prism-ml/Ternary-Bonsai-27B-gguf
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, for llama.cpp (CUDA, Metal, CPU) 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…
Read the full model card ↗ · Preview checked 2026-09-21T11:51:05.453Z
Inside the original model card — Document outline
- Ternary Bonsai 27B — GGUF
- Highlights
- Resources
- Model Overview
- Weight Representation: Q20g128
- Memory Requirement
- Shipped Components
- Peak Memory at Context
- Best Practices
- Generation Parameters
- System Prompt
- Quickstart
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Model overview
prism-ml/Ternary-Bonsai-27B-gguf is a text-generation model repository published by prism-ml on Hugging Face. The source reports the llama.cpp 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
- llama.cpp
- Recent downloads (30 days)
- 662,554
- Cumulative likes
- 1,381
- Hugging Face trending score
- 66
- Reported safetensors parameters
- Not reported
- Architecture
- Not reported
- License
- apache-2.0
- Access
- Not gated by Hugging Face
- Created
- 2026-07-04T00:24:01.000Z
- Last modified
- 2026-08-31T22:04:46.000Z
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 2026-09-21T06:36:47.722Z. Daily imports are snapshots, not real-time monitoring.
Popularity and source listings do not establish security, suitability, licensing rights or benchmark performance.
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