---
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
---
Prism ML Website |
White Paper |
Demo & Examples |
Discord
# Ternary-Bonsai-4B-gguf
Ternary (1.58-bit) language model in GGUF Q2_0 format for `llama.cpp`
## Resources
- **[White Paper](https://github.com/PrismML-Eng/Bonsai-demo/blob/main/ternary-bonsai-8b-whitepaper.pdf)**
- **[Demo repo](https://github.com/PrismML-Eng/Bonsai-demo)** — examples for serving, benchmarking, and integrating Bonsai
- **[Discord](https://discord.gg/prismml)** — community support and updates
- **Kernels**: Q2_0 is not yet in mainline `llama.cpp`. Use our fork at [PrismML-Eng/llama.cpp](https://github.com/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
```bash
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
```bash
./build/bin/llama-cli \
-m Ternary-Bonsai-4B-Q2_0.gguf \
-p "Explain quantum computing in simple terms." \
-n 256
```
### `llama.cpp` server
```bash
./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
```bibtex
@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**