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AI 模型 84759
- Qwen3.5-9B-AWQ-BF16-INT8↗
cyankiwi
> [!Note] > This repository contains model weights and configuration files for the post-trained model in the Hugging Face Transformers format. > > These artifacts are compatible with Hugging Face Transformers, vLLM, SGLang, KTransformers, etc. Over recent months, we have intensified our focus on developing foundation models that deliver exceptional utility and performance. Qwen3.5 represents a significant leap forward, integrating breakthroughs in multimodal learning, architectural efficiency, reinforcement learning scale, and global accessibility to empower developers and enterprises with unprecedented capability and efficiency. GPT-OSS-120B GPT-OSS-20B Qwen3-Next-80B-A3B-Thinking Qwen3-30BA3B-Thinking-2507 Qwen3.5-9B Qwen3.5-4B
- Toto-2.0-2.5B↗
Datadog
- Toto-2.0-313m↗
Datadog
Toto (Time Series Optimized Transformer for Observability) is a family of time series foundation models for multivariate forecasting developed by Datadog. Toto 2.0 is the current generation, featuring u-μP-scaled transformers ranging from 4m to 2.5B parameters, all trained from a single recipe. Forecast quality improves reliably with parameter count across the family. The family sets a new state of the art on three forecasting benchmarks: BOOM, our observability benchmark; GIFT-Eval, the standard general-purpose benchmark; and the recent contamination-resistant TIME benchmark. Every Toto 2.0 size sits on or near the Pareto frontier on both BOOM and GIFT-Eval. The three largest sizes rank first, second, and third among foundation models on GIFT-Eval CRPS rank. On TIME, Toto 2.0 sizes take the top three spots on every metric, ahead of every other external foundation model evaluated.
- gemma-4-31B-it-Grand-Horror-X-INTENSE-HERETIC-UNCENSORED-Thinking↗
DavidAU
- DeepSeek-Prover-V1.5-Base↗
DeepSeek
- ESFT-token-code-lite↗
DeepSeek
- gemma-4-26b-a4b-it-oQ8e-mtp-drafter↗
dynamicagency
- Audio8-ASR-Infinite↗
Edge0
**Audio8 ASR Infinite** is a native streaming speech recognition model built to be as responsive as possible. It offers a selectable audio clock (80/120/160 ms) and a transcription delay (240–560 ms). With our adapted vLLM build it transcribes unlimited-length audio **24/7** without drifting. - **Super responsive** — the native streaming architecture decodes 12.5 times per second. - **Unlimited-length transcription** — a rolling KV Cache keeps both **memory and latency constant**, even in **24/7 operation**. - **Selectable streaming clock** — one text token per clock step (12.5 / 8.3 / 6.25 decisions per second), balancing perception granularity and resource cost. - **Configurable transcription delay** — set how much delay to trade for accuracy. - **Semantic VAD** — distinguishes thinking pauses, stuttering and real end of turn, where traditional acoustic VAD usually fails. - **Bilingual** — Chinese and English. The checkpoint has a native context of 30 seconds. But with Rolling KV Cache, it can transcribe 24/7 nonstop.
- regnet-y-040↗
facebook
RegNet model trained on imagenet-1k. It was introduced in the paper Designing Network Design Spaces and first released in this repository. Disclaimer: The team releasing RegNet did not write a model card for this model so this model card has been written by the Hugging Face team. The authors design search spaces to perform Neural Architecture Search (NAS). They first start from a high dimensional search space and iteratively reduce the search space by empirically applying constraints based on the best-performing models sampled by the current search space.
- fish-speech-1.5↗
fishaudio
**Fish Speech V1.5** is a leading text-to-speech (TTS) model trained on more than 1 million hours of audio data in multiple languages. Supported languages: - English (en) >300k hours - Chinese (zh) >300k hours - Japanese (ja) >100k hours - German (de) ~20k hours - French (fr) ~20k hours - Spanish (es) ~20k hours - Korean (ko) ~20k hours - Arabic (ar) ~20k hours - Russian (ru) ~20k hours - Dutch (nl) <10k hours - Italian (it) <10k hours - Polish (pl) <10k hours - Portuguese (pt) <10k hours