dinov3-vitl16-pretrain-lvd1689m-GGUF
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查看详情 ↗DISCOVER & COMPARE
探索 AI 模型 18
GEM-X-GGUF
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查看详情 ↗Kimodo-G1-RP-v1-GGML
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查看详情 ↗Kimodo-G1-SEED-v1-GGML
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查看详情 ↗Kimodo-SOMA-RP-v1.1-GGML
LocalAI-ioNative F32 GGML/GGUF conversion of nvidia/Kimodo-SOMA-RP-v1.1. The model predicts the compact SOMA 30-joint control skeleton. Its reusable Llama-derived text encoder is distributed separately as `Llama-3-Kimodo-GGML`. The model is installed at `models/kimodo-soma-rp-v1.1-f32.gguf` by `scripts/download_gguf_weights.sh --output "$PWD" --model soma-rp-v1.1`. Converted from upstream revision `6c9233af1180b8151e3c4703477104af5dce9dd5`. `MANIFEST.json` records the source revision and SHA-256 of the GGUF. The model remains subject to the NVIDIA Open Model License. This conversion grants no additional rights.
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查看详情 ↗Kimodo-SOMA-SEED-v1.1-GGML
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查看详情 ↗Llama-3-Kimodo-GGML
LocalAI-ioNative GGML/GGUF text encoder used by Kimodo. This is the reusable LLM2Vec encoder only; download a matching Kimodo diffusion model separately, for example `Kimodo-SMPLX-RP-v1-GGML`. From a kimodo.cpp checkout with the Hugging Face CLI installed, install both with: The downloader installs the recommended Q8_0 variant by default. Select a different one with `--text-quantization bf16|q8_0|q6_k|q5_k|q4_k|q4_k_m`. Every variant is one monolithic weight GGUF and uses the same `tokenizer.gguf`. Kimodo reads individual tensor ranges from the monolith, so streaming one layer at a time does not require separate per-layer files.
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查看详情 ↗This model was converted to GGUF format from `mudler/LocalAI-functioncall-qwen2.5-7b-v0.5` using llama.cpp via the ggml.ai's GGUF-my-repo space. Refer to the original model card for more details on the model. Note: You can also use this checkpoint directly through the usage steps listed in the Llama.cpp repo as well. Step 2: Move into the llama.cpp folder and build it with `LLAMA_CURL=1` flag along with other hardware-specific flags (for ex: LLAMA_CUDA=1 for Nvidia GPUs on Linux).
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查看详情 ↗LocalVQE
LocalAI-io**Local Voice Quality Enhancement** — compact neural models for acoustic echo cancellation (AEC), noise suppression (NS), and dereverberation of 16 kHz speech, running on commodity CPUs in real time. Causal and streaming (256-sample hop, 16 ms latency). This page hosts the published weights. Inference runs the GGML C++ engine on the GGUF files directly (build instructions on GitHub). **Authors:** Richard Palethorpe (richiejp) and Claude (Anthropic). LocalVQE is a streaming, CPU-tuned derivative of **DeepVQE** (Indenbom et al., Interspeech 2023).
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输入 音频→ 输出 音频
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查看详情 ↗MotionBricks-G1-GGML
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查看详情 ↗privacy-filter-GGUF
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查看详情 ↗privacy-filter-multilingual-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-multilingual`, a multilingual PII **token-classification** model (a fine-tune of `openai/privacy-filter`). It labels every token with a BIOES tag over **54 PII categories (217 classes)** across **16 languages**, so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it. This GGUF uses a **custom architecture, `openai-privacy-filter`**, that is not (yet) part of upstream llama.cpp. It runs on:
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查看详情 ↗privacy-filter-nemotron-GGUF
LocalAI-ioGGUF conversion of `OpenMed/privacy-filter-nemotron`, a **fine-grained** PII **token-classification** model — a fine-tune of `openai/privacy-filter` on the `nvidia/Nemotron-PII` dataset. It labels every token with a BIOES tag over **55 PII categories (221 classes)** in a single forward pass, then decodes coherent spans with a constrained Viterbi procedure — so it can be served locally with **no Python** as the encoder/NER tier of a PII redactor. Where the base `openai/privacy-filter` covers 8 coarse categories, this fine-tune trades multilingual breadth for **category depth**: 55 fine-grained English categories (first/last name, government IDs, financial, healthcare, vehicle, digital, …). For the full model description, label space, evaluation, limitations, and citations, see the **source model card** — this card only covers the GGUF packaging and how to run it.
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查看详情 ↗Qwen-Image-2.1-Anime-Fusal-GGUF
LocalAI-io**Built with Qwen.** This repository packages a tested LocalAI and stable-diffusion.cpp runtime for MicksHF/Qwen-Image-2.1-Anime-Fusal. It is a multi-file runtime assembly, not a merged checkpoint. The source model includes NSFW training material and is not for all audiences. This repository grants no rights to third-party artwork, characters, trademarks, datasets, or generated outputs. Users are responsible for lawful and responsible use. The text encoder does not include vision weights. This package supports text-to-image. It does not support reference-image editing without a compatible Qwen3-VL vision projector or combined encoder.
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查看详情 ↗sam-3d-body-dinov3-GGUF
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查看详情 ↗SkinTokens-GGUF
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查看详情 ↗TRELLIS.2-4B-GGUF
LocalAI-ioGGUF (`f16`) conversions of microsoft/TRELLIS.2-4B for the **trellis2cpp** / ggml runtime — a CUDA-free, PyTorch-free C++ port of the TRELLIS.2 image-to-3D pipeline. Load these directly; no safetensors conversion or Python is needed at inference. The image conditioning encoder (DINOv3) and the sparse-structure decoder are published separately: dinov3-vitl16-pretrain-lvd1689m-GGUF and TRELLIS-image-large-GGUF. Converted from the upstream `bf16`/`fp16` safetensors to GGUF `f16` (ggml ftype 1) with the `convert_*_to_gguf.py` scripts in the trellis2cpp project. No weights were retrained or modified — this is a format conversion only.
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查看详情 ↗TRELLIS-image-large-GGUF
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
开放权重，就代表可以免费商用吗？+
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
排行榜第一，就是最适合我的模型吗？+
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。