---

license: apache-2.0

library_name: gguf

pipeline_tag: text-generation

tags:

• ternary
• 1.58-bit
• gguf
• llama-cpp
• q2_0
• on-device
• prismml
• bonsai

base_model:

• prism-ml/Ternary-Bonsai-8B-unpacked

---

<p align="center">

<img src="./assets/bonsai-logo.svg" width="280" alt="Bonsai">

</p>

<p align="center">

<a href="https://prismml.com"><b>Prism ML Website</b></a> &nbsp;|&nbsp;

<a href="https://github.com/PrismML-Eng/Bonsai-demo/blob/main/ternary-bonsai-8b-whitepaper.pdf"><b>White Paper</b></a> &nbsp;|&nbsp;

<a href="https://github.com/PrismML-Eng/Bonsai-demo"><b>Demo &amp; Examples</b></a> &nbsp;|&nbsp;

<a href="https://discord.gg/prismml"><b>Discord</b></a>

</p>

Ternary-Bonsai-8B-gguf

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

<p align="center">

<img src="./assets/frontier.svg" width="680" alt="Pareto Frontier">

</p>

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