tessera-hypernetwork 0.2.5

Generate per-session LoRA adapters for inference tasks

Tessera Hypernetwork

Generate per-session LoRA adapters for inference tasks. This is the Python hypernetwork service component of Tessera, which works alongside the Rust core to provide LoRA adapter generation via hypernetwork synthesis.

Features

• Doc-to-LoRA with SHINE: Generate adapters from document content using SHINE (ICML 2026) for long-context internalization
• Text-to-LoRA: Generate adapters from natural language descriptions
• Metadata-to-LoRA: Generate adapters from structured user metadata
• OpenAI-compatible API: Easy integration with existing tooling
• FastAPI: Modern async Python web framework

Installation

pip install tessera-hypernetwork

Usage

CLI Commands

The tessera CLI provides commands for generating LoRA adapters and running the hypernetwork server:

# Generate LoRA adapter from metadata
tessera generate --from-metadata '{"task": "classification", "domain": "medical"}' \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Generate LoRA adapter from text description
tessera generate --from-text "Senior litigation associate specializing in IP law" \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Generate LoRA adapter from document
tessera generate --from-doc ./document.txt \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Start the hypernetwork server
tessera serve --port 8080 --host 0.0.0.0

# Start server with Qdrant vector database
tessera serve --port 8080 --qdrant-url http://localhost:6333

# Check server health
tessera health --url http://localhost:8000

# List available base models
tessera list

Server Mode

You can also run the server directly:

python -m tessera_hypernetwork.server

API

The hypernetwork service provides a FastAPI server with the following endpoints:

• POST /v1/generate - Generate a LoRA adapter for a given prompt
• GET /health - Health check endpoint

Development

Install development dependencies:

pip install tessera-hypernetwork[dev]

Run tests:

pytest

Integration with Tessera

This hypernetwork service is designed to work with the Tessera Rust core. The Rust core handles semantic caching, vector similarity search, and adapter composition, while this Python service handles the actual LoRA adapter generation via hypernetwork synthesis.

Full Tessera CLI Lifecycle

The Tessera hypernetwork service provides a comprehensive CLI for LoRA adapter generation and serving:

# Generate LoRA adapter from metadata (JSON string or file)
tessera generate --from-metadata '{"task": "classification", "domain": "medical"}' \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Generate LoRA adapter from natural language description
tessera generate --from-text "Senior litigation associate specializing in IP law" \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Generate LoRA adapter from document content
tessera generate --from-doc ./document.txt \
  --base-model meta-llama/Llama-3-8B \
  --rank 16 \
  --save ./adapter.safetensors

# Start the hypernetwork server
tessera serve --port 8080 --host 0.0.0.0

# Start server with Qdrant vector database integration
tessera serve --port 8080 --qdrant-url http://localhost:6333

# Check server health status
tessera health --url http://localhost:8000

# List available base models and their dimensions
tessera list

For the complete Tessera system, see: https://github.com/theoddden/Tessera

License

Apache-2.0