prism-ml/Ternary-Bonsai-2-27B-mlx-2bit — new model trending #29 on Hugging Face
Prism ML releases Ternary-Bonsai-2-27B, a 27B ternary-weight model derived from Qwen3.8-27B, running on laptops at 8.6 GB with 98.2% FP16 quality retained.
Prism ML released Ternary-Bonsai-2-27B, a 27.36B-parameter model derived from Qwen3.8-27B with fully ternary {-1, 0, +1} weights in a Hadamard-rotated basis. The MLX 2-bit pack is 8.60 GB on disk versus ~54 GB FP16 and averages 84.78 across 14 thinking-mode benchmarks, claimed to retain 98.2% of FP16 intelligence. It keeps 262K-token context via a ~75% linear-attention backbone, ships custom ternary MLX and CUDA kernels, and has a GGUF companion (PTQ1_0 at 5.95 GB, PQ2_0 at 7.21 GB) for llama.cpp. Licensed Apache 2.0.
- 8.60 GB on disk vs ~54 GB FP16, roughly a 7x reduction
- 84.78 average across 14 benchmarks, 98.2% of FP16 quality
- End-to-end ternary weights with Hadamard-rotated basis, no FP16 fallback
- 262K context on-device via ~75% linear-attention Qwen3.8-27B backbone
- Apache 2.0 with GGUF companion for llama.cpp
- Vendors
- Prism ML
- Products
- Ternary-Bonsai-2-27Bllama.cpp
- AI models
- Ternary-Bonsai-2-27BQwen3.8-27B
From the original article
12 min read at huggingface.cotext-generation · 5,056 downloads · 162 likes · license apache-2. 0
By prism-ml · huggingface.co
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