Duplicate from prism-ml/Ternary-Bonsai-27B-gguf

Co-authored-by: Pasha <pashak@users.noreply.huggingface.co>

.eval_results/aime_2026.yaml

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+- dataset:
+    id: MathArena/aime_2026
+    task_id: MathArena/aime_2026
+  source:
+    name: Model Card
+    url: https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf
+  value: 87.5

.eval_results/gsm8k.yaml

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+- dataset:
+    id: openai/gsm8k
+    task_id: gsm8k
+  source:
+    name: Model Card
+    url: https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf
+  value: 96.06

.eval_results/mmmu_pro.yaml

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+- dataset:
+    id: MMMU/MMMU_Pro
+    task_id: mmmu_pro_vision
+  source:
+    name: Model Card
+    url: https://huggingface.co/prism-ml/Ternary-Bonsai-27B-gguf
+  value: 68.96

.gitattributes

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+Ternary-Bonsai-27B-mmproj-BF16.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-PQ2_0.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-Q2_g64.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-mmproj-Q8_0.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-dspark-bf16.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-dspark-Q2_0.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-F16.gguf filter=lfs diff=lfs merge=lfs -text
+Ternary-Bonsai-27B-dspark-Q4_1.gguf filter=lfs diff=lfs merge=lfs -text

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+   9. Accepting Warranty or Additional Liability. While redistributing
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NOTICE.txt

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+This software is copyright 2026-present Prism ML, Inc. It is available under the Apache 2.0 license.
+If you publicly deploy or redistribute this software, we would appreciate attribution such as: "Created using Bonsai by Prism ML."
+
+This software is built from Qwen3.6-27B, Copyright 2026 Alibaba Cloud, which is available under the Apache 2.0 License: https://huggingface.co/Qwen/Qwen3.6-27B/blob/main/LICENSE

README.md

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+---
+license: apache-2.0
+library_name: llama.cpp
+pipeline_tag: text-generation
+tags:
+- conversational
+- ternary
+- 2-bit
+- gguf
+- llama-cpp
+- cuda
+- metal
+- on-device
+- hybrid-attention
+- prismml
+- bonsai
+base_model:
+- Qwen/Qwen3.6-27B
+---
+
+<p align="center">
+  <img src="./assets/bonsai-logo.svg" width="280" alt="Bonsai">
+</p>
+
+<p align="center">
+  <a href="https://prismml.com"><b>Prism ML Website</b></a> &nbsp;|&nbsp;
+  <a href="https://github.com/PrismML-Eng/Bonsai-demo"><b>Whitepaper</b></a> &nbsp;|&nbsp;
+  <a href="https://github.com/PrismML-Eng/Bonsai-demo"><b>Demo &amp; Examples</b></a> &nbsp;|&nbsp;
+  <a href="https://discord.gg/prismml"><b>Discord</b></a>
+</p>
+
+# Ternary Bonsai 27B — GGUF
+
+Full 27B-class reasoning in ternary transformer weights, for llama.cpp (CUDA, Metal, CPU)
+
+> **\~9.4x** smaller than FP16 (ideal) | **95%** of FP16 intelligence retained | **\~26 tok/s** on an Apple M5 Pro laptop
+
+## Highlights
+
+- **\~7.2 GB** deployed footprint (down from \~54 GB FP16) — full 27B-class reasoning on a standard laptop or a single GPU
+- **95% of FP16 intelligence retained**: 80.49 average across 15 thinking-mode benchmarks — a *higher* score than the conventional IQ2_XXS build (72.73) at less than two-thirds of its footprint
+- **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse: math within two points of full precision (93.40), coding at 85.96, agentic tool use at 74.01
+- **End-to-end ternary language weights** across embeddings, attention projections, MLP projections, and LM head, at a *true* 1.71 bits per weight — no high-precision escape hatches behind a low-bit label; the vision tower ships in compact 4-bit HQQ
+- **262K-token context** on-device, kept practical by the Qwen3.6-27B hybrid-attention backbone (\~75% linear attention) and 4-bit KV-cache quantization
+- **GGUF Q2_0_g128** format with custom 2-bit hybrid-attention kernels for llama.cpp (CUDA, Metal) — packed weights are consumed directly, never expanded back to FP16
+- **Ships with a DSpark speculative-decoding drafter layer** trained against the Bonsai 27B target — a lossless **1.34x** decode speedup on the CUDA serving path
+- **MLX companion**: also available as [Ternary-Bonsai-27B-mlx-2bit](https://huggingface.co/prism-ml/Ternary-Bonsai-27B-mlx-2bit) for native Apple Silicon inference
+- **1-bit companion**: the phone-class operating point (\~3.9 GB) that fits an iPhone 17 Pro Max, published in GGUF as [Bonsai-27B-gguf](https://huggingface.co/prism-ml/Bonsai-27B-gguf)
+
+## Resources
+
+- **[Whitepaper](https://github.com/PrismML-Eng/Bonsai-demo/blob/main/bonsai-27b-whitepaper.pdf)** — full methodology, benchmarks, and measurement notes
+- **[Demo & examples](https://github.com/PrismML-Eng/Bonsai-demo)** — serving, benchmarking, and integrating Bonsai
+- **Low-bit kernels**: [llama.cpp fork](https://github.com/PrismML-Eng/llama.cpp) (CUDA + Metal) · [MLX fork](https://github.com/PrismML-Eng/mlx) (Apple Silicon) · [mlx-swift fork](https://github.com/PrismML-Eng/mlx-swift) (iOS/macOS)
+- **[Discord](https://discord.gg/prismml)** — join the community for support, discussion, and updates
+
+## Model Overview
+
+| Item              | Specification                                                                                    |
+| :---------------- | :----------------------------------------------------------------------------------------------- |
+| Base model        | Derived from Qwen3.6-27B, a 27B hybrid-attention causal language model (architecture unchanged)  |
+| Parameters        | \~27.3B ternary language weights (\~24.8B backbone across 64 blocks + \~2.5B embedding/LM head) + \~0.46B vision tower (27 blocks) |
+| Architecture      | Hybrid attention (\~75% linear / \~25% full attention), SwiGLU MLP, RoPE, RMSNorm                   |
+| Context length    | 262K tokens (full-context capable on-device, enabled by the predominantly linear-attention backbone) |
+| KV cache          | Near-lossless 4-bit KV quantization; the hybrid backbone grows a full-attention cache on only 16 of 64 layers (\~4.3 GB at the full 262K window) |
+| Weight format     | GGUF Q2_0_g128: {−1, 0, +1} weights in 2-bit slots with FP16 group-wise scaling                   |
+| Low-bit coverage  | Embeddings, attention projections, MLP projections, LM head                                       |
+| Vision tower      | HQQ 4-bit; optional \~0.63 GB mmproj pack (Q8_0 container), loaded only for image input            |
+| Deployed size     | **\~7.2 GB** (5.9 GB ideal at 1.71 bits/weight; see below)                                         |
+| Acceleration      | DSpark speculative-decoding drafter layer provided                                                |
+| Backends          | llama.cpp (CUDA, Metal, CPU)                                                                      |
+| License           | Apache 2.0                                                                                        |
+
+## Weight Representation: Q2_0_g128
+
+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.
+
+### Memory Requirement
+
+| Format              | True bits/weight | Ideal size | Deployed size | Reduction (ideal) |
+| :------------------ | ---------------: | ---------: | ------------: | ----------------: |
+| FP16 (baseline)     | 16.0             | \~54 GB     | —             | 1.0x              |
+| **GGUF Q2_0_g128**  | **1.71**         | **5.9 GB** | **\~7.2 GB**   | **\~9.4x**         |
+
+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 — the only component that must stay resident for text inference; a negligible tail of normalization and scale parameters remains in higher precision.
+
+Unlike conventional low-bit builds — whose advertised labels understate their true average bit-width (a widely-used "2-bit" build of Qwen3.6-27B is really 2.8 bits/weight at 9.4 GB) — the Bonsai representation carries a bit-width that matches its name.
+
+### Shipped Components
+
+Two optional components ship alongside the language model (on-disk sizes):
+
+| Component      | Pack                               | Size     | Residency                          |
+| :------------- | :--------------------------------- | -------: | :--------------------------------- |
+| Language model | 2-bit g128 slots (Q2_0)            | 7.17 GB  | resident                            |
+| DSpark drafter | Q4_1 (default)                     | 1.95 GB  | optional — speculative decoding     |
+| DSpark drafter | bf16 (reference)                   | 7.29 GB  | optional                            |
+| Vision tower   | mmproj HQQ 4-bit (Q8_0 container)  | 0.63 GB  | optional — multimodal input only    |
+| Vision tower   | mmproj BF16 (reference)            | 0.93 GB  | optional                            |
+
+The vision tower is usually offloaded: it sits outside the accelerator's resident budget and is loaded only when an image actually arrives, so text-only serving never pays for it. A group-64 ternary pack (7.59 GB) is also published, matching the 64-value-group Q2_0 packing in llama.cpp — the same native g128 representation with each scale repeated per 64-value block.
+
+### Peak Memory at Context
+
+What a device must actually accommodate is *peak* memory — weights plus KV cache plus activations and runtime buffers (\~1.3 GB across backends). Measured, language model only, no KV-cache compression (sizes in decimal GB; the Q4_K_XL row is derived from its weight footprint plus the same measured cache-and-overhead build-up, all other rows directly measured):
+
+| Build                                | Weights | 4K ctx | 10K ctx | 100K ctx |
+| :----------------------------------- | ------: | -----: | ------: | -------: |
+| **Ternary Bonsai (llama.cpp Q2_0)**  | 7.15    | 8.4    | 8.7     | 14.7     |
+| Qwen3.6-27B "4-bit" (Q4_K_XL)        | 17.6    | 19.2   | 19.6    | 25.6     |
+| 27B 16-bit (GGUF bf16)               | 51.25   | 52.6   | 53.3    | 59.3     |
+
+The ternary build holds a **100K-token context at 14.7 GB without any KV-cache compression** — a budget that fits mainstream laptops outright; the conventional Q4_K_XL build needs \~25.6 GB before the first long document is loaded. These peaks are the conservative case, with the cache left at FP16. Enabling the 4-bit KV cache shrinks the context-dependent term \~4x: the 100K peak drops to \~10.1 GB, and the full 262K window fits in \~12.8 GB peak.
+
+## Best Practices
+
+### Generation Parameters
+
+| Parameter   | Suggested |
+| :---------- | :-------- |
+| Temperature | 0.7       |
+| Top-p       | 0.95      |
+| Top-k       | 20        |
+
+These are the settings used for all reported benchmark results (thinking mode).
+
+### System Prompt
+
+You can use a simple system prompt such as:
+
+```
+You are a helpful assistant
+```
+
+## Quickstart
+
+### llama.cpp (CUDA)
+
+```bash
+# Clone the PrismML fork of llama.cpp (includes the Q2_0_g128 hybrid-attention kernels)
+git clone https://github.com/PrismML-Eng/llama.cpp
+cd llama.cpp
+
+# Build with CUDA support
+cmake -B build -DGGML_CUDA=ON && cmake --build build -j
+
+# Download the 2-bit GGUF weights
+hf download prism-ml/Ternary-Bonsai-27B-gguf Ternary-Bonsai-27B-Q2_0.gguf --local-dir .
+
+# Run inference
+./build/bin/llama-cli \
+    -m Ternary-Bonsai-27B-Q2_0.gguf \
+    -p "Explain quantum computing in simple terms." \
+    -n 256 \
+    --temp 0.7 --top-p 0.95 --top-k 20 \
+    -ngl 99
+```
+
+### llama.cpp (Metal / macOS)
+
+```bash
+# Build with Metal support (default on macOS)
+cmake -B build && cmake --build build -j
+
+# Run inference
+./build/bin/llama-cli \
+    -m Ternary-Bonsai-27B-Q2_0.gguf \
+    -p "Explain quantum computing in simple terms." \
+    -n 256 \
+    --temp 0.7 --top-p 0.95 --top-k 20 \
+    -ngl 99
+```
+
+### llama.cpp Server
+
+```bash
+./build/bin/llama-server \
+    -m Ternary-Bonsai-27B-Q2_0.gguf \
+    --host 0.0.0.0 --port 8080 -ngl 99
+```
+
+Open the web UI at [http://127.0.0.1:8080](http://127.0.0.1:8080), or see our [llama.cpp fork](https://github.com/PrismML-Eng/llama.cpp) for more examples.
+
+> **Deploying to a phone?** The ternary build (\~7.2 GB) exceeds the \~6 GB per-app iOS memory budget and is laptop/GPU-only. Use the 1-bit companion (\~3.9 GB), which fits an iPhone 17 Pro Max via [MLX Swift](https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit).
+
+## Cross-Platform Throughput
+
+`tg128` is token-generation throughput over 128 generated tokens (the memory-bandwidth-bound, interactive phase); `pp512` is prompt-processing throughput over 512 input tokens (the compute-bound phase). Both in tokens/s, measured with `llama-bench` on this GGUF pack (custom low-bit kernels).
+
+| Platform                     | Footprint | TG128 (tok/s) | PP512 (tok/s) |
+| :--------------------------- | --------: | ------------: | ------------: |
+| Laptop (Apple M5 Max, Metal) | 7.2 GB    | 44.0          | 830           |
+| Laptop (Apple M5 Pro, Metal) | 7.2 GB    | 26.2          | 393           |
+| Laptop (Apple M4 Pro, Metal) | 7.2 GB    | 18.0          | 125           |
+| Single GPU (H100, CUDA)      | 7.2 GB    | 98.0          | 2596          |
+
+On the laptop, the FP16 baseline (\~54 GB) and even conventional "4-bit" builds (17.6 GB) do not fit at all — the meaningful statement is not a speedup ratio but that a 27B model runs interactively on an everyday laptop. The measured decode streams \~186 GB/s of weights on the M5 Pro, confirming the memory-bandwidth-dominated profile that the low-bit representation is built to exploit. The H100 row is the exception that proves the rule: at batch size 1 a datacenter GPU is limited by kernel-launch and synchronization latency rather than weight bandwidth, so the ternary and binary variants converge there (98 vs 104.8 tok/s) despite their \~1.9x difference in bytes per step.
+
+## Speculative Decoding: DSpark
+
+Ternary Bonsai 27B ships with a **DSpark** drafter layer trained against the low-bit target — a semi-autoregressive drafter with confidence-scheduled verification. Speculative decoding is lossless: verification preserves the target distribution exactly, so accepted tokens are indistinguishable from ordinary generation.
+
+The drafter is a compact **six-layer block-parallel transformer** conditioned on hidden states tapped from five evenly spaced layers of the target; its drafter-unique weights add roughly **0.5 GB at serving precision** (embeddings and output head are shared with the resident target). It follows the DSpark recipe with a diffusion-flavored block-denoising objective, survival-probability-weighted distillation, per-source-normalized hidden-state taps, and a draft block size chosen from a measured verify-cost model of the serving stack. The drafter ships 4-bit quantized — the \~1.95 GB Q4_1 pack is the default; it drafts faster than the bf16 reference at essentially unchanged draft quality, and because verification preserves the target distribution exactly, drafter precision affects only speed, never output quality.
+
+On the CUDA serving path the drafter is a measured net win — an accepted length of τ ≈ 3.7 at draft depth k = 4 turns into a **1.34x** end-to-end decode speedup on H100 (98 → 131.8 tok/s). On Apple Silicon the batch-1 verification pass does not yet amortize, so the drafter layer is not enabled by default on-device.
+
+## Benchmarks
+
+Evaluated with EvalScope + vLLM on NVIDIA H100 under identical infrastructure, decoding, and scoring, in **thinking mode** — where the model's full reasoning is exercised and the sub-4-bit collapse of conventional methods is most visible. 15 benchmarks across six skill categories. For cross-family context the table also includes Gemma-4-31B, a model of the same capability tier, with its conventional low-bit builds — the collapse below 4 bits is a property of the methods, not of one base model. Bit-widths are true averages; "vs FP16" is relative to the Qwen3.6-27B FP16 reference.
+
+| Variant                                                                    | True bpw | Footprint  | Thinking avg | vs FP16    |
+| :-------------------------------------------------------------------------- | -------: | ---------: | -----------: | ---------: |
+| Qwen3.6-27B FP16                                                            | 16.0     | 54 GB      | 85.07        | 100%       |
+| Qwen3.6-27B Q4_K_XL ("4-bit")                                               | 5.2      | 17.6 GB    | 84.99        | 99.9%      |
+| Qwen3.6-27B IQ2_XXS ("2-bit")                                               | 2.8      | 9.4 GB     | 72.73        | 85.5%      |
+| Gemma-4-31B FP16                                                            | 16.0     | 61.5 GB    | 84.58        | 99.4%      |
+| Gemma-4-31B QAT ("4-bit")                                                   | 6.0      | 23.3 GB    | 83.41        | 98.0%      |
+| Gemma-4-31B Q2_K_XL ("2-bit")                                               | 3.0      | 11.8 GB    | 73.31        | 86.2%      |
+| **Ternary Bonsai 27B**                                                      | **1.71** | **5.9 GB** | **80.49**    | **94.6%**  |
+| 1-bit Bonsai 27B                                                            | 1.125    | 3.9 GB     | 76.11        | 89.5%      |
+
+At 5.9 GB, Ternary Bonsai 27B outscores both sub-4-bit conventional builds by more than seven points at one-half to two-thirds of their size.
+
+The aggregate gap also understates *how* the conventional builds fail: their degradation is selective, concentrated on the benchmarks that demand sustained chains of reasoning. IQ2_XXS falls to 57.5 on AIME26 and 56.4 on LiveCodeBench while still scoring 88.93 on MMLU-Redux — which is why casual testing misses the collapse. Ternary Bonsai holds exactly these benchmarks, keeping AIME at 87.5–90.8 and LiveCodeBench at 82.8.
+
+### By Skill Category
+
+| Category                | Benchmarks                          | FP16  | Ternary 27B |
+| :---------------------- | :---------------------------------- | ----: | ----------: |
+| Knowledge & reasoning   | MMLU-Redux, MuSR                    | 83.15 | 76.96       |
+| Math                    | GSM8K, MATH-500, AIME25, AIME26     | 95.33 | 93.40       |
+| Coding                  | HumanEval+, MBPP+, LiveCodeBench    | 88.74 | 85.96       |
+| Instruction following   | IFEval, IFBench                     | 78.47 | 71.77       |
+| Agentic / tool calling  | BFCL v3, τ²-Bench                   | 80.00 | 74.01       |
+| Vision                  | MMMU-Pro, OCR Bench v2              | 72.61 | 65.19       |
+| **Overall (15)**        |                                     | **85.07** | **80.49** |
+
+The reasoning backbone comes through intact: math stays within two points of full precision (93.40), coding at 85.96, and the ternary model spends its extra footprint to hold the most demanding categories — agentic tool use at 74.01 and vision at 65.19 — the behaviors that conventional sub-4-bit representations lose first.
+
+### Full Per-Benchmark Results
+
+<details>
+<summary>Expand full per-benchmark results (thinking mode)</summary>
+
+| Benchmark              | FP16  | Ternary 27B |
+| :--------------------- | ----: | ----------: |
+| MMLU-Redux             | 93.42 | 88.05       |
+| MuSR                   | 72.88 | 65.87       |
+| GSM8K                  | 95.30 | 96.06       |
+| MATH-500               | 99.40 | 99.20       |
+| AIME25                 | 93.29 | 90.84       |
+| AIME26                 | 93.33 | 87.50       |
+| HumanEval+             | 95.12 | 93.90       |
+| MBPP+                  | 83.33 | 81.22       |
+| LiveCodeBench          | 87.77 | 82.75       |
+| IFEval                 | 88.91 | 85.03       |
+| IFBench (prompt-loose) | 68.03 | 58.50       |
+| BFCL v3                | 77.10 | 74.41       |
+| τ²-Bench               | 82.90 | 73.61       |
+| MMMU-Pro               | 79.94 | 68.96       |
+| OCR Bench v2           | 65.28 | 61.42       |
+| **Average (15)**       | **85.07** | **80.49** |
+
+</details>
+
+## Intelligence Density
+
+Intelligence density captures the ratio of a model's capability to its deployed size:
+
+```
+D = -log2(1 - score/100) / size_GB
+```
+
+| Variant                                                                    | Size (GB) | Benchmark avg | Intelligence Density (1/GB) |
+| :-------------------------------------------------------------------------- | --------: | -----------: | --------------------------: |
+| 1-bit Bonsai 27B                                                            | 3.9       | 76.11        | 0.530                       |
+| **Ternary Bonsai 27B**                                                      | **5.9**   | 80.49        | **0.400**                   |
+| Qwen3.6-27B IQ2_XXS                                                         | 9.4       | 72.73        | 0.199                       |
+| Gemma-4-31B Q2_K_XL                                                         | 11.8      | 73.31        | 0.162                       |
+| Qwen3.6-27B Q4_K_XL                                                         | 17.6      | 84.99        | 0.155                       |
+| Gemma-4-31B QAT                                                             | 23.3      | 83.41        | 0.111                       |
+| Qwen3.6-27B FP16                                                            | 54        | 85.07        | 0.051                       |
+| Gemma-4-31B FP16                                                            | 61.5      | 84.58        | 0.044                       |
+
+Ternary Bonsai 27B delivers **2x** the density of the densest conventional build (IQ2_XXS at 0.199) and nearly **8x** FP16 — no conventional build of Qwen3.6-27B or Gemma-4-31B exceeds 0.2. Each stored gigabyte is translated into far more usable intelligence.
+
+## Use Cases
+
+- **Laptop-local 27B agents**: full 27B reasoning and tool use on a standard laptop at \~26 tok/s, with the 262K context available for long-document analysis, full-repository code work, and other tasks that depend on holding a large working set in context
+- **Privacy-sensitive and offline settings**: on-device execution keeps prompts and data on the device by construction, and works with intermittent or no connectivity
+- **Single-GPU and commodity-GPU serving**: 27B-class quality from a single consumer or entry-level datacenter GPU, with headroom for larger batches, longer contexts, or co-resident models — combined with the KV-cache quantization, high-throughput serving and long-context document analysis become practical on a single 24 GB GPU
+- **Quality-first low-bit deployment**: when the deployment target has laptop-class memory or better, ternary is the operating point that retains the most of the full-precision model's behavior
+
+## Limitations
+
+- **The quality–footprint trade-off**: the ternary model retains 94.6% of the full-precision average, and the gap is modest and predictable — the reasoning core (math, coding) stays within a few points of baseline, with the difference concentrated in the most demanding categories
+- **Does not fit a phone**: at \~7.2 GB the ternary build exceeds the \~6 GB per-app iOS memory budget; use the 1-bit companion via [MLX Swift](https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit) for phone deployment
+- **Served in 2-bit slots today**: the deployed footprint (\~7.2 GB) sits above the representation's \~5.9 GB native target; native ternary kernels are an active engineering target and would return the remaining bandwidth and footprint advantage directly as latency and energy improvements
+- **Agentic coding** (long-horizon, multi-file, run-test-and-repair workflows) is not yet a strong target of this release; a Bonsai 27B variant tuned for agentic coding is next on the roadmap
+- **KV compression headroom**: this release standardizes on a 4-bit KV cache; early results show the key cache can be pushed toward the sub-2-bit regime — a path to still longer contexts within a fixed device-memory budget
+
+## Citation
+
+If you use Ternary Bonsai 27B, please cite:
+
+```bibtex
+@techreport{bonsai27b,
+    title   = {Bonsai 27B: Full 27B-Class Reasoning in Binary and Ternary
+               Transformer Weights --- on Laptops and Phones},
+    author  = {Prism ML},
+    year    = {2026},
+    month   = {July},
+    url     = {https://prismml.com}
+}
+```
+
+## Contact
+
+For questions, feedback, or collaboration inquiries: **contact@prismml.com**

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