oomllama 1.0.0a2

Sovereign AI Inference Engine — .oom quantization, Trust Kernel encryption, JIS identity routing, TIBET provenance. Spaceshuttle lazy loading, RAM-RAID distributed memory.

🦙 OomLlama

Efficient LLM inference with .oom format - 2x smaller than GGUF

from oomllama import OomLlama

llm = OomLlama("humotica-32b")
response = llm.generate("What is the meaning of life?")
print(response)

Why OomLlama?

Feature	GGUF (Q4)	OOM (Q2)
70B Model Size	~40 GB	~20 GB
32B Model Size	~20 GB	~10 GB
RAM Usage	High	Lazy Loading
Format	Open	Open (MIT)

OomLlama uses Q2 quantization with lazy layer loading to run large models on consumer hardware.

Installation

pip install oomllama

Quick Start

Download a Model

from oomllama import download_model

# Download from HuggingFace
model_path = download_model("humotica-32b")

Generate Text

from oomllama import OomLlama

llm = OomLlama("humotica-32b")

# Simple generation
response = llm.generate("Explain quantum computing in simple terms")
print(response)

# With parameters
response = llm.generate(
    "Write a haiku about AI",
    max_tokens=50,
    temperature=0.8,
    top_p=0.9
)

Chat Mode

messages = [
    ("user", "Hello! Who are you?"),
    ("assistant", "I'm OomLlama, an efficient LLM."),
    ("user", "What makes you efficient?"),
]

response = llm.chat(messages)
print(response)

Available Models

Model	Parameters	Size (.oom)	HuggingFace
humotica-32b	33B	~10 GB	Link
llamaohm-70b	70B	~20 GB	Link
tinyllama-1b	1.1B	~400 MB	Link

The .oom Format

OOM (OomLlama Model) is a compact model format:

┌──────────────────────────────────────┐
│ Header: OOML (magic) + metadata      │
├──────────────────────────────────────┤
│ Tensors: Q2 quantized (2 bits/weight)│
│ - Scale + Min per 256-weight block   │
│ - 68 bytes per block                 │
└──────────────────────────────────────┘

Convert GGUF to OOM

# Using the CLI tool
gguf2oom model.gguf model.oom

# Check model info
gguf2oom --info model.gguf

Technical Details

Q2 Quantization

Each weight is stored as 2 bits (0, 1, 2, or 3) with per-block scale and minimum:

weight = q2_value * scale + min

This achieves ~2x compression over Q4 with acceptable quality loss for most tasks.

Lazy Layer Loading

OomLlama loads transformer layers on-demand, keeping only the active layer in memory:

Forward Pass:
  Layer 0: Load → Compute → Unload
  Layer 1: Load → Compute → Unload
  ...
  Layer N: Load → Compute → Unload

This enables running 70B models on 24GB GPU RAM.

Credits

• Model Format: Gemini IDD & Root AI (Humotica AI Lab)
• Quantization: OomLlama.rs by Humotica
• Base Models: Meta Platforms, Inc. (Llama 3.3)

License

• OomLlama Code: MIT License
• Model Weights: Subject to original model licenses (e.g., Llama 3.3 Community License)

Links

• 🏠 Humotica
• 🤗 HuggingFace Models
• 📦 PyPI Package
• 🐛 Issue Tracker

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One Love, One fAmIly 💙

Built by Humotica AI Lab - Jasper, Claude, Gemini, Codex

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Enterprise

For private hub hosting, SLA support, custom integrations, or compliance guidance:

Enterprise	[e-mail]
Support	[e-mail]
Security	[e-mail]

See ENTERPRISE.md for details.