저장한 도구도구 추천
FOXAI SEARCH

전체 검색

AI 모델 2119

  • gemma-3-4b-it-GGUF↗

    ggml-org

    * [Gemma 3 Technical Report][g3-tech-report] * [Responsible Generative AI Toolkit][rai-toolkit] * [Gemma on Kaggle][kaggle-gemma] * [Gemma on Vertex Model Garden][vertex-mg-gemma3] Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. Gemma 3 models are multimodal, handling text and image input and generating text output, with open weights for both pre-trained variants and instruction-tuned variants. Gemma 3 has a large, 128K context window, multilingual support in over 140 languages, and is available in more sizes than previous versions. Gemma 3 models are well-suited for a variety of text generation and image understanding tasks, including question answering, summarization, and reasoning. Their relatively small size makes it possible to deploy them in environments with limited resources such as laptops, desktops or your own cloud infrastructure, democratizing access to state of the art AI models and helping foster innovation for everyone. - **Input:** - Text string, such as a question, a prompt, or a document to be summarized - Images, normalized to 896 x 896 resolution and encoded to 256 tokens each - Total input context of 128K tokens for the 4B, 12B, and 27B sizes, and 32K tokens for the 1B size

  • gemma-4-E4B-it-litert-lm↗

    litert-community

    Main Model Card: google/gemma-4-E4B-it This model card provides the Gemma 4 E4B model in a way that is ready for deployment on Android, iOS, Desktop, IoT and Web. Gemma is a family of lightweight, state-of-the-art open models from Google, built from the same research and technology used to create the Gemini models. This particular Gemma 4 model is small so it is ideal for on-device use cases. By running this model on device, users can have private access to Generative AI technology without even requiring an internet connection.

  • odia-distil-gpt2↗

    sam2ai

  • Qwen3.8-27B-GGUF-shards↗

    Arki05

    - `source/`: Unsloth's BF16 GGUF (unsloth/Qwen3.8-27B-GGUF), unchanged. - `types/ -hq.gguf`: every linear block tensor at llama.cpp type `T` (IQ1_M … Q8_0, IQ4_NL, MXFP4, NVFP4), rebuilt from the BF16 weights Hessian-quantized. Same type, format and size as llama.cpp's: plain GGUF types, for stock llama.cpp (a GGUF assembled from them holds the plain type `T`); modified versions of the original weights. - `types/ .gguf` (Q2_K … Q8_0): token_embd, output, the multi-token-prediction layer and the linear attention's small `ssm_alpha` / `ssm_beta` tensors at llama.cpp's own quantization. - `manifest.json` (`qcore-store/1`), `skeleton.gguf`, `store.json`, `records/`: what qalloc reads, with every tensor's offset and sha256. The earlier version of this store (every tensor at ~41 types by llama.cpp's and ik_llama.cpp's quantizers) is at revision `0090490`.

  • MiniCPM-V-4.6-Thinking↗

    openbmb

    A Pocket-Sized MLLM for Ultra-Efficient Image and Video Understanding on Your Phone * [2026.05.17] ⭐️⭐️⭐️ We release the API service of MiniCPM-V 4.6, with a **public free API key** together! Try it now. **MiniCPM-V 4.6 Thinking** is the long chain-of-thought reasoning variant of MiniCPM-V 4.6. It generates an explicit reasoning trace before producing the final answer, substantially boosting performance on complex multimodal reasoning, math, and OCR-heavy tasks, while keeping the same edge-friendly architecture (SigLIP2-400M vision encoder + Qwen3.5-0.8B LLM) and the mixed 4x/16x visual token compression of MiniCPM-V 4.6.

  • Qwen3.8-27B-MLX-4bit↗

    TensorFold

    A native Apple-silicon conversion of Qwen/Qwen3.8-27B , quantized with stock 4-bit affine weights for MLX-VLM and oMLX. This repository contains a stock 4-bit affine MLX conversion of Qwen3.8-27B. The upstream model is a dense, native vision-language model with flexible thinking control and support for text, images, and video. Its tokenizer, processor configuration, chat template, and generation configuration are preserved. The decode figure is the median of three greedy 256-token runs after a 256-token Metal-kernel warm-up. Individual runs measured 39.90, 39.89, and 39.91 tokens/s. This is a practical local reference, not a controlled cross-platform benchmark; prompt length, context growth, sampler settings, memory pressure, thermal state, and runtime versions can materially change performance.

  • Qwen3.6-35B-A3B-NVFP4-MTP-GGUF↗

    michaelw9999

    This repo contains two experimental NVFP4 GGUF quantizations of **Qwen3.6-35B-A3B** for `llama.cpp`. This was quantized using my experimental advanced-gguf-quantizer tool. Both models were imatrix calibrated for the first time using a new custom dataset that I am evaluating. All PPL/KLD results were measured against the same BF16 wikitest KLD base, and then compared to the official NVFP4 release by NVIDIA. Further evaluation tests are underway to identify real world performance differences between **TURBO** and **HQ**.

  • QwenCleo-ASR-GGUF↗

    mohammedaly22

    **Egyptian Arabic & code-switching speech recognition for audio.cpp: 3–4× faster than PyTorch on the same GPU, no Python** These are standalone GGUF packages of QwenCleo-ASR, a Qwen3-ASR-1.7B fine-tune for Egyptian Arabic and Arabic↔English code-switching (`فبتديك كل ال soft skills بشكل indirect`). On the author's 3,699-utterance Egyptian test set it reaches **19.85% WER, against 41.51% for base Qwen3-ASR-1.7B**. Full results are on the original model card. In audio.cpp it runs through the existing **`qwen3_asr`** family, the same C++ runtime and options as Qwen3-ASR: offline and streaming modes, long-audio chunking, word timestamps with Qwen3 Forced Aligner, and the OpenAI-compatible server.

  • Ornith-1.5-35B-A3B-Huihui-Sangreal-MTP-ICE-GGUF↗

    gbuzhf

    GGUF quantizations of huihui-ai's abliterated Ornith-1.5-35B-A3B, made with a Sangreal 36,025-chunk importance matrix, ICE 1.6 per-tensor recipes and ICE 2.0 post-quantization expert repair. Every file keeps the MTP draft layer. The ICE files carry peculiar-ragdoll's v22.5.0 Qwen-Sharp chat template with gbuzhf's arrow4.0 harness operator wrapper. > ⚠️ **Uncensored.** This is an abliterated checkpoint — refusal behaviour has been > suppressed in the weights. Sandbox it at the OS level and control its network and > code-execution access; with no refusal backstop, a prompt injection from a hostile > page or third-party code has nothing to stop it. Score: F16 = 100; 70 % full-distribution fidelity `1/(1+KLD)` + 30 % same-top-1, averaged over the three sets.

  • wide_resnet50_2.racm_in1k↗

    timm

    This model features: * ReLU activations * single layer 7x7 convolution with pooling * 1x1 convolution shortcut downsample Recipe details: * RandAugment `RACM` recipe. Inspired by and evolved from EfficientNet RandAugment recipes. Published as `B` recipe in ResNet Strikes Back. * RMSProp (TF 1.0 behaviour) optimizer, EMA weight averaging * Step (exponential decay w/ staircase) LR schedule with warmup - **Model Type:** Image classification / feature backbone - **Model Stats:** - Params (M): 68.9 - GMACs: 11.4 - Activations (M): 14.4 - Image size: train = 224 x 224, test = 288 x 288 - **Papers:** - ResNet strikes back: An improved training procedure in timm: https://arxiv.org/abs/2110.00476 - Wide Residual Networks: https://arxiv.org/abs/1605.07146 - Deep Residual Learning for Image Recognition: https://arxiv.org/abs/1512.03385 - **Original:** https://github.com/huggingface/pytorch-image-models