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AI 모델 2119

• qwen3-4b-mamba3-hybrid-stage2b-kd-bias0↗

  arianraje

  **Stage-2b endpoint, bias-0 arm** -- long-context forward-KL logit distillation against Qwen3-4B at sequence length 32,768, 294M tokens (141 steps of 2,097,152; micro-batch 1 x 4 ranks x 16 accumulation), from the stage-2a endpoint of this arm. LR halved from stage 2a: mixers 1e-4, inherited weights 1e-5, cosine to 0.1x with 3% warmup; all weights train. 4x B200. Final val KL at 32k **0.2021**, top1-agreement **0.8056**, wikitext-2 PPL (4096 context) **11.93**, from a starting point of 1.1727 / 0.7167 / 12.49. At this same stage the Qwen GDN arm reaches KL 0.2177 / top1 0.7985 and the Qwen Mamba2 arm 0.2336 / 0.7875; see the table below for the bias-1 arm. This is the input to stage-3 on-policy distillation, which has not been run. No downstream evaluation yet. This is a **custom container** (`model_type: qwen3_mamba3`), not a stock architecture, so `trust_remote_code=True` is required. The modeling code is bundled in this repo (`modeling_qwen3_mamba3.py`) and needs no other checkout:

• qwen3-forced-aligner-0.6b↗

  OpenASR

  **Word-level forced alignment for OpenASR transcripts -- a non-autoregressive Qwen3 audio+text model that refines per-word timestamps** A capability-pack support model for the **OpenASR** runtime — pure-Rust inference, **no Python at inference time**. Not a standalone transcription model: it augments another OpenASR ASR model's own decode path. Qwen3-ForcedAligner-0.6B is a **word-level forced-alignment** model from **Qwen**, sharing its audio-encoder + LM `thinker` tensor layout byte-for-byte with **Qwen3-ASR** (same `Qwen3ASRForConditionalGeneration` architecture). The only structural difference is the final head: instead of a tied vocabulary `lm_head`, it uses an independent `Linear(hidden_size, 5000)` classification head over 80ms-wide timestamp bins. Given a transcript's text and its source audio, it runs a single non-autoregressive forward pass and reads off word-boundary timestamps at argmax ` ` positions -- refining a model family's own (typically decode-time-approximate) per-word timestamps. This OpenASR repo repackages the weights as `.oasr` packs that run natively in the OpenASR runtime -- no Python at inference, all decoding local. OpenASR recommends the validated **q4_k** tier and also ships **q8_0** and an **fp16** full-precision reference tier. The q4_k label is the catalog's unified product name, not a claim that every matrix uses Q4_K: the audio encoder, token embedding, and timestamp head remain Q8_0, and pack verification replays the exact per-tensor policy. Q3 and legacy all-Q4 packs are rejected because small logit perturbations can move a word boundary across multiple 80ms bins.

• Kimi-VL-A3B-Thinking↗

  月之暗面 · Kimi

  > [!Warning] > This model has a new version: Kimi-VL-A3B-Thinking-2506. Please consider using the new 2506 version for better abilties on general visual understanding, reasoning, video and agent scenarios. *Please set a higher temperature for thinking model, especially when your problem requires relatively long thinking process. We have updated the following sample script for HF inference.* We present **Kimi-VL**, an efficient open-source Mixture-of-Experts (MoE) vision-language model (VLM) that offers **advanced multimodal reasoning, long-context understanding, and strong agent capabilities**—all while activating only **2.8B** parameters in its language decoder (Kimi-VL-A3B).

• LFM2.5-1.2B-JP-202606-ONNX↗

  LiquidAI

  ONNX export of **LFM2.5-1.2B-JP-202606** for cross-platform deployment via ONNX Runtime, Transformers.js, and the WebGPU stack. Same weights, same chat template — just compiled into ONNX graphs at multiple precisions, including a WebGPU-friendly INT4 + FP16 mix. LiquidAI/LFM2.5-1.2B-JP-202606 は当社の汎用日本語チャットモデルです。本リポジトリはその ONNX エクスポートで、ONNX Runtime / Transformers.js / WebGPU での実行に対応しています。重みおよびチャットテンプレートは同一です。 > **`model_q4f16.onnx` is the recommended variant for WebGPU**: INT4 weights with FP16 scales, FP16 KV cache and conv state I/O, FP32 logits via an inserted Cast — the format Transformers.js targets for browser inference.

• 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.**