162 lines
6.4 KiB
Markdown
162 lines
6.4 KiB
Markdown
---
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license: apache-2.0
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library_name: gguf
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pipeline_tag: text-generation
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tags:
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- ternary
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- 1.58-bit
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- gguf
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- llama-cpp
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- q2_0
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- on-device
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- prismml
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- bonsai
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base_model:
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- prism-ml/Ternary-Bonsai-8B-unpacked
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---
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<p align="center">
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<img src="./assets/bonsai-logo.svg" width="280" alt="Bonsai">
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</p>
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<p align="center">
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<a href="https://prismml.com"><b>Prism ML Website</b></a> |
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<a href="https://github.com/PrismML-Eng/Bonsai-demo/blob/main/ternary-bonsai-8b-whitepaper.pdf"><b>White Paper</b></a> |
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<a href="https://github.com/PrismML-Eng/Bonsai-demo"><b>Demo & Examples</b></a> |
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<a href="https://discord.gg/prismml"><b>Discord</b></a>
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</p>
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# Ternary-Bonsai-8B-gguf
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Ternary (1.58-bit) language model in GGUF Q2_0 format for `llama.cpp`
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<p align="center">
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<img src="./assets/frontier.svg" width="680" alt="Pareto Frontier">
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</p>
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## Resources
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- **[White Paper](https://github.com/PrismML-Eng/Bonsai-demo/blob/main/ternary-bonsai-8b-whitepaper.pdf)**
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- **[Demo repo](https://github.com/PrismML-Eng/Bonsai-demo)** — examples for serving, benchmarking, and integrating Bonsai
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- **[Discord](https://discord.gg/prismml)** — community support and updates
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- **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.
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## Model Overview
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| Item | Specification |
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| :--------------- | :----------------------------------------------------------------------- |
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| Base model | Qwen3-8B |
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| Parameters | 8.19B (~6.95B non-embedding) |
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| Architecture | GQA (32 query / 8 KV heads), SwiGLU MLP, RoPE, RMSNorm |
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| Layers | 36 Transformer decoder blocks |
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| Context length | 65,536 tokens |
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| Vocab size | 151,936 |
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| Weight format | GGUF Q2_0 g128: {-1, 0, +1} with FP16 group-wise scaling |
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| Packed Q2_0 size | **2.03 GiB** (2.18 GB) |
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| Ternary coverage | Embeddings, attention projections, MLP projections, LM head |
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| License | Apache 2.0 |
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## Quantization Format: GGUF Q2_0 (g128)
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Each weight takes a value from {-1, 0, +1}, with one shared FP16 scale per group of 128 weights:
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```
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w_i = scale_g * t_i, t_i in {-1, 0, +1}
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```
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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.
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### Memory
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| Format | Size | Reduction | Ratio |
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| :---------------- | ----------: | --------: | ---------: |
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| FP16 | 16.38 GB | -- | 1.0x |
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| **GGUF Q2_0 g128**| **2.03 GiB** (2.18 GB) | **86.7%** | **7.5x** |
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## Files in this repo
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| File | Format | Size | Recommended |
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| :------------------------------ | :----- | -----: | :---------- |
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| `Ternary-Bonsai-8B-F16.gguf` | FP16 | 16.38 GB | baseline / re-quantization source |
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| `Ternary-Bonsai-8B-Q2_0.gguf` | Q2_0 (g128) | 2.03 GiB | **recommended** (lossless for ternary) |
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## Quickstart
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### Build from the Prism fork
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```bash
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git clone https://github.com/PrismML-Eng/llama.cpp
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cd llama.cpp
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cmake -B build -DGGML_METAL=ON # or -DGGML_CUDA=ON, -DGGML_VULKAN=ON
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cmake --build build -j
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```
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### `llama.cpp` CLI
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```bash
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./build/bin/llama-cli \
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-m Ternary-Bonsai-8B-Q2_0.gguf \
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-p "Explain quantum computing in simple terms." \
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-n 256
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```
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### `llama.cpp` server
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```bash
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./build/bin/llama-server -m Ternary-Bonsai-8B-Q2_0.gguf -c 4096
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```
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## Throughput (llama.cpp, Apple M4 Pro 48 GB)
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| Backend | PP512 (tok/s) | TG128 (tok/s) |
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| :--------------- | ------------: | ------------: |
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| Metal (GPU) | 455 | **76** |
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| NEON CPU (10 t) | 146 | **32** |
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Flags: `-ngl 99 -fa 1` for Metal; `-ngl 0 -fa 1 -t 10` for CPU.
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## Benchmarks
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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.
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| Model | Size | Avg | MMLU-R | MuSR | GSM8K | HE+ | IFEval | BFCL |
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| :------------------------ | ----------: | -------: | -----: | ---: | ----: | ---: | -----: | ---: |
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| Qwen 3 8B | 16.38 GB | **79.3** | 83 | 55 | 93 | 82.3 | 81.5 | 81 |
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| **Ternary Bonsai 8B** | **2.18 GB** | **75.5** | 72.6 | 56.2 | 91 | 77.4 | 81.8 | 73.9 |
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| *1-bit Bonsai 8B (prior)* | *1.15 GB* | *70.5* | 65.7 | 50 | 88 | 73.8 | 79.8 | 65.7 |
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| RNJ 8B | 16.63 GB | **73.1** | 75.5 | 50.4 | 93.7 | 84.2 | 73.8 | 61.1 |
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| Ministral3 8B | 16.04 GB | **71.0** | 68.9 | 53.8 | 87.9 | 72.6 | 67.4 | 75.4 |
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| Olmo 3 7B | 14.60 GB | **70.9** | 72 | 56.1 | 92.5 | 79.3 | 87.1 | 38.4 |
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Ternary Bonsai 8B ranks **2nd** among all compared models despite being 1/8th the size.
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## Intelligence Density
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```
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density = -ln(1 - score/100) / size_GB
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```
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| Model | Size | Intelligence Density (1/GB) |
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| :------------------------ | ----------: | --------------------------: |
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| **Ternary Bonsai 8B** | **2.18 GB** | **0.645** |
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| *1-bit Bonsai 8B (prior)* | *1.15 GB* | *1.062* |
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| Qwen 3 8B | 16.38 GB | 0.096 |
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| RNJ 8B | 16.62 GB | 0.079 |
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## Citation
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```bibtex
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@techreport{ternarybonsai,
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title = {Ternary Bonsai: 1.58-bit Language Models at 8B, 4B, and 1.7B Scale},
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author = {Prism ML},
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year = {2026},
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month = {April},
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url = {https://prismml.com}
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}
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```
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## Contact
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For questions, feedback, or collaboration inquiries: **contact@prismml.com** |