Saved toolsSubmit a tool
FOXAI SEARCH

Search FoxAI

AI Models 2119

  • GLM-5.3-DFlash2↗

    incoai

    This repository contains the DFlash 2 draft model for `zai-org/GLM-5.3`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. DFlash 2 is a block-diffusion drafter for speculative decoding. It predicts a whole block of tokens in a single pass and keeps the top candidates at every position. A lightweight selector then traces one coherent path through them. Two-tap dynamic convolutions in the backbone keep the draft from decaying toward the end of the block. Decoding is lossless: greedy output matches the target model exactly, and sampling preserves its distribution. DFlash 2 is also supported by vLLM v0.28.0 and later; see `incoai/GLM-5.3-NVFP4` for a vLLM serving example with the NVFP4-quantized target. See the blog post for more details.

  • sam-3d-body-dinov3-GGUF↗

    LocalAI-io

  • MiniCPM5-2B-GGUF↗

    openbmb

    MiniCPM Tech Report | MiniCPM Wiki(Chinese) | GitHub Repo | UltraData | Online Demo We are releasing **MiniCPM5-2B**, the second model in the **MiniCPM5** series, following MiniCPM5-1B. It is a dense 2B Transformer that scales up the same training recipe, built for on-device, local deployment, and resource-constrained scenarios, reaching 2B-class open-source SOTA. 🏆 **2B-class open-source SOTA**: compared with strong open-source models of similar size, MiniCPM5-2B achieves SOTA performance within this comparison set. It remains competitive with 4B-class models overall, while showing its advantages over models of comparable size in coding, mathematics, long-context understanding, tool use, and agentic tasks.

  • Speaker-wavLM-tbr↗

    Orange

    This model produces embeddings that globally represent the timbral traits of a speaker's voice. These embeddings can be used the same way as for a classical speaker verification (ASV): in order to compare two voice signals, an embeddings vector must be computed for each of them. Then the cosine similarity between the two embeddings can be used for comparison. The main difference with classical ASV embeddings is that, here, only the timbral traits are compared. The following code snippet uses the file spk_embeddings.py to build the architecture of the model. Its weights are then downloaded from this repository. The python file also contains the function to compute the timbral embeddings of an audio file. In this tutorial version, the audio file is expected to be sampled at 16kHz. Depending on the available memory (cpu or gpu), you may change the value of the *max_size* parameter, which is used to truncate the long audio signals.

  • falcon-mamba-7b↗

    tiiuae

    Find below some example scripts on how to use the model in `transformers` (Make sure to have the latest transformers, or the one built from source): Falcon-Mamba has been trained with ~ 5,500 GT mainly coming from Refined-Web, a large volume web-only dataset filtered and deduplicated. Similar to the others Falcon suite models, Falcon-Mamba has been trained leveraging a multi-stage training strategy to increase the context-length from 2,048 to 8,192. Moreover, inspired by the concept of Curriculum Learning, we carefully selected data mixtures throughout the training stages, considering both data diversity and complexity. Note that at inference the context-length is not relevant as the Mamba architecture has no limit on long range dependency. At the last training stage, small portion of high-quality curated data was used to further enhance performance. Overall, the data sources included RefinedWeb-English, high quality technical data, code data and math data extracted from public sources. In particular, we used samples coming from Fineweb-edu during our last training stage.

  • 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

  • WAI-NSFW-illustrious-SDXL-v110-GGUF↗

    kekusprod

    Also you need illustrious_clip_g_fp8_e4m3fn.safetensors, illustrious_clip_l_fp8_e4m3fn.safetensors text encoders and illustrious_v110_vae_fp8_e4m3fn.safetensors VAE I am not sure about ability to reproduce same result in sdxl models, but seems like Q4_0 not that bad as i thought, absolutely usable

  • linkup-sparseup-embed-v1↗

    Linkup-Platform

    > **TL;DR:** SPARSEUP is a strong and efficient sparse retriever trained on the same backbone family and fine-tuning data as DenseOn / LateOn (149M, ModernBERT). Strongest public vocabulary-based sparse encoder we know of under 150M on BEIR-13 (56.4 nDCG@10). Apache 2.0. Use it! LightOn released open data and a recipe to train **DenseOn** (single-vector) and **LateOn** (late interaction). **SPARSEUP** fills the missing slot: a learned sparse embedding model, so the three architectures can be compared with backbone and data held fixed. The model is initialized from `LateOn-unsupervised` with ModernBERT’s MLM `head` / `decoder.bias` grafted on (`decoder.weight` stays tied to LateOn embeddings). Contrastive fine-tuning on LightOn’s mixture (7 hard negatives sampled from 50, plus in-batch). No cross-encoder distillation. Three extra knobs *vs* vanilla SPLADE-like model: 1. **`logit_shift=15`** — `log(1 + ReLU(x − 15))` so ReLU support starts sparse 2. **`position_top_k=12`** — each token keeps 12 vocab dims max before pooling (expansion budget) 3. **`vocab_fold=case_space`** — fold case / `Ġ` surface forms onto one id after pooling (avoid duplication of surface forms = improve efficiency). Voc goes from 50*k* to ~34*k*.

  • InternVL3-14B-AWQ↗

    OpenGVLab

    We introduce InternVL3, an advanced multimodal large language model (MLLM) series that demonstrates superior overall performance. Compared to InternVL 2.5, InternVL3 exhibits superior multimodal perception and reasoning capabilities, while further extending its multimodal capabilities to encompass tool usage, GUI agents, industrial image analysis, 3D vision perception, and more. Additionally, we compare InternVL3 with Qwen2.5 Chat models, whose corresponding pre-trained base models are employed as the initialization of the langauge component in InternVL3. Benefitting from Native Multimodal Pre-Training, the InternVL3 series achieves even better overall text performance than the Qwen2.5 series. As shown in the following figure, InternVL3 retains the same model architecture as InternVL 2.5 and its predecessors, InternVL 1.5 and 2.0, following the "ViT-MLP-LLM" paradigm. In this new version, we integrate a newly incrementally pre-trained InternViT with various pre-trained LLMs, including InternLM 3 and Qwen 2.5, using a randomly initialized MLP projector. As in the previous version, we applied a pixel unshuffle operation, reducing the number of visual tokens to one-quarter of the original. Besides, we adopted a similar dynamic resolution strategy as InternVL 1.5, dividing images into tiles of 448×448 pixels. The key difference, starting from InternVL 2.0, is that we additionally introduced support for multi-image and video data.

  • sam3_pt↗

    pankjkkkkkk