unirt 0.1.4

Python binding for UniRT — multi-backend on-device LLM inference with an extensible VLM API

unirt (Python binding)

Python binding for the UniRT SDK — run LLMs locally through a single API with interchangeable backends:

runtime	models	hardware
llama_cpp	GGUF	CPU / Metal / Vulkan / CUDA
mlx	HF safetensors (validated SmolLM2-style Llama/ByteLevel-BPE layout; dense or MLX-quantized)	Apple Silicon Metal GPU
onnxruntime	ONNX encoder embeddings	CPU / Apple Core ML

The bundled llama_cpp runtime supports GGUF VLMs through libmtmd when an mmproj is present. MLX remains text-only and fails explicitly for VLM models. MLX also requires a usable Metal device; if none is visible, model loading fails before native model allocation.

Usage

from unirt.auto import AutoModelForCausalLM

model = AutoModelForCausalLM.from_pretrained(
    'bartowski/SmolLM2-135M-Instruct-GGUF',
    precision='Q4_K_M',
    device_map='llama_cpp',
)
out = model.generate(prompt, max_new_tokens=128, temperature=0.7)
print(out.text)
model.close()

Embedding repositories select an ONNX variant and tokenizer sidecars without downloading the PyTorch checkpoint:

from unirt import AutoModelForEmbedding

with AutoModelForEmbedding.from_pretrained(
    'sentence-transformers/all-MiniLM-L6-v2',
    device_map='cpu',  # or 'coreml' on Apple Silicon
) as model:
    vectors = model.encode(['a cat on a mat', 'a kitten on a rug'])
    print(len(vectors), len(vectors[0]))  # 2, 384

Repository ids are inspected and downloaded with huggingface_hub. GGUF repositories download only the selected quantization (including all of its shards) plus tokenizer/config sidecars. The default cache is ~/.cache/unirt; set UNIRT_DATADIR to move it and UNIRT_HFTOKEN for gated or private repositories.

Generation is stateless by default (n_past=0 clears prior KV state before prefilling the supplied prompt). To continue from a known cached prefix, pass the exact prefix length through n_past; invalid values are rejected rather than silently duplicating context.

The native library is discovered automatically from <repo>/sdk/pkg-unirt/lib (dev layout) or the packaged wheel; set UNIRT_LIB_PATH / UNIRT_PLUGIN_PATH to override. See the repository README for build instructions, the interactive chat example, and the OpenAI-compatible server (python3 -m unirt.server).