tessera-hypernetwork 1.0.7

Generate per-session LoRA adapters for inference tasks

Tessera Hypernetwork

Generate per-session LoRA adapters for inference tasks using hypernetwork synthesis.

Features

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

Installation

pip install tessera-hypernetwork

Quick Start

Generate LoRA Adapter from Metadata

tessera generate \
  --from-metadata '{"task": "classification", "domain": "medical"}' \
  --save ./adapter.safetensors

The --base-model argument is optional and defaults to mistralai/Mistral-7B-Instruct-v0.2.

Generate LoRA Adapter from Text Description

tessera generate \
  --from-text "Senior litigation associate specializing in IP law" \
  --save ./adapter.safetensors

Generate LoRA Adapter from Document

tessera generate \
  --from-doc ./document.txt \
  --save ./adapter.safetensors

Start the Hypernetwork Server

tessera serve --port 8000 --host 0.0.0.0

Check Server Health

tessera health --url http://localhost:8000

List Available Base Models

tessera list

CLI Commands

Generate

Generate LoRA adapters from metadata, text, or documents:

# From metadata (JSON string or file)
tessera generate \
  --from-metadata '{"task": "classification", "domain": "medical"}' \
  --base-model mistralai/Mistral-7B-Instruct-v0.2 \
  --rank 16 \
  --save ./adapter.safetensors

# From text description
tessera generate \
  --from-text "Medical diagnosis assistant" \
  --base-model mistralai/Mistral-7B-Instruct-v0.2 \
  --rank 16 \
  --save ./adapter.safetensors

# From document
tessera generate \
  --from-doc ./document.txt \
  --base-model mistralai/Mistral-7B-Instruct-v0.2 \
  --rank 16 \
  --save ./adapter.safetensors

Options:

• --from-metadata: JSON metadata string or file path
• --from-text: Natural language description
• --from-doc: Document content or file path
• --base-model: Base model identifier (default: mistralai/Mistral-7B-Instruct-v0.2)
• --rank: LoRA rank (default: 16)
• --save: Output path for safetensors file (required)
• --mode: Generation mode: doc, metadata, or text (auto-inferred if not specified)

Serve

Start the hypernetwork server:

tessera serve --port 8000 --host 0.0.0.0

Options:

• --port: Port to serve on (default: 8000)
• --host: Host to bind to (default: 0.0.0.0)
• --qdrant-url: Qdrant vector database URL (optional)
• --workers: Number of worker processes (default: 1)

Health

Check server health status:

tessera health --url http://localhost:8000

Options:

• --url: Server URL (default: http://localhost:8000)

List

List available base models and their dimensions:

tessera list

LoRAX Adapter Management

Import, list, and unload adapters:

# Import an adapter
tessera lorax import-adapter \
  --path ./adapter.safetensors \
  --name my-adapter \
  --base-model mistralai/Mistral-7B-Instruct-v0.2 \
  --server-url http://localhost:8000

# List loaded adapters
tessera lorax list-adapters --server-url http://localhost:8000

# Unload an adapter
tessera lorax unload --name my-adapter --server-url http://localhost:8000

API Endpoints

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
• POST /v1/adapters - Import adapter safetensors
• GET /v1/adapters - List loaded adapters
• DELETE /v1/adapters/{name} - Unload adapter

Development

Install development dependencies:

pip install tessera-hypernetwork[dev]

Run tests:

pytest

Run linting:

ruff check .

License

Apache-2.0