License
Apache-2.0 — inherited unchanged from the base model. See LICENSE and the
base model card. This is a weight-only quantization; no rights are widened.
Quantization (self-hosted, reproducible)
- Tool:
gptqmodel==7.3.2; run on a CUDA GPU (RTX 5090) with lazy checkpoint
loading; calibration: wikitext-2 (or bundled fallback text).
- Contract:
bits=4, group_size=128, desc_act=false, sym=true, lm_head=false, dynamic={"-:.*mtp.*":{}}
(MTP tensors stay BF16).
- Input 51.75 GB (BF16) → output 18.22 GB (−64.8%), 5 safetensors shards,
config arch
Qwen3_5ForConditionalGeneration, image_token_id 248056.
- 400 quantized INT4 weight tensors + 15 preserved BF16
mtp.* tensors.
Serving (Arc Pro B70)
Served with vLLM XPU 0.28.0 (kernels 0.1.12.3), MTP3 speculative decoding
with BF16 draft (DRAFT_INT4=0), prefix caching, qwen3_xml tool-call parser, fp8 KV,
KV cache 4.3 GiB (KV_CACHE_MEMORY_BYTES=4617089843), max-num-seqs=1.
No repetition/presence penalty — sampling is pure Qwen defaults from the
model's generation_config.json (temperature 1.0 / top_k 20 / top_p 0.95);
the deploy repo's patch_tile_mask.py boot patch covers the vLLM 0.28.0
!-degeneration bug instead. Benchmark/report details live in the
deployment repository
(benchmarks/ section).
One-click container: zrlu/qwen38-27b-arc-pro-b70:latest on Docker Hub
(entrypoint auto-downloads this repo to /model on first start; switch to any
other published B70 quant with -e HF_REPO=zrlu/<repo>), Dockerfile and
pi-agent setup in the deploy repository.
Usage
# standalone / evalfrom transformers import AutoModelForCausalLM, AutoTokenizertok = AutoTokenizer.from_pretrained("zrlu/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70")m = AutoModelForCausalLM.from_pretrained( "zrlu/Huihui-Qwen3.8-27B-abliterated-GPTQ-Int4-sym-G128-MTP-BF16-B70", device_map="auto")
Signed: zrlu. GPU: Arc Pro B70 (0xe223) + RTX 5090 (quantization only).