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AI モデル 2119

• sam2-hiera-large↗

  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. SAM2 can be used for automatic mask generation to segment all objects in an image using the `mask-generation` pipeline: SAM2's key strength is its ability to track objects across video frames. Here's how to use it for video segmentation:

• Python-Code-13B-GGUF↗

  TheBloke

  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. The creator of the source model has listed its license as `cc-by-nc-nd-4.0`, and this quantization has therefore used that same license. As this model is based on Llama 2, it is also subject to the Meta Llama 2 license terms, and the license files for that are additionally included. It should therefore be considered as being claimed to be licensed under both licenses. I contacted Hugging Face for clarification on dual licensing but they do not yet have an official position. Should this change, or should Meta provide any feedback on this situation, I will update this section accordingly.

• eu-pii-anonimization-multilang↗

  bardsai

  > **A newer, better version is available: bardsai/eu-pii-anonimization-multilang-v2.** > On real web pages, scrapes, PDFs and invoices its strict F1 is 0.973 vs 0.885 for this model, and it is better on every public benchmark we tested (Gretel 0.692 vs 0.615, Nemotron-PII 0.933 vs 0.882, OpenPII 0.997 vs 0.928). Same label set; ONNX and INT8 weights included. New projects should use v2. A multilingual PII detector built for EU compliance reality: 24 official languages, GDPR special categories, AI Act high-risk data - in one model. GDPR and the AI Act require you to detect and redact personal data across every language your users write in. Most open-source PII models were trained on English with a few translations bolted on, and they cover the basics — names, emails, phone numbers — while missing exactly the categories regulators care about: biometric data, genetic data, health information, political opinions, ethnic origin.

• llama-2-tiny-testing↗

  BlackSamorez

• lfm2-electronics-vlm-v2↗

  erikku-sama

• Ternary-Bonsai-2-27B-Heretic-Satellite↗

  HybridRMD

  > ### Teacher model > > This model is a **LoRA decision-distillation of > `convaiinnovations/laya`** > by **Convai Innovations**. laya supplies every training label — the > `should_compact` / `preserve` / `level` / `mode` decisions this model emits. > No laya weights are redistributed here; the teacher is credited as the source > of the supervision, which it is. > > If you want the same decisions without this model, go and use laya. A **Qwen3.8-27B Bonsai 2 (Heretic)** derivative carrying a small LoRA that turns a long working context into one structured compaction decision. **Text only.** No vision tower, no projector. If you feed it images it will not do anything useful with them.

• Phi-3-medium-4k-instruct↗

  Microsoft

  🎉 **Phi-3.5**: [[mini-instruct]](https://huggingface.co/microsoft/Phi-3.5-mini-instruct); [[MoE-instruct]](https://huggingface.co/microsoft/Phi-3.5-MoE-instruct) ; [[vision-instruct]](https://huggingface.co/microsoft/Phi-3.5-vision-instruct) The Phi-3-Medium-4K-Instruct is a 14B parameters, lightweight, state-of-the-art open model trained with the Phi-3 datasets that includes both synthetic data and the filtered publicly available websites data with a focus on high-quality and reasoning dense properties. The model belongs to the Phi-3 family with the Medium version in two variants 4K and 128K which is the context length (in tokens) that it can support. The model has undergone a post-training process that incorporates both supervised fine-tuning and direct preference optimization for the instruction following and safety measures. When assessed against benchmarks testing common sense, language understanding, math, code, long context and logical reasoning, Phi-3-Medium-4K-Instruct showcased a robust and state-of-the-art performance among models of the same-size and next-size-up.

• flux-3-image-klein-base-FP8↗

  unsloth

  This is an FP8 / INT8 quantized version of FLUX 3 Image Klein Base (the undistilled checkpoint, 50 steps with CFG 3.5). - The INT8 file is Black Forest Labs' own `flux3_image_klein_int8_convrot.safetensors` from black-forest-labs/flux-3-image-klein-comfy, unchanged (INT8 weights with ConvRot rotation). The FP8 file is ours. - Optimized for efficient inference with reduced memory footprint: the transformer is 5.81 GB (INT8) or 6.42 GB (FP8) instead of 11.1 GB. Same-seed LPIPS vs the bf16 model (lower is better): 0.022 INT8, 0.036 FP8. - Loads in Unsloth Desktop. The text encoder (Qwen3-VL-4B-Instruct) and the VAE are unchanged and identical to the distilled release; take them from black-forest-labs/flux-3-image-klein-base or black-forest-labs/flux-3-image-klein. The text encoder rows are scored against the bf16 text encoder with the bf16 transformer, so they measure only the encoder. The VAE stays bf16. This repo also carries the upstream diffusers folders from black-forest-labs/flux-3-image-klein-base, unchanged (`modular_model_index.json`, `text_encoder/`, `transformer/`, `vae/`), so Unsloth Desktop downloads every component from here.

• musicgen-large↗

  facebook

  MusicGen is a text-to-music model capable of genreating high-quality music samples conditioned on text descriptions or audio prompts. It 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 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*. Four checkpoints are released: - small - medium - **large** (this checkpoint) - melody

• sam2-hiera-base-plus↗

  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.