prism-ml/Ternary-Bonsai-2-27B-mlx-2bit
text-generation model by prism-ml
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From the publisher
Model card & documentation
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Prism ML Website | Whitepaper | Demo & Examples | Discord Full 27B-class reasoning in ternary transformer weights — on everyday laptops Each weight takes a value from {−1, 0, +1}, with one shared FP16 scale factor for every group of 128 weights. A ternary value carries log₂3 ≈ 1.585 bits of information, so the effective storage cost of the format is \1.71 bits/weight (ternary code + 16-bit scale amortized over 128 weights); counting the small set of tensors held above the ternary representation brings the model as a whole to 1.72 bits/weight — an idealized \9.3x reduction vs FP16. The weights are stored in a rotated basis: each matrix is transformed blockwise by an orthogonal Hadamard rotation before the ternary assignment, and the runtime applies the matching transform to activations. The rotation is folded into the stored weights offline, so it costs no extra bits and no extra weight…
Read the full model card ↗ · Preview checked 2026-10-29T21:55:20.790Z
Inside the original model card — Document outline
- Bonsai 2 27B
- Highlights
- Resources
- Model Overview
- Weight Representation: Ternary g128
- Memory Requirement
- MLX Packaging
- Best Practices
- Generation Parameters
- System Prompt
- Quickstart
- Cross-Platform Throughput
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Model overview
prism-ml/Ternary-Bonsai-2-27B-mlx-2bit is a text-generation model repository published by prism-ml on Hugging Face. The source reports the mlx library.
This page summarizes Hub metadata. For intended use, training data, evaluation results and limitations, consult the original model card.
Model facts
- Task
- text-generation
- Library
- mlx
- Recent downloads (30 days)
- 63,997
- Cumulative likes
- 404
- Hugging Face trending score
- 58
- Reported safetensors parameters
- 27,359,638,768
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-09-16T23:41:18.000Z
- Last modified
- 2026-09-22T21:18:49.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: Qwen/Qwen3.8-27B
Parameter count is not a RAM/VRAM requirement. Check precision, quantization, context length and runtime compatibility in the model card; no hardware or API-cost claim is inferred here.
Use and evaluate
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Source and freshness
Source: Hugging Face Hub. Metadata observed 2026-09-30T06:38:12.437Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.