Duplicate from prism-ml/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
+  value: 87.08
+  source:
+    url: https://huggingface.co/prism-ml/Bonsai-27B-gguf
+    name: Model Card

.eval_results/gsm8k.yaml

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+- dataset:
+    id: openai/gsm8k
+    task_id: gsm8k
+  value: 92.8
+  source:
+    url: https://huggingface.co/prism-ml/Bonsai-27B-gguf
+    name: Model Card

.eval_results/mmmu_pro.yaml

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+- dataset:
+    id: MMMU/MMMU_Pro
+    task_id: mmmu_pro_vision
+  value: 60.48
+  source:
+    url: https://huggingface.co/prism-ml/Bonsai-27B-gguf
+    name: Model Card

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LICENSE.txt

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NOTICE.txt

@@ -0,0 +1,4 @@
+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
+- 1-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>
+
+# 1-bit Bonsai 27B — GGUF
+
+Full 27B-class reasoning in binary transformer weights, for llama.cpp (CUDA, Metal, CPU)
+
+> **\~14.2x** smaller than FP16 | **\~90%** of FP16 intelligence retained | **\~44 tok/s** on an Apple M5 Pro laptop
+
+## Highlights
+
+- **\~3.9 GB** deployed footprint (down from \~54 GB FP16) — a 27B model on everyday laptops and single GPUs
+- **Retains thinking, reasoning, and agentic behavior** deep in the sub-4-bit regime, where conventional low-bit representations collapse — 76.11 average across 15 thinking-mode benchmarks (89.5% of FP16), including math at 91.66 and coding at 81.88
+- **End-to-end binary language weights** across embeddings, attention projections, MLP projections, and LM head, at a *true* 1.125 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 Q1_0_g128** format with custom 1-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 **1.37x** decode speedup on the CUDA serving path
+- **MLX companion**: also available as [Bonsai-27B-mlx-1bit](https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit) for native Apple Silicon inference, including iPhone (\~11 tok/s on iPhone 17 Pro Max via MLX Swift)
+- **Ternary companion**: the quality-oriented operating point (\~7.2 GB, 95% of FP16) is also published in GGUF as [Ternary-Bonsai-27B-gguf](https://huggingface.co/prism-ml/Ternary-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 binary 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 Q1_0_g128: {−1, +1} weights 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     | **\~3.9 GB** (\~14.2x smaller than FP16)                                                            |
+| Acceleration      | DSpark speculative-decoding drafter layer provided                                                |
+| Backends          | llama.cpp (CUDA, Metal, CPU)                                                                      |
+| License           | Apache 2.0                                                                                        |
+
+## Weight Representation: Q1_0_g128
+
+Each weight is a single sign bit: `0` maps to `−scale`, `1` maps to `+scale`. Every group of 128 weights shares one FP16 scale factor.
+
+Effective bits per weight: **1.125** (1 sign bit + 16-bit scale amortized over 128 weights) — an idealized \~14.2x reduction vs FP16. This is the most aggressive operating point in the Bonsai 27B family: it minimizes both stored footprint and the weight traffic incurred at every decoding step. The GGUF Q1_0_g128 pack is the model's native layout — ideal and deployed sizes match.
+
+### Memory Requirement
+
+| Format              | True bits/weight | Size        | Reduction   |
+| :------------------ | ---------------: | ----------: | ----------: |
+| FP16 (baseline)     | 16.0             | \~54 GB      | 1.0x        |
+| **GGUF Q1_0_g128**  | **1.125**        | **\~3.9 GB** | **\~14.2x**  |
+
+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 | 1-bit g128 (Q1_0)                  | \~3.9 GB  | resident                            |
+| DSpark drafter | Q4_1 (default)                     | 1.79 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.
+
+### 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 |
+| :----------------------------------- | ------: | -----: | ------: | -------: |
+| **1-bit Bonsai (llama.cpp Q1_0)**    | 3.79    | 5.2    | 5.6     | 11.6     |
+| 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 1-bit build holds a **100K-token context at 11.6 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 \~6.8 GB, and the full 262K window fits in \~9.4 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 Q1_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 1-bit GGUF weights
+hf download prism-ml/Bonsai-27B-gguf Bonsai-27B-Q1_0.gguf --local-dir .
+
+# Run inference
+./build/bin/llama-cli \
+    -m Bonsai-27B-Q1_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 Bonsai-27B-Q1_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 Bonsai-27B-Q1_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?** iPhone deployment uses the MLX Swift runtime — see [Bonsai-27B-mlx-1bit](https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit) (\~11 tok/s on iPhone 17 Pro Max).
+
+## 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) | 3.9 GB    | 66.4          | 874           |
+| Laptop (Apple M5 Pro, Metal) | 3.9 GB    | 44.2          | 421           |
+| Laptop (Apple M4 Pro, Metal) | 3.9 GB    | 26.0          | 133           |
+| Single GPU (H100, CUDA)      | 3.9 GB    | 104.8         | 2755          |
+
+On the edge platforms 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 on the device in the first place. 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 binary and ternary variants converge there (104.8 vs 98 tok/s) despite their \~1.9x difference in bytes per step.
+
+Decode energy on the M5 Pro measures **0.275 mWh/token** (with the DSpark drafter enabled) — an order of magnitude more energy-efficient per token than datacenter GPUs (0.63–1.32 mWh/token across the GPU classes). Local inference is not just private and low-latency but cheap in energy.
+
+## Speculative Decoding: DSpark
+
+1-bit 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 \~1.79 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 ≈ 3.6 at draft depth k = 4 turns into a **1.37x** end-to-end decode speedup on H100 (104.8 → 143.8 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%**  |
+
+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. 1-bit Bonsai holds exactly these benchmarks, keeping AIME above 87 at a third of IQ2_XXS's footprint.
+
+### By Skill Category
+
+| Category                | Benchmarks                          | FP16  | 1-bit 27B |
+| :---------------------- | :---------------------------------- | ----: | --------: |
+| Knowledge & reasoning   | MMLU-Redux, MuSR                    | 83.15 | 73.39     |
+| Math                    | GSM8K, MATH-500, AIME25, AIME26     | 95.33 | 91.66     |
+| Coding                  | HumanEval+, MBPP+, LiveCodeBench    | 88.74 | 81.88     |
+| Instruction following   | IFEval, IFBench                     | 78.47 | 65.74     |
+| Agentic / tool calling  | BFCL v3, τ²-Bench                   | 80.00 | 66.03     |
+| Vision                  | MMMU-Pro, OCR Bench v2              | 72.61 | 59.57     |
+| **Overall (15)**        |                                     | **85.07** | **76.11** |
+
+The reasoning backbone comes through intact: math stays at 91.66 — within four points of full precision — and coding at 81.88, the behaviors that conventional sub-4-bit representations lose first. The 1-bit model trades part of the ternary model's margin on the most demanding categories for the smallest footprint in the family.
+
+### Full Per-Benchmark Results
+
+<details>
+<summary>Expand full per-benchmark results (thinking mode)</summary>
+
+| Benchmark              | FP16  | 1-bit 27B |
+| :--------------------- | ----: | --------: |
+| MMLU-Redux             | 93.42 | 82.75     |
+| MuSR                   | 72.88 | 64.02     |
+| GSM8K                  | 95.30 | 92.80     |
+| MATH-500               | 99.40 | 98.00     |
+| AIME25                 | 93.29 | 88.75     |
+| AIME26                 | 93.33 | 87.08     |
+| HumanEval+             | 95.12 | 89.63     |
+| MBPP+                  | 83.33 | 79.60     |
+| LiveCodeBench          | 87.77 | 76.40     |
+| IFEval                 | 88.91 | 79.11     |
+| IFBench (prompt-loose) | 68.03 | 52.36     |
+| BFCL v3                | 77.10 | 70.72     |
+| τ²-Bench               | 82.90 | 61.34     |
+| MMMU-Pro               | 79.94 | 60.48     |
+| OCR Bench v2           | 65.28 | 58.65     |
+| **Average (15)**       | **85.07** | **76.11** |
+
+</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                       |
+
+1-bit Bonsai 27B delivers roughly **2.7x** the density of the densest conventional build (IQ2_XXS at 0.199) and over **10x** 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 any standard laptop at \~26–66 tok/s (M4 Pro through M5 Max), with the 262K context available for long-document analysis and full-repository code work
+- **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
+- **Phone deployment via MLX**: the same weights ship as [Bonsai-27B-mlx-1bit](https://huggingface.co/prism-ml/Bonsai-27B-mlx-1bit) — the first 27B-class model to run on a phone
+
+## Limitations
+
+- **The quality–footprint trade-off**: the binary model retains 89.5% 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; if quality is the priority, consider the ternary GGUF build (94.6%)
+- **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; Bonsai's tolerance to KV-cache error grows with context length, and 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 1-bit 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**