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lackonendes/PAW-35B-A3B-GGUF
PAW-35B-A3B-GGUF is a machine learning model from lackonendes. Use it for the machine learning task on the model card, and read the license before you ship it in a product. It is set up for gguf. The card lists the license as apache-2.0.
If this model is useful to you, support the work by following @nawariokra on X, or buy me a coffee on Ko-fi.
Downloads · 30 days
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.gguf7.9 GB · 100%
From the Hugging Face model README
If this model is useful to you, support the work by following @nawariokra on X, or buy me a coffee on Ko-fi.
<a href="https://ko-fi.com/F4P3296XP7"><img src="https://ko-fi.com/img/githubbutton_sm.svg" alt="Support me on Ko-fi" height="32"></a>
A 1.5415 bpw trellis-coded quantization of Qwen/Qwen3.6-35B-A3B that
runs on one 24 GB GPU at 133–183 tok/s.
| size | ~7.86 GB (this file: PAW-35B-A3B.gguf) |
| rate | 1.54150390625 bpw |
| speed | 133.7 tok/s mixed · 182.7 tok/s code (1× RTX 3090) |
| reasoning | 97.0% GSM8K vs parent's 97.5% — near parity |
| code | 94.0% / 93.3% retention (HumanEval+ / MBPP+) — a real gap |
This is not a standard GGUF. It uses the PAW trellis format (paw arch) and
requires the llama-paw fork.
Stock llama.cpp cannot read this model.
PAW is at parity on reasoning and about 6% down on code. If you generate code, that cost is real and statistically significant (see SCORECARD.md). If you reason, it is close to free.
Every number in the scorecard was measured by us on one RTX 3090 with byte-identical harnesses against our own Q8_0 measurement of the parent — nothing copied from model cards.
See SERVING.md for the tuned command (+35% over naive serving, byte-identical output). Short version:
llama-paw, not stock llama.cpp--spec-type draft-dflash)enable_thinking:false is required — see the loop limitationsha256 c3d999f845ca33120764262527bfb2c284acd9006ba827525df0bfe5d4c6f218 PAW-35B-A3B-paw.gguf
Apache 2.0, inherited from the base model Qwen/Qwen3.6-35B-A3B.