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lackonendes/PAW-27B-GGUF
PAW-27B-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.
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.gguf7.8 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.
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A ~2.17 bpw trellis-coded quantization of Qwen/Qwen3.8-27B that runs on
one 24 GB GPU with a 256k context at 38–78 tok/s.
| size | 7.814 GB (this file: PAW-27B.gguf) |
| context | 262,144 tokens on a single RTX 3090 |
| speed | 78.4 tok/s short · 47.8 tok/s at 191k code context (MTP drafter) |
| code | 87.2% HumanEval+, 75.9% MBPP+ |
| general | 58.6% MMLU-Pro, 94% GSM8K |
This is not a standard GGUF. It uses the PAW trellis format (paw-dense
arch) and requires the llama-paw
fork. Stock llama.cpp cannot read this model.
Against two same-family IQ2XXS GGUFs, measured by us on the same machine with byte-identical harnesses:
| size | MMLU-Pro (500) | GSM8K (100) | HumanEval+ | MBPP+ | IFBench-64 strict | |
|---|---|---|---|---|---|---|
| PAW-27B | 7.814 GB | 58.6% | 94% | 87.2% | 75.9% | 23.44% |
| Unsloth IQ2XXS | 7.266 GB | 46.2% | 93% | 84.1% | 72.8% | 14.06% |
| AtomicChat AD-IQ2XXS | 8.977 GB | 53.0% | 94% | 73.8% | 71.7% | 20.31% |
PAW-27B leads MMLU-Pro, HumanEval+, MBPP+ and strict IFBench. Unsloth's file is 0.548 GB smaller and gives up 12.4 points of MMLU-Pro. AtomicChat is larger than PAW-27B and does not recover the coding gap.
These are peer comparisons, not retention numbers. No HumanEval, MBPP or
GSM8K figure exists for Qwen/Qwen3.8-27B at any precision, so there is no
published denominator to claim retention against on those axes, and we are not
going to invent one. What the table shows is that at roughly the same file
size this quantization is ahead of the two obvious alternatives — not how much
of the unquantized parent survives.
The references that do exist for this parent are IFBench (79.5 unquantized, official Qwen) and LiveCodeBench v6 (90.3). The IFBench figure here is a 64-item defect sentinel, not the 300-item benchmark, so it is directional only and not comparable to the official 79.5.
See SERVING.md for the full configuration. Short version:
llama-paw, not stock llama.cpp--spec-type draft-mtp) — 22.7 → 78.4 tok/s, lossless-ub 2048 is a hard ceiling at -c 262144, not a preference-fa on is required for it<think> block verbatim and then repeats the action it
went with. Serve with --no-reasoning-preserve and configure the client
not to return reasoning_content. Details and measurements in
SERVING.md.--reasoning-budget 2048, not -1.➞ (U+279E) and aborts
generation on prompts containing it; in this fork the server returns HTTP
500. Eight HumanEval prompts contain it and it reproduces deterministically
under greedy decoding. This is a model defect, not a harness artifact, and
the affected tasks are scored as failures in the numbers above.sha256 f5ec4b409de07f43bbe7bb5d9b3215181c53f5a26876821f732839a2ea3ab8cf PAW-27B.gguf
Apache 2.0, inherited from the base model Qwen/Qwen3.8-27B.