developerjeremylive/Ternary-Bonsai-2-27B-gguf-etheroi

text-generation model by developerjeremylive

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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 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…

developerjeremylive/Ternary-Bonsai-2-27B-gguf-etheroi on Hugging Face A short preview, not the full document.

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Inside the original model card — Document outline
  1. Bonsai 2 27B — GGUF
  2. Highlights
  3. Resources
  4. Model Overview
  5. Weight Representation: Ternary g128
  6. Memory Requirement
  7. Shipped Components
  8. Best Practices
  9. Generation Parameters
  10. System Prompt
  11. Choosing a Packing
  12. Quickstart

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Model overview

developerjeremylive/Ternary-Bonsai-2-27B-gguf-etheroi is a text-generation model repository published by developerjeremylive on Hugging Face. The source reports the llama.cpp library.

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Model facts

Task
text-generation
Library
llama.cpp
Recent downloads (30 days)
0
Cumulative likes
0
Hugging Face trending score
0
Reported safetensors parameters
Not reported
Architecture
Not reported
License
apache-2.0
Access
Not gated by Hugging Face
Created
2026-09-25T06:16:55.000Z
Last modified
2026-09-25T06:16:55.000Z

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.

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Source: Hugging Face Hub. Metadata observed 2026-09-25T06:37:40.528Z. Daily imports are snapshots, not real-time monitoring.

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