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AI 모델 84760
- msmarco-MiniLM-L6-en-de-v1↗
cross-encoder
This is a cross-lingual Cross-Encoder model for EN-DE that can be used for passage re-ranking. It was trained on the MS Marco Passage Ranking task. The performance was evaluated on three datasets: - **TREC-DL19 EN-EN**: The original TREC 2019 Deep Learning Track: Given an English query and 1000 documents (retrieved by BM25 lexical search), rank documents with according to their relevance. We compute NDCG@10. BM25 achieves a score of 45.46, a perfect re-ranker can achieve a score of 95.47. - **TREC-DL19 DE-EN**: The English queries of TREC-DL19 have been translated by a German native speaker to German. We rank the German queries versus the English passages from the original TREC-DL19 setup. We compute NDCG@10. - **GermanDPR DE-DE**: The GermanDPR dataset provides German queries and German passages from Wikipedia. We indexed the 2.8 Million paragraphs from German Wikipedia and retrieved for each query the top 100 most relevant passages using BM25 lexical search with Elasticsearch. We compute MRR@10. BM25 achieves a score of 35.85, a perfect re-ranker can achieve a score of 76.27. We also check the performance of bi-encoders using the same evaluation: The retrieved documents from BM25 lexical search are re-ranked using query & passage embeddings with cosine-similarity. Bi-Encoders can also be used for end-to-end semantic search.
- tada-tts-1b-GGUF↗
cstr
- 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.