---

license: apache-2.0

library_name: llama.cpp

pipeline_tag: text-generation

base_model:

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

tags:

- gguf

- llama.cpp

- qwen35

- ternary

- tq2_0

- mtp

- nextn

- speculative-decoding

- local-llm

- self-contained

- q2_k

---

Ternary Bonsai 27B with Embedded MTP — GGUF

> One target, one embedded NextN predictor, one deployment artifact per representation.

qwen35 · 64 target blocks · 1 embedded NextN block · 866 tensors · Apache-2.0

---

Release summary

This repository publishes two self-contained GGUF representations of the same

logical model graph:

1. TQ2_0 — primary, native ternary representation
2. Q2_K — compatibility-oriented standard representation

Each file contains both the 27B-class target and its embedded Qwen-style

NextN/MTP predictor. There is no sidecar drafter, no second model identity and

no --model-draft lifecycle.

The two files preserve the same logical tensor set, names, shapes and ordering.

They do not claim bit-identical dequantized values, logits or generated text:

the storage and dequantization contracts are different.

Artifacts

| Priority | Representation | File | Exact size | SHA-256 |

|---:|---|---|---:|---|

| 1 | Native TQ2_0 | Ternary-Bonsai-27B-MTP-TQ2_0.gguf | 8,785,215,776 bytes / 8.18 GiB | 480c695b0de5b78b720d7f500eb3e1650359d0ac8e18c29b231a14b2d9c9bf20 |

| 2 | Standard Q2_K | Ternary-Bonsai-27B-MTP-Q2_K.gguf | 10,491,466,016 bytes / 9.77 GiB | 850b4f4041c7d73e4442f23b1c70464f68fb76bf8717bc13ada1562680684410 |

1. Native TQ2_0 — primary artifact

Choose this file when the target runtime supports the native TQ2_0 tensor

type.

• smallest artifact in this repository;
• 480 tensors stored natively as TQ2_0;
• embedded target and MTP paths in one GGUF;
• preserves the native ternary storage contract;
• requires runtime support for qwen35, draft-mtp and TQ2_0.

2. Standard Q2_K — compatibility artifact

Choose this file when standard Q2_K tensor support is preferable to native

TQ2_0 support.

• all 480 native ternary tensors are represented as ordinary Q2_K;
• the 17 tensors that were already Q2_K remain Q2_K;
• final inventory: 497 Q2_K tensors;
• no private GGUF tensor type and no custom tensor-type patch;
• larger than the native artifact because standard Q2_K carries its own

block metadata;

• still requires runtime support for the qwen35 architecture and embedded

draft-mtp execution.

The Q2_K file is a deterministic representation transform, not a generic

calibration-based post-training quantization pass.

Which file should I use?

| Requirement | Recommended file |

|---|---|

| Minimum repository artifact size | Ternary-Bonsai-27B-MTP-TQ2_0.gguf |

| Native ternary execution | Ternary-Bonsai-27B-MTP-TQ2_0.gguf |

| Runtime already supports TQ2_0 | Ternary-Bonsai-27B-MTP-TQ2_0.gguf |

| Prefer standard Q2_K tensor storage | Ternary-Bonsai-27B-MTP-Q2_K.gguf |

| Runtime lacks TQ2_0 but supports Q2_K | Ternary-Bonsai-27B-MTP-Q2_K.gguf |

| Need embedded MTP without a sidecar drafter | either file |

| Need architecture-independent output equivalence between representations | neither; no such claim is made |

Model graph

self-contained GGUF
│
├── qwen35 target
│   ├── target token embedding
│   ├── 64 transformer blocks
│   ├── final normalization
│   └── target output path
│
└── embedded MTP / NextN path
    ├── dedicated MTP token embedding
    ├── 1 Qwen-style NextN predictor block
    ├── MTP normalization
    └── MTP output path

The ordinary target path and the speculative path resolve from the same file

and the same target identity.

| Deployment property | External drafter | This repository |

|---|---:|---:|

| Target files | 1 | 1 |

| Drafter files | 1 | 0 |

| Runtime model identities | 2 | 1 |

| Independent target/drafter version skew | possible | structurally removed |

| Separate drafter checksum | required | not applicable |

| --model-draft / -md | required | not used |

| Embedded draft-mtp path | not intrinsic | yes |

Tensor inventories

The files contain the same 866 logical tensors. The difference is the storage

type of the 480 ternary tensors.

Native TQ2_0

TQ2_0  480
Q2_K    17
F32    359
Q4_K     1
Q5_K     6
Q6_K     3
-------------
total   866

Standard Q2_K

Q2_K   497
F32    359
Q4_K     1
Q5_K     6
Q6_K     3
-------------
total   866

Q2_K representation contract

The compatibility artifact converts each native ternary block into an ordinary

84-byte Q2_K block:

16 bytes  scale/min metadata
64 bytes  packed 2-bit symbols
 2 bytes  fp16 d
 2 bytes  fp16 dmin
---------
84 bytes  total

For the 480 converted tensors, the transform follows this contract:

packed Q2_K symbols = packed native ternary symbols

group metadata =
    0x00  when all 16 symbols in the logical group are the center symbol
    0xFF  otherwise

Q2_K d    = fp16(fp32(native_scale) / 15)
Q2_K dmin = fp16(fp32(native_scale) / 15)

Consequences:

• the packed discrete symbol identities are preserved by construction;
• the output uses the standard Q2_K block layout;
• the conversion is deterministic for a fixed source artifact;
• the conversion does not claim floating-point equivalence with native

TQ2_0 dequantization;

• downstream logits and generated text may differ between representations.

This distinction is intentional: the Q2_K variant is a compatibility

representation of the ternary code field, not an assertion that the two

dequantizers are interchangeable.

Runtime contract

Use a llama.cpp revision that supports:

• the qwen35 architecture;
• the embedded draft-mtp path;
• TQ2_0 for the primary artifact, or standard Q2_K for the compatibility

artifact.

The Q2_K variant removes the native tensor-type requirement. It does not

remove the architecture or MTP-runtime requirements.

Runtime option names can change across llama.cpp revisions. Confirm the

available flags with:

llama-cli --help | grep -E 'draft-mtp|spec-draft|model-draft'

Download

Primary TQ2_0

hf download vinpix/Ternary-Bonsai-27B-Stock-MTP-GGUF \
  Ternary-Bonsai-27B-MTP-TQ2_0.gguf \
  SHA256SUMS \
  --local-dir .

Compatibility Q2_K

hf download vinpix/Ternary-Bonsai-27B-Stock-MTP-GGUF \
  Ternary-Bonsai-27B-MTP-Q2_K.gguf \
  SHA256SUMS \
  --local-dir .

Verify either or both downloaded artifacts:

sha256sum -c SHA256SUMS --ignore-missing

Inference

Select one artifact:

MODEL="Ternary-Bonsai-27B-MTP-TQ2_0.gguf"
# or:
# MODEL="Ternary-Bonsai-27B-MTP-Q2_K.gguf"

Ordinary target decode

llama-cli \
  -m "$MODEL" \
  -p "Derive a cost model for speculative decoding." \
  -n 256

Embedded MTP

llama-cli \
  -m "$MODEL" \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  -p "Derive a cost model for speculative decoding." \
  -n 256

There is deliberately no -md / --model-draft argument.

OpenAI-compatible server

llama-server \
  -m "$MODEL" \
  --spec-type draft-mtp \
  --spec-draft-n-max 2 \
  --host 127.0.0.1 \
  --port 8080

Speculative-decoding economics

Embedded MTP is beneficial only when the accepted speculative work amortizes

predictor and verification cost:

(C_draft + C_verify) / E[committed draft tokens] < C_target-step

Acceptance rate is an intermediate statistic, not the optimization objective.

The relevant endpoint is end-to-end committed-token throughput under a fixed

workload and configuration.

A minimal controlled comparison is:

A. ordinary target decode
B. draft-mtp with n_max = 1
C. draft-mtp with n_max = 2

Hold constant:

• exact model file and checksum;
• prompt corpus and prompt order;
• context length;
• sampler chain and sampler parameters;
• random seed where supported;
• batch and micro-batch sizes;
• thread count and affinity;
• GPU-layer split and device placement;
• KV-cache types;
• warm-up policy;
• output token budget.

Report separately:

• prompt-evaluation throughput;
• committed generation throughput;
• attempted and accepted draft tokens;
• acceptance ratio;
• time to first token;
• peak host memory and device memory;
• full command line and runtime revision.

Do not compare the native and compatibility representations while attributing

the entire difference to MTP: that changes two independent variables at once.

No hardware-independent speedup claim is made by this repository.

Portable artifact validation

The public claims below are properties of the files and metadata, not of a

specific workstation.

| Check | Native TQ2_0 | Standard Q2_K |

|---|---:|---:|

| Exact byte length recorded | pass | pass |

| SHA-256 recorded | pass | pass |

| GGUF V3 parse | pass | pass |

| Architecture metadata: qwen35 | pass | pass |

| Logical tensor count: 866 | pass | pass |

| Target depth: 64 blocks | pass | pass |

| Embedded NextN depth: 1 block | pass | pass |

| Tensor-name, shape and order inventory | pass | pass |

| Expected type histogram | pass | pass |

| Converted native tensors | not applicable | 480 / 480 |

| Public metadata privacy scan | pass | pass |

| Hub object size and content hash verification | pass | verified at publication |

The model card intentionally publishes no workstation-specific throughput,

device name, driver name, local path, hostname or private build log.

Integrity

The checksum is the authoritative artifact identity; the filename is a

human-readable label.

| File | SHA-256 |

|---|---|

| Ternary-Bonsai-27B-MTP-TQ2_0.gguf | 480c695b0de5b78b720d7f500eb3e1650359d0ac8e18c29b231a14b2d9c9bf20 |

| Ternary-Bonsai-27B-MTP-Q2_K.gguf | 850b4f4041c7d73e4442f23b1c70464f68fb76bf8717bc13ada1562680684410 |

Verify explicitly:

printf '%s  %s\n' \
  '480c695b0de5b78b720d7f500eb3e1650359d0ac8e18c29b231a14b2d9c9bf20' \
  'Ternary-Bonsai-27B-MTP-TQ2_0.gguf' \
  | sha256sum -c -

printf '%s  %s\n' \
  '850b4f4041c7d73e4442f23b1c70464f68fb76bf8717bc13ada1562680684410' \
  'Ternary-Bonsai-27B-MTP-Q2_K.gguf' \
  | sha256sum -c -

Construction and provenance

This is a model-engineering artifact. It is not a new pretraining run and not

an instruction fine-tune.

The final graph combines:

• a pinned Ternary Bonsai 27B target trunk;
• a Qwen3.6-derived NextN/MTP donor package;
• a dedicated MTP embedding and output path;
• a stock-compatible qwen35 GGUF graph;
• two published storage representations of the same logical tensor graph.

| Role | Immutable source | Revision | Source SHA-256 |

|---|---|---|---|

| Ternary target trunk | prism-ml/Ternary-Bonsai-27B-gguf | 3f8cc399dde45ac0475d023634974407af34907c | f659ca3dd7e28ada5d8b5f3637862d0d51ef433bde032ec4c8990ed27c91a385 |

| MTP donor package | lym00/Qwen3.6-27B-MTP-ONLY-GGUF | 03b35db648da71e23ecfb239f70661e069b16054 | 97697fc5278d4bfd0afaa733ba28c4338cb0396b98d5f5acb781e515f72b52c0 |

The donor package identifies Qwen3.6-derived MTP material but does not pin a

separate underlying Qwen source revision. This card does not invent one.

The predictor was grafted from a compatible donor. It was not jointly trained

with the final ternary target.

See NOTICE for attribution and LICENSE for terms.

Scope of claims

This repository claims:

• a self-contained GGUF graph with target and embedded MTP paths;
• exact artifact sizes and cryptographic identities;
• the documented tensor inventories and graph structure;
• complete conversion coverage for the 480 native ternary tensors in the

Q2_K artifact;

• standard Q2_K storage for the compatibility representation.

This repository does not claim:

• a new pretrained or fine-tuned model;
• joint training of the final target and predictor;
• output equivalence between TQ2_0 and Q2_K;
• preservation of upstream benchmark scores after graph construction or

quantization;

• a universal MTP speedup;
• compatibility with every historical or future runtime revision;
• multimodal capability or inclusion of a vision projector;
• endorsement by Prism ML, Qwen, Alibaba Cloud, lym00 or llama.cpp.

Known limitations

• Aggressive low-bit representations can affect accuracy, calibration and

long-context behavior.

• The grafted predictor may be suboptimal relative to a predictor jointly

trained against the final target.

• Speculative decoding can improve, match or reduce throughput depending on

acceptance, backend, context, sampler and device placement.

• The Q2_K compatibility representation is larger than native TQ2_0.
• A successful parse and exact tensor inventory do not substitute for

task-specific evaluation.

• Reproducible performance evaluation requires publishing the complete runtime

and workload configuration.

License and attribution

Repository artifacts are distributed under Apache-2.0, subject to the included

LICENSE and NOTICE files and the terms of the source artifacts.

This independent release is not endorsed by Prism ML, Qwen, Alibaba Cloud,

lym00 or the llama.cpp project.

Citation

@misc{vinpix2026ternarybonsai27bembeddedmtp,
  title  = {Ternary Bonsai 27B with Embedded MTP: Native TQ2_0 and Standard Q2_K GGUF},
  author = {vinpix},
  year   = {2026},
  url    = {https://huggingface.co/vinpix/Ternary-Bonsai-27B-Stock-MTP-GGUF}
}

---

Primary representation: native TQ2_0

Compatibility representation: standard Q2_K

Deployment boundary: one GGUF, one checksum, no sidecar drafter