---

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-4B-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-4B-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 | Qwen3-4B |

| Parameters | 4.0B (~3.6B non-embedding) |

| Architecture | GQA (32 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm |

| Layers | 36 Transformer decoder blocks |

| 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 | 1,020 MiB (1.07 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 | 8.04 GB | -- | 1.0x |

| GGUF Q2_0 g128| 1,020 MiB (1.07 GB) | 86.3% | 7.3x |

Files in this repo

| File | Format | Size | Recommended |

| :------------------------------ | :----- | -----: | :---------- |

| Ternary-Bonsai-4B-F16.gguf | FP16 | 8.04 GB | baseline / re-quantization source |

| Ternary-Bonsai-4B-Q2_0.gguf | Q2_0 (g128) | 1,020 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-4B-Q2_0.gguf \
  -p "Explain quantum computing in simple terms." \
  -n 256

llama.cpp server

./build/bin/llama-server -m Ternary-Bonsai-4B-Q2_0.gguf -c 4096

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

| Backend | PP512 (tok/s) | TG128 (tok/s) |

| :--------------- | ------------: | ------------: |

| Metal (GPU) | 826 | 120 |

| NEON CPU (10 t) | 226 | 56 |

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

Fidelity (Q2_0 vs FP16 baseline)

Q2_0 is effectively lossless for ternary weights — the ternary values land exactly on three of the four 2-bit code points, so quantize/dequantize is bit-exact in the absence of FP16 scale rounding.

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 4B | 1.02 GB | 70.7 | 69.7 | 45.1 | 72.1 | 90.5 | 78.7 | 67.8 |

| 1-bit Bonsai 4B (prior) | 0.57 GB | 62.7 | 58.7 | 41.4 | 69.6 | 87.3 | 71.3 | 48.0 |

| Qwen 3 4B | 8.04 GB | 77.1 | 79.8 | 57.4 | 80.0 | 92.1 | 74.4 | 78.9 |

| Ministral3 3B | 6.86 GB | 73.2 | 77.5 | 56.5 | 73.1 | 91.4 | 69.5 | 71.3 |

| Gemma 3 4B | 7.76 GB | 67.9 | 66.0 | 46.3 | 73.0 | 89.8 | 67.1 | 65.1 |

| Llama 3.2 3B | 6.43 GB | 64.4 | 65.5 | 48.9 | 78.3 | 80.1 | 52.4 | 60.9 |

Intelligence Density

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

| Model | Size | Intelligence Density (1/GB) |

| :------------------------ | ----------: | --------------------------: |

| Ternary Bonsai 4B | 1.02 GB | 1.202 |

| 1-bit Bonsai 4B (prior) | 0.57 GB | 1.744 |

| Ministral3 3B | 6.86 GB | 0.192 |

| Qwen 3 4B | 8.04 GB | 0.183 |

| Llama 3.2 3B | 6.43 GB | 0.161 |

| Gemma 3 4B | 7.76 GB | 0.146 |

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