Boole-1.7B

A small and strong 1-bit LLM

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Boole-1.7B is a 1-bit language model derived from Qwen3-1.7B. Its weights take 247 MB, 13.9× less than the BF16 original, and it keeps 85.9% of the original's ten-task benchmark average (50.55 vs 58.83).

Files

File	Runtime	Size
Boole-1.7B.safetensors	vLLM + Humming · CUDA	247.1 MB

For llama.cpp, use the separate GGUF release.

Quick start

This is a packed 1-bit checkpoint. Use the boole-demo loader to export it for vLLM + Humming on a CUDA GPU. The install pins vLLM 0.31.0 and humming-kernels 0.1.16.

git clone https://github.com/PrecisionNumerics/boole-demo.git && cd boole-demo
python -m pip install -e '.[vllm]'
boole-prep vllm precision-numerics/Boole-1.7B --out out/humming
vllm serve out/humming \
    --dtype bfloat16 --max-model-len 4096 \
    --default-chat-template-kwargs '{"enable_thinking": false}'

The loader downloads the checkpoint, config, and tokenizer files. See the runtime README for a chat request example.

Intended use

English-language inference in non-thinking mode (enable_thinking=false).

Evaluation

Scores are the equal-weight mean of ten tasks: MMLU-Redux, GPQA-Diamond, MuSR, IFEval, IFBench, GSM8K, MATH-500, HumanEval+, MBPP+ and BFCLv3, run in non-thinking mode with greedy decoding (GPQA-Diamond averages ten sampled runs). Harness: EvalScope 1.4.2 and bfcl-eval 2025.10.27.1. See the blog for the comparison.

Full evaluation settings

We use the same evaluation settings as PrismML's Bonsai. The settings below cover 10 generative benchmarks.

Setting	Value
Few-shot examples	0
Generative prompts	Benchmark prompts with the checkpoint's chat template
Thinking mode	Disabled (enable_thinking=false)
Default generation	Greedy: temperature 0, top-p 1
Default seed / repetitions	Seed 42; one response per example
Additional sampling filters	Top-k and min-p disabled; repetition penalty 1.0
Dataset limit	Full benchmark splits; no sample limit

GPQA-Diamond uses temperature 0.6, top-p 0.95, and 10 repetitions per example with seeds 42–51. Its score is averaged across those repetitions.

Maximum generated tokens per response:

Benchmark	Max new tokens
MMLU-Redux	2,048
GPQA-Diamond	8,192
MuSR	2,048
IFEval	4,096
IFBench	4,096
GSM8K	2,048
MATH-500	8,192
HumanEval+	8,192
MBPP+	4,096
BFCL-v3	4,096

Generation context is 16,384 tokens by default and 40,960 for BFCL-v3, capped when a checkpoint specifies a lower limit. Concurrency and memory allocation are tuned to the runtime and available GPU memory.

IFEval and IFBench report the mean of strict prompt-level and instruction-level accuracy. HumanEval+ and MBPP+ use augmented tests and pass@1. BFCL-v3 reports the macro average of 13 single-turn subsets, with is_fc_model=false and underscore_to_dot=false. GSM8K and MATH-500 use a 10-second per-answer scoring deadline; timed-out answers receive zero.

Evaluation packages: EvalScope 1.4.2 and bfcl-eval 2025.10.27.1.

License

Weights and runtime code use Apache 2.0. NOTICE includes upstream Qwen attribution.

Citation

@misc{precisionnumerics2026boole1p7b,
  author = {{Precision Numerics}},
  title = {{Boole}-1.7B},
  year = {2026},
  month = {October},
  url = {https://huggingface.co/precision-numerics/Boole-1.7B}
}