bit-jev

Install, run a decision, then build your own structured requests. bit-jev scores explicit options over a BitNet backbone and returns answers and probabilities without generating answer text token by token.

GitHub documentation · GGUF model and model card · 中文说明

Try it

python -m pip install --upgrade bit-jev
bit-jev-demo

The built-in demo prints a real model answer and latency. The first run downloads the 1.19 GB GGUF from Hugging Face; later runs reuse the cache. Installation itself does not download model weights. Use bit-jev-demo --device gpu for Vulkan.

The Windows x64 wheel includes precompiled CPU and Vulkan GPU runners for AVX2 processors. Inference on those machines needs no Git, CMake, C++ compiler, or Vulkan SDK. Vulkan still needs a compatible graphics driver and its vulkan-1.dll runtime. Other platforms build the native runner on demand and require Git, CMake 3.28+, and a C++17 compiler (Windows C++ Build Tools). Source builds of Vulkan also need its SDK; CUDA builds need a CUDA Toolkit. Both precompiled programs use pinned BitNet and llama.cpp source with the ReLU² runtime patch and carry their MIT license notices.

Resident inference

from bit_jev.gguf import BitJev

request = {
    "state": "A customer reports a duplicate charge.",
    "questions": {
        "team": {
            "type": "choice",
            "instructions": "Which team should handle this?",
            "criteria": {"billing": "Payment and refund issues", "shipping": "Delivery issues"},
        }
    },
}

with BitJev.from_pretrained(device="cpu", threads=8) as model:
    result = model.infer(request)
    print(result["answers"], result["latency_ms"])

Use device="gpu" for Vulkan or device="cuda" for an NVIDIA CUDA build. GPU requests fail clearly if a backend or visible GPU is unavailable. A local model directory can replace the default Hugging Face repo, and binary="/path/to/bit-jev-cpu" can select an existing native runner. The model stays loaded for repeated infer() calls; latency_ms reports native compute only, excluding download, build, loading, encoding, and IPC.

The CLI accepts UTF-8 JSONL input:

bit-jev --device cpu --input requests.jsonl --output results.jsonl

Training and distillation dependencies are optional: pip install 'bit-jev[train]'. The native runner evaluates one causal row per question; multiple questions repeat the shared state. The repository's Apache-2.0 license covers source code, while the checkpoint has no standalone open-weights license. The model card documents Yelp training-data provenance and its unresolved permission request.