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AI Models 84759

• laya-multilingual-ONNX↗

  onnx-community

• Ling-3.0-tiny-ONNX↗

  onnx-community

• InternOmni↗

  OpenGVLab

• InternVL3-9B-AWQ↗

  OpenGVLab

• Qwen3-Embedding-0.6B-int4-cw-ov↗

  OpenVINO

• tiny-random-qwen3-tts↗

  optimum-intel-internal-testing

• shamrock-0-e4b↗

  oxalis-ink

• PP-OCRv5_server_rec_onnx↗

  PaddlePaddle

• PIILOT_NER_Model↗

  ParkJunSeong

  This model is a fine-tuned version of monologg/koelectra-base-v3-discriminator for **Personally Identifiable Information (PII) Detection** in Korean text. 이 모델은 한국어 텍스트 내에서 개인정보(이름, 주민등록번호, 전화번호, 이메일 등)를 식별하기 위해 KoELECTRA를 기반으로 미세조정(Fine-tuning)되었습니다. - **Developed by:** ParkJunSeong - **Shared by:** ParkJunSeong - **Language(s):** Korean - **License:** Apache-2.0 - **Base model:** monologg/koelectra-base-v3-discriminator - **Task:** Token Classification (NER)

• Qwen3.8-Flash-Next-NVFP4↗

  primitive-ai

  Qwen3.8-Flash-Next is 360 GB in BF16 and needs two data-center GPUs in FP8. This build serves it on one 96 GB Blackwell : 88.8 GiB of VRAM, the 51B n-gram table in host RAM, and no patched runtime. Two flags are load-bearing on a single GPU. `--distributed-executor-backend mp`: the default single-GPU executor never starts the n-gram offload worker, and the first forward waits on it forever — the server looks healthy and hangs. `VLLM_PLE_OFFLOAD_READY_TIMEOUT=1800`: the worker loads a 95 GB table before serving and the 600 s default can expire first. Host needs about 100 GB of free RAM. The reasoning parser and tool-call parser in this block are validated on this exact checkpoint: thinking lands in `reasoning` with no markup in `content`, and tool calls arrive as structured `tool_calls` with valid JSON arguments and `finish_reason: tool_calls`. If you have seen other NVFP4 cards warn that the automatic MoE backend picks `FLASHINFER_TRTLLM` and corrupts output: on this image on an RTX PRO 6000 the automatic choice is `FLASHINFER_CUTLASS`, printed at boot, and every number on this card went through it. Measured on the mixed sibling (same expert kernel), `--moe-backend marlin` decodes 10% faster single-stream (93.2 vs 84.6 tok/s) and 1–3% slower at concurrency 32, with TTFT up 12%; tool-calling under Marlin 79.5 (n=1). Marlin does not combine with MTP on this image: the BF16 MTP draft inherits the backend and refuses it, and the draft-scoped override in `--speculative-config` is ignored by Model Runner V2 until vllm-project/vllm#54788 lands. Choose MTP for interactive use, Marlin only without speculation. On vLLM nightly builds from early September the SM120 selector picks Marlin by default anyway (vllm#54788); the flag matters on this image. Two more flags are in the block since 2026-09-06: without them the image's defaults do not boot on a 96 GB card. A field report found it first and we reproduced both failures on this box the same day. Without MTP the default `--max-num-seqs` (1024) exceeds the Mamba state blocks left at 0.92 utilization (598 here) and CUDA graph capture aborts with `Engine core initialization failed`; with MTP at the native 262,1