NVFP4 (W4A4) quantization of `wangzhang/Qwen3.5-122B-A10B-abliterix` — the 122B/A10B Abliterix-abliterated (uncensored) Qwen3.5 MoE — packaged for vLLM serving on NVIDIA Blackwell hardware (DGX Spark GB10 / SM121). The Abliterix-trained uncensored behavior (0.5% refusal rate, KL divergence 0.0115 vs the Qwen3.5-122B-A10B baseline) is preserved while dropping weight memory from BF16 (~230 GB) to NVFP4 (~70 GB) — fitting comfortably on two DGX Spark nodes (2 × 119 GiB unified memory) with headroom for KV-cache, with the same compressed-tensors runtime path as the FP8 sibling. Compared to the FP8 W8A8 variant (bjk110/Qwen3.5-122B-A10B-abliterix-FP8, 116 GB), this NVFP4 build cuts another ~40% off-disk and unlocks the **FlashInfer-CUTLASS NVFP4 MoE backend** for higher throughput on Blackwell.

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Swift-1.5-Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16

bjonor

Unofficial 4-bit GPTQ quantization of `ukisai/Swift-1.5-Qwen3.8-27b` for low-VRAM inference, with the MTP (multi-token-prediction) draft head and the vision tower deliberately **kept unquantized**. Produced with `gptqmodel 7.3.2` on an Intel Arc Pro B70 (30.3 GiB VRAM), ~2.4 h wall time. Quantization contract is identical to the Swift 1.0 artifact `bjonor/Swift-Qwen3.8-27B-GPTQ-Int4-sym-G128-MTP-BF16`. This model is **not affiliated with, endorsed by, or supported by UkisAI or Alibaba Cloud**. It is a community quantization; the licence terms of the base models below continue to apply (see License). > **Runtime status (2026-09-25).** Verified on the stock > `vllm/vllm-openai-xpu:nightly` (`0.29.1rc1.dev422+gd05da62e9.xpu`, > `vllm-xpu-kernels 0.1.14.1`) with the two runtime patches from the recipe > repo: ready in 131 s, MTP-3 mean acceptance length 3.04 (68.0%), solo decode > 61.0 tok/s (p512/g128, median n=5), endpoint + streaming smoke test PASS. > `patch_mtp_boundary.py` is still required for requests that end exactly at > `--max-model-len`; mixed prefill + spec-decode batches run without the GDN > split-dispatch backport on this stack.

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vietnamese-bi-encoder

bkai-foundation-models

This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space and can be used for tasks like clustering or semantic search. We train the model on a merged training dataset that consists of: - MS Macro (translated into Vietnamese) - SQuAD v2 (translated into Vietnamese) - 80% of the training set from the Legal Text Retrieval Zalo 2021 challenge We use phobert-base-v2 as the pre-trained backbone.

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