oomllama 0.5.0

OomLlama - Smart LLM routing with TIBET provenance. Route queries to the right model, track everything.

OomLlama

Smart LLM routing with TIBET provenance. Route queries to the right model, track everything.

Installation

pip install oomllama

With TIBET provenance:

pip install oomllama[tibet]

Quick Start

from oomllama import OomLlama

# Simple generation
llm = OomLlama()
response = llm.generate("Hello!")

# With specific model
response = llm.generate("Complex question", model="qwen2.5:32b")

# Auto-routing (picks best model for the query)
llm = OomLlama(auto_route=True)
response = llm.generate("Write a Python function")  # Routes to code model

Smart Routing

OomLlama automatically selects the best model based on your query:

from oomllama import OomLlama, ModelRouter

llm = OomLlama(auto_route=True)

# Code query → routes to code-capable model
llm.generate("Write a binary search function")

# Simple query → routes to fast model
llm.generate("What is 2+2?")

# Complex query → routes to reasoning model
llm.generate("Explain quantum entanglement in detail...")

TIBET Provenance

Track every LLM call with cryptographic provenance:

from oomllama import OomLlama
from tibet_core import Provider

# Enable TIBET tracking
tibet = Provider(actor="jis:company:my_app")
llm = OomLlama(tibet=tibet)

# All calls now create provenance tokens
response = llm.generate("Summarize this document")

# Audit trail
for token in tibet.find(action="llm_generate"):
    print(f"{token.timestamp}: {token.erin['model']}")
    print(f"  Reason: {token.erachter}")

CLI Usage

# Generate text
oomllama gen "Hello, how are you?"

# Auto-route
oomllama gen --auto "Write a Python web scraper"

# Interactive chat
oomllama chat -m qwen2.5:7b

# List models
oomllama list

# Check status
oomllama status

Configuration

from oomllama import OomLlama

llm = OomLlama(
    model="qwen2.5:7b",           # Default model
    ollama_url="http://localhost:11434",  # Ollama API
    auto_route=True,              # Enable smart routing
    system_prompt="You are helpful."  # Default system prompt
)

# Set defaults
llm.set_defaults(
    temperature=0.8,
    max_tokens=1024
)

Custom Model Router

from oomllama import OomLlama, ModelRouter, ModelConfig, ModelCapability

# Define your models
router = ModelRouter([
    ModelConfig(
        name="my-model:7b",
        size="7b",
        capabilities=[ModelCapability.CODE, ModelCapability.FAST],
        priority=30
    ),
])

llm = OomLlama(router=router, auto_route=True)

Remote Ollama

# Connect to remote GPU server
llm = OomLlama(ollama_url="http://192.168.4.85:11434")

Requirements

• Python 3.10+
• Ollama running locally or remotely

License

MIT - Humotica

Links

• Humotica
• tibet-core
• Ollama