Ternary-Bonsai-1.7B-gguf/README.md

license, library_name, pipeline_tag, tags, base_model

license	library_name	pipeline_tag	tags	base_model
apache-2.0	gguf	text-generation	ternary 1.58-bit gguf llama-cpp q2_0 on-device prismml bonsai	prism-ml/Ternary-Bonsai-1.7B-unpacked

Prism ML Website  |  White Paper  |  Demo & Examples  |  Discord

Ternary-Bonsai-1.7B-gguf

Ternary (1.58-bit) language model in GGUF Q2_0 format for llama.cpp

Resources

• White Paper
• Demo repo — examples for serving, benchmarking, and integrating Bonsai
• Discord — community support and updates
• Kernels: Q2_0 is not yet in mainline llama.cpp. Use our fork at PrismML-Eng/llama.cpp (prism branch, default) which adds Q2_0 support for CPU (NEON/generic) and Metal. Upstream PR coming soon.

Model Overview

Item	Specification
Base model	Qwen3-1.7B
Parameters	1.72B
Architecture	GQA, SwiGLU MLP, RoPE, RMSNorm
Context length	32,768 tokens
Vocab size	151,936
Weight format	GGUF Q2_0 g128: {-1, 0, +1} with FP16 group-wise scaling
Packed Q2_0 size	436 MiB (0.46 GB)
Ternary coverage	Embeddings, attention projections, MLP projections, LM head
License	Apache 2.0

Quantization Format: GGUF Q2_0 (g128)

Each weight takes a value from {-1, 0, +1}, with one shared FP16 scale per group of 128 weights:

w_i = scale_g * t_i,    t_i in {-1, 0, +1}

Q2_0 encodes each weight as a 2-bit code q in {0, 1, 2, 3}, dequantized via w = (q - 1) * scale. One 128-element block is 34 bytes (2 bytes FP16 scale + 32 bytes of packed 2-bit codes) for an effective 2.125 bits/weight. The fourth code point (q = 3, reconstructing to +2 * scale) is reserved for future extensions; for ternary weights it is unused.

Memory

Format	Size	Reduction	Ratio
FP16	3.44 GB	--	1.0x
GGUF Q2_0 g128	436 MiB (0.46 GB)	86.6%	7.5x

Files in this repo

File	Format	Size	Recommended
Ternary-Bonsai-1.7B-F16.gguf	FP16	3.44 GB	baseline / re-quantization source
Ternary-Bonsai-1.7B-Q2_0.gguf	Q2_0 (g128)	442 MB	recommended (lossless for ternary)

Quickstart

Build from the Prism fork

git clone https://github.com/PrismML-Eng/llama.cpp
cd llama.cpp
cmake -B build -DGGML_METAL=ON   # or -DGGML_CUDA=ON, -DGGML_VULKAN=ON
cmake --build build -j

llama.cpp CLI

./build/bin/llama-cli \
  -m Ternary-Bonsai-1.7B-Q2_0.gguf \
  -p "Explain quantum computing in simple terms." \
  -n 256

llama.cpp server

./build/bin/llama-server -m Ternary-Bonsai-1.7B-Q2_0.gguf -c 4096

Throughput (llama.cpp, Apple M4 Pro 48 GB)

Backend	PP512 (tok/s)	TG128 (tok/s)	FP16 TG (tok/s)	Speedup
Metal (GPU)	2,088	229	60	3.8x
NEON CPU (10 t)	508	123	—	—

Flags: -ngl 99 -fa 1 for Metal; -ngl 0 -fa 1 -t 10 for CPU.

Fidelity (Q2_0 vs FP16 baseline)

Measured on wikitext-2 (20 chunks, context 512) via llama-perplexity --kl-divergence:

Metric	Value
Mean KL	0.000000
Top-1 agreement	100.000 %
RMS Δp	0.015 %
PPL ratio (Q/base)	1.0048

Q2_0 is effectively lossless for ternary weights — the ternary values land exactly on three of the four 2-bit code points.

Benchmarks

Evaluated with EvalScope v1.4.2 + vLLM 0.15.1 on NVIDIA H100. Full benchmark suite:

Model	Size	Avg	MMLU-R	MuSR	IFEval	GSM8K	HE+	BFCLv3
Ternary Bonsai 1.7B	0.44 GB	58.47	52.9	50.8	70.1	74.2	51.8	51.0
1-bit Bonsai 1.7B (prior)	0.24 GB	49.60	43.2	45.1	63.0	66.3	45.1	34.9
Qwen3 1.7B	3.44 GB	66.57	66.8	50.1	70.3	83.1	57.3	71.8
Qwen3 0.6B	1.19 GB	48.02	47.5	41.5	62.8	64.1	30.5	41.7
LFM2 1.2B	2.34 GB	46.73	52.9	25.4	77.5	62.2	36.0	26.4
Gemma3 1B	2.00 GB	45.53	43.2	37.0	61.9	64.4	40.2	26.5
Llama 3.2 1B	2.47 GB	39.88	47.2	29.2	47.7	49.0	35.4	30.8

Intelligence Density

density = -ln(1 - score/100) / size_GB

Model	Size	Intelligence Density (1/GB)
Ternary Bonsai 1.7B	0.44 GB	2.001
1-bit Bonsai 1.7B (prior)	0.24 GB	2.832
Qwen3 0.6B	1.19 GB	0.549
Qwen3 1.7B	3.44 GB	0.318
Gemma3 1B	2.00 GB	0.304
LFM2 1.2B	2.34 GB	0.269
Llama 3.2 1B	2.47 GB	0.206

Citation

@techreport{ternarybonsai,
    title   = {Ternary Bonsai: 1.58-bit Language Models at 8B, 4B, and 1.7B Scale},
    author  = {Prism ML},
    year    = {2026},
    month   = {April},
    url     = {https://prismml.com}
}

Contact

For questions, feedback, or collaboration inquiries: contact@prismml.com