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PollardWeights/Qwen3.8-27B-Pollard

sourceHugging Faceapache-2.0updated 13d agoView on Hugging Face
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Model Card

Qwen3.8-27B — Pollard

### Pollard shrank this model: 55.56 GB (f16) → 12.08 GB — 78% smaller, 4.6× down. The smallest rung here; larger, higher-fidelity rungs are listed below. | format | this model's size | |---|---:| | f16 | 55.56 GB | | Q80 | ~29.45 GB | | Q6K | ~22.78 GB | | Q4KM | ~16.11 GB | | PollardMix (this repo's IQ3_S) | 12.08 GB |

Pollard builds of Qwen/Qwen3.8-27B made with Pollard Weights — a ladder of measured-allocation quants (bits placed by per-layer sensitivity, not a uniform crush).

Standard GGUF — runs in stock llama.cpp / ik_llama.cpp, Ollama, LM Studio. Trellis (IQ*_KT) files need ik_llama.cpp; the K-quants run anywhere.

Model details

Parameter count~27.8B
Architectureqwen3_5
Input supporttext
imatrixno
Perplexity measuredyes — table below

Which file should I choose?

Every rung is the same weights, sized to a different RAM budget by the measured allocation. Pick the largest one that fits your machine with room for context:

  • —~24 GB RAM / VRAM → `Q6_K` (22.43 GB).
  • —~18 GB RAM / VRAM → `IQ4_XS` (15.72 GB).
  • —~14 GB RAM / VRAM → `IQ3_S` (12.08 GB).

Available files

filePPLsizetok/sMean KLDnotes
Qwen3.8-27B-Pollard-IQ3_S.gguf(see repo)12.08 GB(16 GB tier)—IQ3_S
Qwen3.8-27B-Pollard-IQ4_XS.gguf—15.72 GB24 GB tier—IQ4_XS
Qwen3.8-27B-Pollard-Q6_K.gguf—22.43 GB32 GB tier—Q6_K

tok/s is hardware-specific; the machine it was measured on is stated in the errata.

Multimodal

Vision needs the projector shipped alongside: `mmproj-Qwen3.8-27B-bf16.gguf` — download it too and pass it with --mmproj. It is not quantized; it is small and the text ladder is where the size lives.

bash
llama-server -m Qwen3.8-27B-Pollard-IQ3_S.gguf --mmproj mmproj-Qwen3.8-27B-bf16.gguf -ngl 99

Download a specific file

bash
pip install -U "huggingface_hub[cli]"
hf download PollardWeights/Qwen3.8-27B-Pollard \
  --include "Qwen3.8-27B-Pollard-IQ3_S.gguf" --local-dir ./

How to run

These are standard GGUF and run with llama.cpp:

bash
llama-server -hf PollardWeights/Qwen3.8-27B-Pollard:IQ3_S

or from a local file:

bash
llama-cli    -m Qwen3.8-27B-Pollard-IQ3_S.gguf -ngl 99 -p "Explain why the sky is blue."
llama-server -m Qwen3.8-27B-Pollard-IQ3_S.gguf -ngl 99      # OpenAI-compatible API + web UI at :8080

They also work in anything built on llama.cpp — LM Studio, koboldcpp, Jan, ramalama, Ollama (ollama run hf.co/PollardWeights/Qwen3.8-27B-Pollard).

ARM / AVX

llama.cpp repacks weights into an interleaved layout at load time for faster inference on ARM and AVX machines — no special file needed, online repacking covers these quants. The old Q4_0_4_4/4_8/8_8 variants are not required.

Errata

  • —Trellis (IQ*_KT) quants need ik_llama.cpp to build/run; K-quants run in any recent llama.cpp.
  • —Measured allocation places bits by per-layer sensitivity under a size budget.
  • —Single machine; replication invited.

Credits & license

  • —Base model: `Qwen/Qwen3.8-27B`
  • —Quantization tooling: llama.cpp (ggml-org)
  • —Method + tooling: Pollard Weights — measure first, no claim before a number.
  • —License: apache-2.0, inherited from the base model.

Built with [Pollard Weights](https://github.com/WestWaters/pollard-weights) — frontier models, small hardware, no compromise.