prism-ml/Ternary-Bonsai-27B-mlx-2bit
text-generation model by prism-ml
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 is \1.71 bits/weight (ternary code + 16-bit scale amortized over 128 weights) — an idealized \9.4x reduction vs FP16. Relative to the binary format, the extra zero state gives a more expressive weight alphabet and recovers more of the full-precision model's behavior, which makes ternary the quality-oriented operating point of the Bonsai 27B family. Today's kernels store each ternary value in a 2-bit slot (2.125 bits/weight deployed), so the deployed footprint sits above the representation's information-theoretic minimum until native ternary kernels close the gap. The deployed figure describes the language model alone…
Read the full model card ↗ · Preview checked 2026-10-30T17:09:56.712Z
Inside the original model card — Document outline
- Ternary Bonsai 27B
- Highlights
- Resources
- Model Overview
- Weight Representation: Ternary g128
- Memory Requirement
- Shipped Components
- MLX Packaging
- Peak Memory at Context
- Best Practices
- Generation Parameters
- System Prompt
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Model overview
prism-ml/Ternary-Bonsai-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)
- 983,558
- Cumulative likes
- 219
- Hugging Face trending score
- 0
- Reported safetensors parameters
- 27,356,728,560
- Architecture
- Qwen3_5ForConditionalGeneration
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-07-04T00:24:22.000Z
- Last modified
- 2026-07-14T21:39:03.000Z
Compatibility and lineage
Language coverage was not reported.
Reported base models: Qwen/Qwen3.6-27B
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 2026-11-01T06:51:29.042Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.