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Model: prism-ml/Ternary-Bonsai-8B-gguf
Source: Original Platform

2026-04-29 08:48:26 +08:00

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Ternary-Bonsai-8B-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	Qwen3-8B
Parameters	8.19B (~6.95B non-embedding)
Architecture	GQA (32 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm
Layers	36 Transformer decoder blocks
Context length	65,536 tokens
Vocab size	151,936
Weight format	GGUF Q2_0 g128: {-1, 0, +1} with FP16 group-wise scaling
Packed Q2_0 size	2.03 GiB (2.18 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	16.38 GB	--	1.0x
GGUF Q2_0 g128	2.03 GiB (2.18 GB)	86.7%	7.5x

Files in this repo

File	Format	Size	Recommended
`Ternary-Bonsai-8B-F16.gguf`	FP16	16.38 GB	baseline / re-quantization source
`Ternary-Bonsai-8B-Q2_0.gguf`	Q2_0 (g128)	2.03 GiB	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-8B-Q2_0.gguf \
  -p "Explain quantum computing in simple terms." \
  -n 256

`llama.cpp` server

./build/bin/llama-server -m Ternary-Bonsai-8B-Q2_0.gguf -c 4096

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

Backend	PP512 (tok/s)	TG128 (tok/s)
Metal (GPU)	455	76
NEON CPU (10 t)	146	32

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

Benchmarks

Evaluated with EvalScope v1.4.2 + vLLM 0.15.1 on NVIDIA H100 under identical infrastructure, generation parameters, and scoring. All models are in the 6B-9B parameter range.

Model	Size	Avg	MMLU-R	MuSR	GSM8K	HE+	IFEval	BFCL
Qwen 3 8B	16.38 GB	79.3	83	55	93	82.3	81.5	81
Ternary Bonsai 8B	2.18 GB	75.5	72.6	56.2	91	77.4	81.8	73.9
1-bit Bonsai 8B (prior)	1.15 GB	70.5	65.7	50	88	73.8	79.8	65.7
RNJ 8B	16.63 GB	73.1	75.5	50.4	93.7	84.2	73.8	61.1
Ministral3 8B	16.04 GB	71.0	68.9	53.8	87.9	72.6	67.4	75.4
Olmo 3 7B	14.60 GB	70.9	72	56.1	92.5	79.3	87.1	38.4

Ternary Bonsai 8B ranks 2nd among all compared models despite being 1/8th the size.

Intelligence Density

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

Model	Size	Intelligence Density (1/GB)
Ternary Bonsai 8B	2.18 GB	0.645
1-bit Bonsai 8B (prior)	1.15 GB	1.062
Qwen 3 8B	16.38 GB	0.096
RNJ 8B	16.62 GB	0.079

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

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