Qwen3.8-27B-XYZ

Qwen3.8-27B — GGUF quantizations (XYZ)

GGUF quantizations of Qwen/Qwen3.8-27B (multimodal, hybrid SSM + attention, 64 layers + 1 MTP layer), converted with upstream llama.cpp. All files include the built-in MTP block (nextn_predict_layers=1), usable for speculative decoding.

What the XYZ series is (and isn't)

XYZ is 100% focused on coding. These quantizations are built and tuned for programming work: the per-tensor recipe keeps the parts that matter for code generation (attention K/V, SSM state, and the MTP head) in higher precision, and the build is evaluated against a code test suite so the models stay reliable on code tasks — even at low bits.

That focus is deliberate. General-knowledge domains are not the goal. There are already plenty of generalist models out there — this one is the coding one. If you need the best answer on biology, history, politics, or other non-code topics, a general-purpose build of the base model will usually serve you better. XYZ optimizes for coding behavior first, and that's the trade you're opting into here.

TL;DR — reach for XYZ when the job is writing, reading, or debugging code. For everything else it's still a capable general model, but don't expect it to out-perform an untuned build on pure knowledge tasks.

v2 — main series

All v2 files carry the -v2 suffix.

v2 — low-bit / emergency files

Usage

Serve with llama-server (OpenAI-compatible API):

llama-server -m Qwen3.8-27B-Q5-XYZ-v2.gguf --port 1234

With the vision projector (multimodal):

llama-server -m Qwen3.8-27B-Q5-XYZ-v2.gguf --mmproj mmproj-Qwen3.8-27B-bf16.gguf --port 1234

With MTP speculative decoding:

llama-server -m Qwen3.8-27B-Q5-XYZ-v2.gguf --spec-type draft-mtp --port 1234

Recommended sampling: --temp 0.6 for balanced, coherent output.

What I like to use, in a 24gb gpu

llama-server -m Qwen3.8-27B-Q5-XYZ.gguf \
--host 127.0.0.1 --port 8080 --ctx-size 128000 -b 2048 --parallel 1 \
-ngl 999 --threads 8 -ub 512 -ctk q8_0 -ctv q8_0 -fa on -kvu \
--no-mmap --temp 0.6 --spec-type draft-mtp,ngram-mod \
--spec-draft-type-k q4_0 --spec-draft-type-v q4_0 --spec-draft-n-max 6 \
--spec-draft-p-min 0.75 --spec-ngram-mod-n-match 24 --spec-ngram-mod-n-min 48 \
--spec-ngram-mod-n-max 64 --reasoning-budget 2048 --reasoning-preserve \
-mm G:\LLM\Models\mmproj-Qwen3.8-27B-bf16.gguf


using --reasoning-budget 2048 is like set to medium the thinking, use 512 for low thinking and 8196 for high, and use nothing to xHigh.

What I use for 16GB, using mtp is an option but you will be limited to 64k. You can also use q4_0 and double context but there will be degradation, 128k is a sweet spot for most cases.

llama-server -m Qwen3.8-27B-Q3-XYZ-v2.gguf --host 0.0.0.0 --port 8080
--ctx-size 128000 -b 2048 -ngl 999 --threads 4 -ub 512
-ctk q8_0 -ctv q8_0 -fa on -kvu --temp 0.6
--spec-type ngram-mod --spec-ngram-mod-n-match 24
--spec-ngram-mod-n-min 48
--spec-ngram-mod-n-max 64
--reasoning-preserve


using --reasoning-budget 2048 is like set to medium the thinking, use 512 for low thinking and 8196 for high, and use nothing to xHigh.

Extreme quantizations (Q1/Q2) — sampling to reduce hallucinations

The low-bit files (Q1Q/Q2) are more prone to hallucination — use this tuned sampling with llama-server for much more stable output:

llama-server -m Qwen3.8-27B-Q2-XYZ-v2.gguf --host 0.0.0.0 --port 1234 \
  --temp 0.3 \
  --top-p 0.9 \
  --top-k 40 \
  --repeat-penalty 1.10 \
  --repeat-last-n 512 \
  --dry-multiplier 0.8 \
  --dry-base 1.75 \
  --dry-allowed-length 2

Try more flags and combinations with https://github.com/TRI-Tech-Revolution-Intelligence/LocalLLM/releases

How to configure Pi harness to work with low, medium and high thinking limits, https://github.com/quimmedes/pi-thinking-budgets-extension

The DRY sampler (repetition-aware) is the key flag for low-bit models — --dry-multiplier 0.8 --dry-base 1.75 --dry-allowed-length 2 keeps generation on-track without over-penalizing. Works for Q1Q-XYZ-v2 too.

Notes

• Model is Apache-2.0, architecture Qwen3_5ForConditionalGeneration (hybrid SSM + attention, full attention every 4th layer), vocab 248,320, tie_word_embeddings=false.
• Quantized with upstream llama.cpp, 4 threads, --tensor-type per-tensor recipe with imatrix. Key tensors (attention K/V, SSM params, MTP head) are kept in BF16 — full precision where it matters.
• The Q1/Q2 files are experimental: below ~2.5 BPW a reasoning model can get stuck in the thinking loop on some prompts.
• Sizes are exact file sizes on disk (GiB, base 1024 — matches the sizes shown on the HF file browser).

Files

Quant ladder (v2 builds) + the new Q2.5-v3. Sizes are exact on-disk GiB (base 1024).