Swift Bonsai 2 GGUF
Swift Bonsai 2 is UkisAI's reasoning-efficient derivative of Prism ML's Ternary Bonsai 2 27B. It uses 39.8% fewer thinking tokens while scoring 0.19% higher than the base.
Both the 1-bit and 2-bit quantizations are available in this repository; each file is a plain Bonsai 2 pack.
Quantizations
Each file is a complete model with the Swift correction merged into the ternary weights: no adapter file, patch, or extra flag is needed, and the files are the same size as the base Bonsai 2 packs. PTQ1_0 remains the earlier Swift release; PQ2_0 now contains updated merged weights.
Training approach
We built Swift by identifying reasoning-marker tokens that, in our analysis, trigger overthinking in the model's reasoning rollouts. We then fine-tuned the model by penalizing usage of those tokens while it reasons.
Swift produces shorter reasoning traces while keeping accuracy in line with the base model.
Evaluation
GPQA and C-Eval compare base Ternary Bonsai 2 27B with the historical Swift runtime correction under the saved protocols. IFBench and AIME compare base PQ2_0 with the current Swift PQ2_0 file. See the note below. Scores are the percentage of correct responses across all scored repetitions: three complete GPQA-Diamond runs and five runs each of C-Eval, IFBench, and AIME 2025. Token reductions are relative to the base model.
Token statistics measure thinking tokens, except AIME, where they measure the full completion. IFBench uses the official loose scorer on the full 1,500 responses. An up arrow means the current 2-bit model used more tokens. Token changes are not a direct measurement of latency or cost changes.
Research status and limitations
This is an experimental research release. In our internal evaluations and practical testing, benchmark scores did not consistently translate into reliable general-purpose behavior. Instruction following, tool use, and open-ended coding or agent tasks remain uneven. These observations concern the specific models, runtimes, and tests we used; they do not establish a general conclusion about ternary models.
We see ternary quantization as a promising direction for making larger models more accessible. Further advances in training, low-bit adaptation, and inference support may make the approach increasingly useful. This release shares a research result and its current limitations, rather than presenting a production-ready model.
How to use
Download and run
These files use Prism's PQ2_0 and PTQ1_0 ternary packs and run on the PrismML-Eng/llama.cpp fork (tested at revision 1a07bfa5f). Stock llama.cpp does not support these tensor types. No patch, adapter file, or extra flag is needed.
Linux prerequisites: Git, CMake, a C++ compiler, and the NVIDIA CUDA toolkit.
git clone https://github.com/PrismML-Eng/llama.cpp && cd llama.cpp
cmake -B build -DGGML_CUDA=ON && cmake --build build --config Release -j
hf download ukisai/Swift-Bonsai-2-GGUF Swift-Bonsai-2-PQ2_0.gguf --local-dir .
./build/bin/llama-server -m Swift-Bonsai-2-PQ2_0.gguf --host 127.0.0.1 --port 8080 \
-ngl 99 -c 32768 --flash-attn on --jinja \
--temp 1.0 --top-p 0.95 --top-k 20 --min-p 0.0 --repeat-penalty 1.0
To run 1-bit / PTQ1_0 instead, download Swift-Bonsai-2-PTQ1_0.gguf and pass it to -m.
The command above serves a 32,768-token context on 127.0.0.1:8080. Use -c 98304 for the longer evaluation caps if memory permits. Runtime memory includes model weights and context/state caches; download size is not total VRAM usage.
OpenAI-compatible API
curl http://127.0.0.1:8080/v1/chat/completions \
-H 'Content-Type: application/json' \
-d '{
"model": "bonsai-2-swift",
"messages": [{"role": "user", "content": "Explain the difference between correlation and causation."}],
"temperature": 1.0,
"top_p": 0.95,
"top_k": 20,
"min_p": 0.0,
"max_tokens": 4096,
"chat_template_kwargs": {"enable_thinking": true, "reasoning_effort": "xhigh"}
}'
The launcher flags above are the intended model settings. Custom integrations should preserve them.
License and attribution
Created using Bonsai by Prism ML. This is an independent UkisAI release, not an official Prism ML release or endorsement.
The model is distributed under Apache-2.0. The upstream notice and attributions are retained.
Citation
@misc{swift-bonsai-2-gguf,
title = {Swift Bonsai 2 GGUF},
author = {UkisAI},
year = {2026},
url = {https://huggingface.co/ukisai/Swift-Bonsai-2-GGUF}
}