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AI モデル 2119

• POCKET-Darwin-180B-GGUF↗

  FINAL-Bench

  > ### ⭐ Latest and flagship models > [](https://huggingface.co/FINAL-Bench/VKORE-46M-NPU) [](https://huggingface.co/FINAL-Bench/Darwin-27B-ZTC-v5) > [](https://huggingface.co/collections/FINAL-Bench/pocket-models-6a618ee5d23eafb7e185a5c6) [](https://huggingface.co/collections/FINAL-Bench/darwin-family-699987b1f652864af0122193) [](https://huggingface.co/FINAL-Bench) [](https://vidraft.net) [](https://thevc.kr/vidraft) **A 180B frontier model from a laptop to DGX Spark.** A 4-bit GGUF build of Darwin-180B-RSI-R3 (111.3 GB, 4 files) for llama.cpp. It runs on a **gaming laptop with an 8 GB GPU and 32 GB RAM**, a **128 GB mini PC**, a CPU-only server, a single datacenter GPU, or **NVIDIA DGX Spark**. - **Quality kept:** MMLU-Pro (2,000 questions, paired) **87.65% vs 87.65%** for the BF16 original, difference **+0.00 points, 95% CI [−0.95, +1.00]**. - **Runs on a laptop:** **4.17 tokens/s** on an RTX 5060 Laptop GPU (8 GB) with 32 GB RAM. - **Runs without a GPU:** **18–21 tokens/s** on one server CPU socket, **78.8 GB peak RAM**. - **Runs on a mini PC:** any mini PC with **128 GB RAM** holds the whole model in memory, no GPU needed. - **Built-in confidence:** ZTC AUC **0.758** reading its own state; the 20% of questions it is most sure about are **97.5%** correct. Probe file and script included in `ztc/`. - **Shorter answers:** on the same 2,000 questions, answers average **3,694 tokens vs 4,322** for the same-format parent build (−14.5%).

• Swift-1.5-Qwen3.8-27b-MXFP4-GGUF↗

  tsaipifong

  MXFP4 GGUF quantizations of ukisai/Swift-1.5-Qwen3.8-27b, with the **MTP (multi-token prediction) layer kept** for self-speculative decoding. Tuned for WHIRL 0.2.0, now on AMD Ryzen AI Max+ 395 (Radeon 8060S) and Radeon AI PRO R9700. **Rebuilt 2026-10-19:** the three language-model files were re-quantized with an **imatrix-weighted MXFP4 quantizer** (same tensor layout, same file names). Against the BF16 model their KL divergence is 17–19% lower than the first release (2026-10-01); see Quality. The vision projector is unchanged. > **Unofficial community quantization.** This repository is not affiliated with, endorsed by, or supported by UkisAI or Alibaba Cloud. The names "Swift" and "UkisAI" are trademarks of UkisAI and are used here only to describe the origin of the weights. For the official model, official quantizations, evaluations and enterprise licensing, see the upstream repository.

• musicgen-melody↗

  facebook

  Audiocraft provides the code and models for MusicGen, a simple and controllable model for music generation. MusicGen is a single stage auto-regressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. Unlike existing methods like MusicLM, MusicGen doesn't not require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. By introducing a small delay between the codebooks, we show we can predict them in parallel, thus having only 50 auto-regressive steps per second of audio. MusicGen was published in Simple and Controllable Music Generation by *Jade Copet, Felix Kreuk, Itai Gat, Tal Remez, David Kant, Gabriel Synnaeve, Yossi Adi, Alexandre Défossez*. **Model type:** MusicGen consists of an EnCodec model for audio tokenization, an auto-regressive language model based on the transformer architecture for music modeling. The model comes in different sizes: 300M, 1.5B and 3.3B parameters ; and two variants: a model trained for text-to-music generation task and a model trained for melody-guided music generation.

• SAM-Audio-GGUF↗

  audio-cpp

  GGUF weights for native inference with audio.cpp. SAM Audio separates a sound described by text, a masked image or video, or positive and negative time spans from an input recording. These packages use the original Meta checkpoints, not the separate `-tv` variants. The official SAM Audio implementation is pinned to bb4c699.

• gemma-3-1b-pt↗

  unsloth

  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. These models were trained on a dataset of text data that includes a wide variety of sources. The 27B model was trained with 14 trillion tokens, the 12B model was trained with 12 trillion tokens, 4B model was trained with 4 trillion tokens and 1B with 2 trillion tokens. Here are the key components: The combination of these diverse data sources is crucial for training a powerful multimodal model that can handle a wide variety of different tasks and data formats.

• Qwen3.8-27B-DFlash2↗

  z-lab

  This repository contains the DFlash 2 draft model for `Qwen/Qwen3.8-27B`. It is not a standalone language model: it runs inside a speculative decoding server and drafts tokens for the target model to verify. This repository is a mirror of `incoai/Qwen3.8-27B-DFlash2`. 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. See the blog post for other engines and more details.

• sam2.1-hiera-tiny↗

  facebook

  Repository for SAM 2: Segment Anything in Images and Videos, a foundation model towards solving promptable visual segmentation in images and videos from FAIR. See the SAM 2 paper for more information. The official code is publicly release in this repo. Refer to the demo notebooks for details.

• wav2vec2-xlsr-53-espeak-cv-ft↗

  facebook

  This checkpoint leverages the pretrained checkpoint wav2vec2-large-xlsr-53 and is fine-tuned on CommonVoice to recognize phonetic labels in multiple languages. When using the model make sure that your speech input is sampled at 16kHz. Note that the model outputs a string of phonetic labels. A dictionary mapping phonetic labels to words has to be used to map the phonetic output labels to output words. **Abstract** Recent progress in self-training, self-supervised pretraining and unsupervised learning enabled well performing speech recognition systems without any labeled data. However, in many cases there is labeled data available for related languages which is not utilized by these methods. This paper extends previous work on zero-shot cross-lingual transfer learning by fine-tuning a multilingually pretrained wav2vec 2.0 model to transcribe unseen languages. This is done by mapping phonemes of the training languages to the target language using articulatory features. Experiments show that this simple method significantly outperforms prior work which introduced task-specific architectures and used only part of a monolingually pretrained model.

• Qwen3-ASR-1.7B-gguf↗

  handy-computer

  GGUF conversions of Qwen/Qwen3-ASR-1.7B for use with transcribe.cpp. Ported from upstream commit 7278e1e, pinned 2026-04-19. Validated against the qwen_asr 0.0.6 reference at transcribe.cpp commit 3f61df7 on 2026-04-20. Offline multilingual speech-to-text. Same audio-LLM architecture as the 0.6B variant (bidirectional audio encoder feeding a Qwen3 causal LM with audio-token injection), wider: encoder `d_model=1024` (16 heads), LM `hidden_size=2048`, `intermediate_size=6144`. Auto-detects the audio's language across 30 languages and emits the transcript in that language. Takes a 16 kHz mono WAV and produces a transcript.

• Qwen-Image-2.1-DiT-GGUF↗

  pottokao

  > **Not affiliated with, or endorsed by, Alibaba / Qwen.** Quantized redistribution of the > `Qwen/Qwen-Image-2.1` diffusion transformer under the > **Qwen Research License** (copy included as `LICENSE`). **Non-commercial use only.** > [!IMPORTANT] > **Loads in stock city96/ComfyUI-GGUF — no patch, no extra nodes.** > These files carry `general.architecture = qwen_image`, so `UnetLoaderGGUF` recognises them directly > (GGUFs without that field fail with `Unknown model architecture!`). > > **Text encoder:** our Qwen-Image-2.1 Text Encoder GGUF > works with this DiT, but it needs one extra node first — > ComfyUI-GGUF-Qwen3VL-TE (stock ComfyUI-GGUF > doesn't load Qwen3-VL text encoders yet). With a **safetensors** text encoder (bf16 / int8 / FP8 from the same > repo family) nothing extra is needed. The sample / showcase images in this repo were generated with the technique behind **ComfyUI-QwenImage-PhotoStyles** — a small ComfyUI node (17 photographic styles; one short prompt becomes a full styled prompt via the PE-T2I rewriter). If you're interested in how the images are made, the node and its recipe are there.