trillim 0.1.5

The fastest inference framework to run BitNet models on CPUs

Trillim

High-performance CPU inference engine for BitNet models. Runs ternary-quantized models ({-1, 0, 1} weights) using platform-specific SIMD optimizations (AVX2 on x86, NEON on ARM).

Quick Start

Prerequisites

• Python 3.12+ with uv - can use pip or any package manager

Install and run

# Install trillim
uv add trillim

# Pull a pre-quantized model
uv run trillim pull Trillim/BitNet-TRNQ

# Chat
uv run trillim chat Trillim/BitNet-TRNQ

Quantize your own model

If you have a HuggingFace BitNet model with safetensors weights:

# Quantize model weights → qmodel.tensors + rope.cache
uv run trillim quantize <path-to-model> --model

# Optionally extract a PEFT LoRA adapter → qmodel.lora
uv run trillim quantize <path-to-model> --adapter <path-to-adapter>

API Server

Trillim includes an OpenAI-compatible API server:

# Start the server
uv run trillim serve <model-dir>

# With voice pipeline (speech-to-text + text-to-speech)
uv run trillim serve <model-dir> --voice

Endpoints:

• POST /v1/chat/completions — chat completions (streaming supported)
• POST /v1/completions — text completions
• GET /v1/models — list loaded models
• POST /v1/models/load — hot-swap models and LoRA adapters at runtime
• POST /v1/audio/transcriptions — speech-to-text (with --voice)
• POST /v1/audio/speech — text-to-speech (with --voice)
• GET /v1/voices — list available TTS voices
• POST /v1/voices — register a custom voice from audio (need to accept pocket-tts' terms on huggingface)

Python SDK

The server is built on a composable SDK. Each capability (LLM, Whisper, TTS) is a standalone component:

from trillim import Server, LLM, TTS, Whisper

# Inference only
Server(LLM("models/BitNet")).run()

# Inference + voice
Server(LLM("models/BitNet"), Whisper(), TTS()).run()

# TTS only
Server(TTS()).run()

LoRA Adapters

Trillim supports PEFT LoRA adapters as bf16 corrections on top of the ternary base model:

# Ensure qmodel.lora is in the directory 
# (uv run trillim quantize ... will do this)
uv run trillim chat Trillim/BitNet-TRNQ --lora

Supported Architectures

• BitnetForCausalLM — BitNet with ternary weights and ReLU² activation
• LlamaForCausalLM — Llama-style with SiLU activation

Platform Support

Platform	Status
x86_64 (AVX2)	Supported
ARM64 (NEON)	Supported

Thread count is auto-detected as num_cores - 2. Override by passing a --threads N CLI argument.

License

The Trillim Python SDK source code is MIT-licensed. The C++ inference engine binaries (inference, trillim-quantize) bundled in the pip package are proprietary — you may use them as part of Trillim but may not reverse-engineer or redistribute them separately. See LICENSE for full terms.