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AI 모델 2119
- yam-sam-7B-i1-GGUF↗
mradermacher
- gtr-t5-base↗
sentence-transformers
This is a sentence-transformers model: It maps sentences & paragraphs to a 768 dimensional dense vector space. The model was specifically trained for the task of semantic search. This model was converted from the Tensorflow model gtr-base-1 to PyTorch. When using this model, have a look at the publication: Large Dual Encoders Are Generalizable Retrievers. The tfhub model and this PyTorch model can produce slightly different embeddings, however, when run on the same benchmarks, they produce identical results. The model uses only the encoder from a T5-base model. The weights are stored in FP16.
- juggernaut-reborn↗
stablediffusionapi
Coding in PHP/Node/Java etc? Have a look at docs for more code examples: View docs Try model for free: Generate Images payload = json.dumps({ "key": "your_api_key", "model_id": "juggernaut-reborn", "prompt": "ultra realistic close up portrait ((beautiful pale cyberpunk female with heavy black eyeliner)), blue eyes, shaved side haircut, hyper detail, cinematic lighting, magic neon, dark red city, Canon EOS R3, nikon, f/1.4, ISO 200, 1/160s, 8K, RAW, unedited, symmetrical balance, in-frame, 8K", "negative_prompt": "painting, extra fingers, mutated hands, poorly drawn hands, poorly drawn face, deformed, ugly, blurry, bad anatomy, bad proportions, extra limbs, cloned face, skinny, glitchy, double torso, extra arms, extra hands, mangled fingers, missing lips, ugly face, distorted face, extra legs, anime", "width": "512", "height": "512", "samples": "1", "num_inference_steps": "30", "safety_checker": "no", "enhance_prompt": "yes", "seed": None, "guidance_scale": 7.5, "multi_lingual": "no", "panorama": "no", "self_attention": "no", "upscale": "no", "embeddings": "embeddings_model_id", "lora": "lora_model_id", "webhook": None, "track_id": None })
- slimsam-77-uniform↗
Xenova
https://huggingface.co/nielsr/slimsam-77-uniform with ONNX weights to be compatible with Transformers.js. If you haven't already, you can install the Transformers.js JavaScript library from NPM using: Next, select the channel with the highest IoU score, which in this case is the second (green) channel. Intersecting this with the original image gives us an isolated version of the subject:
- surya_layout2↗
datalab-to
A lightweight document **layout detection** model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. Released under the AI Pubs OpenRAIL-M license (see `LICENSE`) — the same license as the surya-ocr-2 model weights.
- pythia-70m↗
EleutherAI
The *Pythia Scaling Suite* is a collection of models developed to facilitate interpretability research (see paper). It contains two sets of eight models of sizes 70M, 160M, 410M, 1B, 1.4B, 2.8B, 6.9B, and 12B. For each size, there are two models: one trained on the Pile, and one trained on the Pile after the dataset has been globally deduplicated. All 8 model sizes are trained on the exact same data, in the exact same order. We also provide 154 intermediate checkpoints per model, hosted on Hugging Face as branches. The Pythia model suite was deliberately designed to promote scientific research on large language models, especially interpretability research. Despite not centering downstream performance as a design goal, we find the models match or exceed the performance of similar and same-sized models, such as those in the OPT and GPT-Neo suites. Previously, we released an early version of the Pythia suite to the public. However, we decided to retrain the model suite to address a few hyperparameter discrepancies. This model card lists the changes ; see appendix B in the Pythia paper for further discussion. We found no difference in benchmark performance between the two Pythia versions. The old models are still available, but we suggest the retrained suite if you are just starting to use Pythia. **This is the current release.**
- sam2.1-hiera-small-ONNX↗
onnx-community
- chandohasam↗
samvaran
- Qwen3.8-27B-Uncensored-NVFP4-NInferV3↗
wallawalla47
**A single-file NInfer V3 (`.ninfer`) artifact of orcarouter/Qwen3.8-27B-Uncensored-NVFP4, built for an NVIDIA RTX 5090 (sm_120a), for Infernix (recommended) and NInfer. No conversion step at load time: point `infernix-serve` (or `ninfer-serve`) at the `.ninfer` file and run.** This repository is the NInfer form of the orcarouter checkpoint above, an abliterated (refusal-removed) mixed NVFP4/FP8 build of Qwen3.8-27B. Its NVFP4 and FP8 weights are carried over **bit-exactly** for every transformer linear, and two speculative-decoding components (DFlash2 draft model, indexed proposal head) are added so the artifact runs with the engines' fastest decode paths. **Recommended engine: Infernix**, my engine grown from NInfer, which replaces my earlier ninfer-custom fork. In an agentic coding replay on the 27B it served 90 % of prompt tokens from its prefix cache and reached the first token in about half NInfer's average time, and it prefills long prompts 1.3-1.5× faster, at the same quality (benchmarks, measured on NInfer's official 27B artifact). It also runs Qwen3.8-Flash-Next. The artifact still works with NInfer itself and with ninfer-custom.
- SONAR_200_text_encoder↗
cointegrated
This is a port of the multilingual SONAR text encoder (https://huggingface.co/facebook/SONAR) to the `transformers` format from `fairseq2`. Its embeddings are expected be equal to those the official implementation (https://github.com/facebookresearch/SONAR), but the latter stays the source of truth. The encoder supports the same 202 languages as NLLB-200 (see also the source model card and FLORES-200 lang code mapping).