저장한 도구도구 추천
FOXAI SEARCH

전체 검색

AI 모델 34

  • LTX2.3-10Eros-1.5-GGUF↗

    vantagewithai

    v1.5 Changelog: 1.4 and DMD only struggled with one thing, explicit motion and anatomy that was easy to access in version 1.2 and sulphur. All that data is still inside the model; versions 1.2, 1.3, 1.4, and 1.5 are almost all the same model. The difference is how the attn_2 and attn_1 gates are handled. 1.5 is 1.4 with a linear ramp across the gates to open up those explicit details from sulphur like they are on version 1.2's blocks. It still mostly maintains 1.4's base model style prompting and more stable outputs and structure. Because it is a hybrid of a model made for DMD and a model made for LTX distilled lora use, it goes best with a hybrid lora to make use of the motion vareity. Currently the hybrid block DMD lora here seems to work nicely: https://huggingface.co/TenStrip/LTX2.3_DMD_Lora/blob/main/LTX2.3_DMD_hybrid_v2.safetensors or these ones: https://huggingface.co/maximsobolev275/LTX2.3-DMD-Distill-Lora-Hybrids v1.4 Changelog: Built off 1.3 and bringing back explicit prompting and motion hopefully without any kind of anatomy redraw or negative tendency. Still requires intense prompt refinement. This version is set up to be trained on to fix it into a real base, it doesn't depict anatomy well but it also isn't confused by it which is priority for the first lora passes I'll do. Use lora stacks to improve it if needed. v1.3 Changelog: Only designed to work with DMD lora on a workflow like my V5 DMD. https://huggingface.co/TenStrip/LTX2.3-10Eros_Workflows/blob/main/10Eros_10SNodes_I2V_Basic_DMD_V5.json

  • pplx-embed-v1-0.6b-onnx-int8-standard↗

    tss-deposium

    Re-exports of perplexity-ai/pplx-embed-v1-0.6B (revision `2c4d510d`) with **standard ONNX operators**, usable by Transformers.js / ONNX Runtime Web (WASM and WebGPU) and ONNX Runtime Node. The official ONNX files use `GatherBlockQuantized`, which ORT Web cannot load. Pooling: masked mean over `last_hidden_state` (identical to the official `pooler_output`), then L2, then Matryoshka truncation to 256 dims and L2 again, as in the Deposium stack. Measurements: 38 texts in 6 languages (10 to 218 tokens) with ONNX Runtime 1.25 CPU, plus 8 texts (up to 512 tokens) in Chrome 152 with Transformers.js 4.3.0 / ORT Web 1.31-dev on an RTX 5060 (WebGPU) and on WASM. Full report, scripts and raw results: deposium-turbov3 `docs/2026-09-17-browser-embedding-t1-weight-only-reexport.md`.

  • Qwen3.8-27B-GGUF↗

    qtum

    A 27B dense vision-language model (27.8B including the vision tower). 64 decoder layers in a hybrid stack: `full_attention_interval=4` means every fourth layer is grouped-query full attention (16 layers) and the rest are Gated DeltaNet linear attention (48 layers). Vocab 248320 with untied embeddings. Context length 262144 natively, extensible further with YaRN. The mmproj file is separate by design: llama.cpp loads the text backbone and the vision projector as two files. Download one tier plus one mmproj. Without the mmproj the model still runs, but text-only. The `mmproj-` and `mtp-` prefixes are what recent llama.cpp builds match on to tell the three file kinds apart, so `-hf qtum/Qwen3.8-27B-GGUF` picks a backbone rather than a projector, and `--spec-type mtp` finds the draft on its own.

  • NVIDIA-Nemotron-Labs-Dory1.0-4B-GGUF↗

    NVIDIA

    **NVIDIA-Nemotron-Labs-Dory1.0-4B-GGUF** is a looped (latent-recurrent) large language model (LLM) trained by NVIDIA. The model employs a normalized-GPT architecture with interleaved hybrid sliding window attention (SWA) and full attention layers. By repeatedly applying a shared block of layers, Dory1.0-4B can spend more compute per token allowing it to scale intelligence. This model is ready for commercial or non-commercial use. Dory (Looped LM) Architecture: The layer stack is divided into three groups: Input (I), Recurrent (R) and Output (O). The shared Recurrent block (R) is applied multiple times between the Input and Output blocks, and the input is reinjected into every loop. In Dory, the Input and Output blocks have 8 layers each, while the Recurrent block has 20 layers. Accuracy numbers measured by NVIDIA under a consistent harness (NeMo Gym / Nemo Evaluator SDK). Terminal-Bench 2.1 uses the Terminus-2 harness for all models. † Reported scores.