision (Images)	✅	❌	❌	✅	✅	✅
Audio Processing	✅	❌	❌	✅	✅	✅
Uncensored Text	❌	✅		✅	❌	❌
Advanced Reasoning	⭐⭐⭐	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐
Russian Language	⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
Local Deployment	✅	✅	✅	✅	❌	❌
Privacy	✅	✅	✅	✅		❌
Free & Open Source	✅	✅	✅	✅	❌	❌
Model Size	~10 GB	~10 GB	~10 GB	~10 GB	N/A	N/A
Hardware Requirements	16GB RAM	16GB RAM	16GB RAM	16GB RAM	Cloud	Cloud

2. Performance Benchmarks

Tested on Intel N100 (Beelink mini PC, 16GB RAM, CPU-only inference)

Task	Google Gemma 4	HuiHui	Opus	Rumeni	GPT-4o (API)
Text Generation (512 tokens)	42s	45s	48s	47s	~2s
Image Analysis	58s	N/A	N/A	61s	~3s
Audio Transcription (1 min)	85s	N/A	N/A	92s	~5s
Math Problem (Complex)	28s	31s	22s	25s	~1s
Code Generation	38s	41s	35s	40s	~2s
Reasoning (Logic)	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
RAM Usage	12 GB	12 GB	13 GB	14 GB	N/A
Disk Space	10 GB	10 GB	10 GB	10 GB	N/A

3. Capability Comparison

Capability	Google Gemma 4 E2B	HuiHui Abliterated	Opus Reasoning	Rumeni	LLaVA-1.6	Claude 3.5 Sonnet
Multimodal Input	✅ Vision + Audio	Text only	❌ Text only	✅ Vision + Audio	✅ Vision only	✅ Vision only
Content Restrictions	High	None	High	None	Medium	High
Step-by-Step Reasoning	Basic	Basic	Advanced	Advanced	Basic	Advanced
Creative Writing	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐
Mathematical Accuracy	72%	68%	89%	85%	65%	92%
Code Generation	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐
Russian Proficiency	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐	⭐⭐⭐
Offline Operation	✅	✅	✅	✅	✅	❌
Customizable	✅	✅	✅	✅	✅	❌

4. Component Breakdown

Component	Weight	Source	Purpose
vision_tower	100%	Google Gemma 4 E2B	Image processing
audio_tower	100%	Google Gemma 4 E2B	Audio processing
embed_tokens	100%	Google Gemma 4 E2B	Token embeddings
language_model.layers	60% Rumeni + 40% Opus	HuiHui + Claude Opus	Text generation + reasoning
language_model.norm	70% Rumeni + 30% Opus	HuiHui + Claude Opus	Layer normalization
lm_head	50% Rumeni + 50% Opus	HuiHui + Claude Opus	Output projection

5. Use Case Comparison

Use Case	Google Gemma 4	HuiHui	Opus	Rumeni	Best For
Personal Assistant	⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐	Rumeni - all-in-one
Content Creation	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	HuiHui/Rumeni - uncensored
Math & Logic	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Opus/Rumeni - reasoning
Image Analysis	⭐⭐⭐⭐⭐	❌	❌	⭐⭐⭐⭐	Google/Rumeni - vision
Audio Transcription	⭐⭐⭐⭐⭐	❌	❌	⭐⭐⭐⭐⭐	Google/Rumeni - audio
Code Generation	⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	Opus/Rumeni - code