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PocketAiHub/Qwopus3.6-27B-v2-MTPLX-BF16
Qwopus3.6-27B-v2-MTPLX-BF16 is a text generation model from PocketAiHub. Use it when you need the model to write or continue text. It is set up for mlx. The card lists the license as apache-2.0.
This is the full-precision BF16 MTPLX release of Jackrong/Qwopus3.6-27B-v2, pinned to source revision d0d82f4ccc9d41d4fe9595e96be4595327bb5de7. It packages the 26.896B-parameter language-model trunk in BF16 together with
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.safetensors54.6 GB · 100%
From the Hugging Face model README
This is the full-precision BF16 MTPLX release of
Jackrong/Qwopus3.6-27B-v2,
pinned to source revision
d0d82f4ccc9d41d4fe9595e96be4595327bb5de7.
It packages the 26.896B-parameter language-model trunk in BF16 together with
the model's native BF16 multi-token-prediction (MTP) sidecar for speculative
decoding on Apple Silicon. PocketAI did not train or fine-tune the underlying
model; this repository is a conversion and runtime-packaging release.
This is the BF16 master artifact used to derive smaller quantized builds. No body quantization is applied.
The model includes mtp.safetensors and a verified MTP runtime contract.
MTPLX drafts multiple future tokens with the native MTP head, then verifies
them against the full model. The output distribution remains target-verified;
the MTP head is an inference accelerator, not a separate replacement model.
This package requires MTPLX 2.4.0 or newer for native MTP acceleration.
Stock mlx-lm does not consume the split MTP sidecar by itself.
Install the runtime:
python3 -m pip install "mtplx==2.4.0"
Start the local OpenAI-compatible server with the verified settings:
mtplx quickstart \
--model PocketAiHub/Qwopus3.6-27B-v2-MTPLX-BF16 \
--download \
--profile sustained \
--depth 2 \
--port 8000
PocketAI also supports this artifact through its managed MTPLX runtime. This BF16 release is very large: the repository is about 54.7 GB before runtime and KV-cache overhead. It was verified on a 128 GB Apple Silicon Mac; lower-memory configurations have not been qualified.
The release was forged and tested with MTPLX 2.4.0 on an Apple M5 Max with
128 GB unified memory. The sustained profile selected depth 2.
| Decode mode | Throughput | Relative to AR | Acceptance by MTP position |
|---|---|---|---|
| Autoregressive | 9.333 tok/s | 1.000x | — |
| MTP depth 1 | 11.835 tok/s | 1.268x | 96.62% |
| MTP depth 2 | 14.569 tok/s | 1.561x | 94.96%, 91.60% |
| MTP depth 3 | 12.409 tok/s | 1.330x | 94.41%, 86.01%, 76.92% |
These are single-machine forge-verification measurements, not universal
performance guarantees. Throughput varies with hardware, prompt length,
sampling settings, thermals, and runtime version. The complete structured
evidence is in mtplx_runtime.json.
model-00001-of-00011.safetensors … model-00011-of-00011.safetensors — BF16 language-model trunkmodel.safetensors.index.json — trunk weight indexmtp.safetensors — BF16 native MTP sidecarmtplx_runtime.json — MTPLX contract, forge provenance, and verification evidenceReleased under the Apache License 2.0, following the upstream model. This conversion/packaging release is maintained by PocketAiHub. MTPLX is developed by Youssof Altoukhi.