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> ### TLDR > **Mixed-K quants as a service** — assemble your own EXL3 checkpoint from > shared, attested, per-expert segments. Pure safetensors. No new format. **Fungible Quant Segments (for use in Progressive Tensors):** offline per-expert bit-width selection and EXL3 checkpoint assembly for the Gilded Gnosis vLLM stack. This repository does **not** implement live runtime per-expert bit-width reallocation. The core of this repo is the **shared K3 base tier** — the "everyone downloads this once" layer of the progressive-JPEG model for quants — covering every MoE layer (3–78). **Do not infer exact coverage from a layer range printed in this card.** Read the signed `index-kK.json` keys for the actual published layer set. When an `fq-layer-coverage/1` `per_k[K].layer_coverage.layers` field is present, it is also an exact set. Legacy `per_k[K].layers` and `layer_range` are extrema only; `segment_count` is an inventory count, not membership. Segments are content-addressed and per-layer, so every published window is durable value on its own.

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ppo-Huggy

Malgesw

基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。

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Game AI
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taatiknet

malper

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unikud

malper

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scincl

malteos

SciNCL is a pre-trained BERT language model to generate document-level embeddings of research papers. It uses the citation graph neighborhood to generate samples for contrastive learning. Prior to the contrastive training, the model is initialized with weights from scibert-scivocab-uncased. The underlying citation embeddings are trained on the S2ORC citation graph. Paper: Neighborhood Contrastive Learning for Scientific Document Representations with Citation Embeddings (EMNLP 2022 paper). These model weights are the ones that yielded the best results on SciDocs (`seed=4`). In the paper we report the SciDocs results as mean over ten seeds.

Embeddings & similarity
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An experimental image-specialized MiniMax H3 VAE that directly decodes a single temporal latent (`T=1`) into one image. It was made to provide a usable direct-one-latent image path, not as a claim of state-of-the-art or high-fidelity image reconstruction. It is distributed as a standard merged H3 VAE checkpoint: no custom node or separate decoder head is required. > [!IMPORTANT] > **This checkpoint is for images only. Do not use it as a replacement for the original H3 VAE in video workflows.** Its image-specialized decoder materially regresses multi-frame video reconstruction and can introduce patch-grid ghosting and cross-frame mixing. Keep the original MiniMax H3 VAE for video. > [!NOTE] > Reconstruction quality is limited. Outputs can remain soft and can lose fine text, thin contours, hair, foliage and microtexture; the example below is intended to show the actual result rather than imply a quality guarantee.

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