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AI モデル 2119
- parakeet-tdt-0.6b-v3-coreml↗
FluidInference
On‑device multilingual ASR model converted to Core ML for Apple platforms. This model powers FluidAudio’s batch ASR and is the same model used in our backend. It supports 25 European languages and is optimized for low‑latency, private, offline transcription. The conversion script is here: https://github.com/FluidInference/mobius/tree/main/models/stt/parakeet-tdt-v3-0.6b/coreml And the benchmarks are here: https://github.com/FluidInference/FluidAudio/blob/main/Documentation/Benchmarks.md#transcription
- Qwen3.8-27B-Intel-Arc-Tuned-GGUF↗
Greatjedi
**Hardware-Accelerated Weights for Intel Arc GPUs (Battlemage Xe2 / Alchemist Xe / oneAPI SYCL)** *Engineered by Greatjedi & Gemini (DeepMind Antigravity Team)* If you own an **Intel Arc GPU** (such as the **Intel Arc Pro B60 24GB**, Arc A770 16GB, or Battlemage graphics cards) and have attempted to run popular 27B quantization variants (such as `IQ2_S`, `IQ3_XXS`, or Unsloth's "Dynamic" `UD-Q4_K_S`), you likely noticed your generation speed crawling at an agonizing **8.1 to 12.2 tokens/second**, despite the GPU having more than enough raw compute and 512 GB/s memory bandwidth. 1. **Intel Arc Matrix Engines (XMX)** utilize **DPAS** (Dot Product Accumulate Systolic) hardware. DPAS requires dense, linearly indexed integers (`INT2`, `INT4`, `INT8`) to feed its systolic pipeline at thousands of operations per clock cycle. 2. **I-Quants (`IQ2_S`, `IQ3_XXS`, `IQ4_XS`, `IQ3_S`)** use non-linear 8-dimensional vector lattice codebooks ($E_8$). They require runtime table lookups per weight element. 3. In `llama.cpp`'s SYCL backend, **there are zero kernel implementations for I-Quants**. Whenever an IQ tensor is evaluated, SYCL drops out of hardware systolic GEMM into slow CPU/host dequantization loops. 4. **The Hidden Catch in Unsloth "UD" Models**: When we reverse-engineered Unsloth's popular `Qwen3.8-27B-UD-Q4_K_S.gguf`, we discovered it secretly contains **201 I-Quant tensors** (`IQ4_XS`, `IQ3_S`, `IQ3_XXS`), causing it to suffer from the exact same dequantization stall on Intel Arc!
- AWPainting-v1-2↗
sam749
You can use this with the 🧨Diffusers library
- MOSS-Image-SFT↗
drawthingsai
MOSS-Image-SFT is a supervised fine-tuned text-to-image model in the **MOSS-Image** family from OpenMOSS, derived from MOSS-Image-Base. It uses the same architecture and inference pipeline, supporting natural-language prompts, tag sequences, and JSON layouts for controlling text placement and object arrangement. Use Python 3.10 or later and an NVIDIA GPU with CUDA support. Install a PyTorch 2.9 or later build compatible with your CUDA environment, then install: The model and custom pipeline code are downloaded and cached automatically. You can also pass a complete local model directory to `from_pretrained`. The transformer and text encoder use bfloat16; the VAE and initial noise use float32. The bundled VAE uses the FLUX.2 small decoder.
- GLM-5.3-Flash-DFlash2-GGUF↗
Anbeeld
This repository contains the DFlash 2 draft model for `zai-org/GLM-5.3-Flash`. 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. We compare autoregressive decoding, GLM-5.3-Flash's native MTP, and DFlash 2. All speculative methods propose seven draft tokens per verification step.
- mobilevit-small↗
apple
MobileViT model pre-trained on ImageNet-1k at resolution 256x256. It was introduced in MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer by Sachin Mehta and Mohammad Rastegari, and first released in this repository. The license used is Apple sample code license. Disclaimer: The team releasing MobileViT did not write a model card for this model so this model card has been written by the Hugging Face team. MobileViT is a light-weight, low latency convolutional neural network that combines MobileNetV2-style layers with a new block that replaces local processing in convolutions with global processing using transformers. As with ViT (Vision Transformer), the image data is converted into flattened patches before it is processed by the transformer layers. Afterwards, the patches are "unflattened" back into feature maps. This allows the MobileViT-block to be placed anywhere inside a CNN. MobileViT does not require any positional embeddings.
- sam2-hiera-large-hf↗
facebook
- MiniMax-M3-EAGLE3-GQA↗
Inferact
**Inferact/MiniMax-M3-EAGLE3-GQA** is a **grouped-query-attention (GQA)** EAGLE3 draft model for accelerating inference of MiniMax-M3, served with **vLLM** and trained with **TorchSpec**. It is **retrained on the same datasets** as the multi-head-attention version Inferact/MiniMax-M3-EAGLE3 — **kimi-mtp, OpenCodeInstruct, SWE-bench, and SWE-bench-Pro** — with the draft's attention changed from **MHA to GQA** (`num_key_value_heads: 64 → 4`) for **inference efficiency** (16× smaller draft KV cache) and **compatibility with the target model**. The draft is a 1-layer dense Llama (`LlamaForCausalLMEagle3`) on MiniMax-M3's `hidden_size=6144` / `vocab_size=200064`; at serve time it shares the target's embedding and LM head (EAGLE3). See `config.json` for the full architecture.
- Luna-AI-Llama2-Uncensored-GGUF↗
TheBloke
Many thanks to William Beauchamp from Chai for providing the hardware used to make and upload these files! GGUF is a new format introduced by the llama.cpp team on August 21st 2023. It is a replacement for GGML, which is no longer supported by llama.cpp. GGUF offers numerous advantages over GGML, such as better tokenisation, and support for special tokens. It is also supports metadata, and is designed to be extensible. The creator of the source model has listed its license as `cc-by-sa-4.0`, and this quantization has therefore used that same license.
- sam-audio-small-tv↗
facebook