unirt 0.3.0

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	in the published wheels
llama_cpp	GGUF (LLM, VLM, embeddings, rerank)	CPU, Metal on macOS	every wheel
mlx	HF safetensors (SmolLM2-style Llama/ByteLevel-BPE layout; dense or MLX-quantized)	Apple Silicon Metal GPU	macOS wheel (Apple-only)
onnxruntime	ONNX encoder embeddings	CPU, Apple Core ML	every wheel

unirt devices lists what your install actually has. The llama_cpp runtime is built for a generic CPU baseline plus Metal on macOS — no CUDA, no Vulkan.

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.

Install

pip install unirt

Wheels ship the native libraries — no toolchain, no build step — for macOS 14+ arm64, Linux x86_64 and arm64 (manylinux_2_31, glibc 2.31+), and Windows 10+ x86_64 and arm64. Python 3.10+.

Quickstart (CLI)

unirt chat bartowski/SmolLM2-135M-Instruct-GGUF   # download + interactive chat
unirt serve <model> --port 8080                   # OpenAI-compatible server
unirt embed <encoder> "some text"                 # one vector per argument
unirt rerank <cross-encoder> "a query" "doc one" "doc two"
unirt pull <hf-repo>                              # download only
unirt ls                                          # cached models
unirt devices                                     # plugins + devices

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.

Structured output

Constrain decoding so the reply is guaranteed to parse — the grammar masks invalid tokens at every step, which makes even small models reliable JSON emitters (llama_cpp backend; MLX rejects these options):

schema = {'type': 'object',
          'properties': {'city': {'type': 'string'}, 'country': {'type': 'string'}},
          'required': ['city', 'country']}
out = model.generate('Facts about the capital of France as JSON.',
                     json_schema=schema)      # dict or serialized JSON string
data = json.loads(out.text)                    # always parses

model.generate(prompt, json_mode=True)         # any syntactically valid JSON
model.generate(prompt, grammar='root ::= ...')  # raw GBNF

The server accepts the OpenAI response_format field with types json_object and json_schema. Note a length finish can still truncate mid-object — budget max_tokens accordingly.

OpenAI-compatible server

python3 -m unirt.server --model bartowski/SmolLM2-135M-Instruct-GGUF \
  --backend llama_cpp --port 8080

Then point any OpenAI client (or plain curl) at http://localhost:8080/v1/chat/completions — streaming SSE included, and GGUF VLMs accept image content parts when loaded with an mmproj.

--embedding-model <encoder> adds /v1/embeddings and --rerank-model <cross-encoder> adds /v1/rerank; either may be given without --model, so a retrieval sidecar needs no chat model at all.

Sampling takes temperature, top_p, top_k, min_p, seed, repetition_penalty, presence_penalty and frequency_penalty. --api-key (or UNIRT_API_KEY) requires Authorization: Bearer <key> on every /v1 endpoint, leaving /health open for probes — worth setting whenever --host is not loopback.

The native library is closed-source and ships prebuilt. A wheel already bundles it under unirt/lib/; a source checkout is discovered at <repo>/sdk/pkg-unirt/lib after building the SDK, and installing from source without building means populating unirt/lib/ yourself from a Release's native-libs archive before pip install .. UNIRT_LIB_PATH / UNIRT_PLUGIN_PATH override the search.