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PowerLM-3B is a 3B state-of-the-art small language model trained with the Power learning rate scheduler. It is trained on a mix of open-source and proprietary datasets. PowerLM-3B has shown promising results compared to other models in the size categories across various benchmarks, including natural language multi-choices, code generation, and math reasoning. Paper: https://arxiv.org/abs/2408.13359
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PowerMoE-3B is a 3B sparse Mixture-of-Experts (sMoE) language model trained with the Power learning rate scheduler. It sparsely activates 800M parameters for each token. It is trained on a mix of open-source and proprietary datasets. PowerMoE-3B has shown promising results compared to other dense models with 2x activate parameters across various benchmarks, including natural language multi-choices, code generation, and math reasoning. Paper: https://arxiv.org/abs/2408.13359
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
TinyTimeMixers (TTMs) are compact pre-trained models for Multivariate Time-Series Forecasting, open-sourced by IBM Research. **With model sizes starting from 1M params, TTM (accepted in NeurIPS 24) introduces the notion of the first-ever “tiny” pre-trained models for Time-Series Forecasting.** This model card contains the model-weights for research-use only and full reproducibility of our results published in our paper. However - if you are looking for TTM model weights for commercial and enterprise use, please refer to our granite releases here TTM outperforms several popular benchmarks demanding billions of parameters in zero-shot and few-shot forecasting. TTMs are lightweight forecasters, pre-trained on publicly available time series data with various augmentations. TTM provides state-of-the-art zero-shot forecasts and can easily be fine-tuned for multi-variate forecasts with just 5% of the training data to be competitive. Refer to our paper for more details.
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> [!IMPORTANT] > This repository is an AWQ 4-bit quantized version of `meta-llama/Llama-3.3-70B-Instruct`, originally released by Meta AI. This model was quantized using AutoAWQ from FP16 down to INT4 using GEMM kernels, with zero-point quantization and a group size of 128. Hardware: Intel Xeon CPU E5-2699A v4 @ 2.40GHz, 256GB of RAM, and 2x NVIDIA RTX 3090.
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RegesCore's packaging of Ornith-1.0-35B, the 35-billion-parameter mixture-of-experts agentic coding model from the DeepReinforce Team. The weights are Unsloth's GGUF quantizations of Ornith, unchanged; RegesCore adds system prompts, fixed chat templates, an Ollama Modelfile and setup notes, so it runs well on your own hardware with llama.cpp, LM Studio, Ollama or any OpenAI-compatible server, and inside the Reges apps. Built by **Like A King Inc.** (New York), the company behind **RegesCore** (private, local-first AI that runs on your machine or inside your building) and **Reges Code** (a free, local-first AI code editor). RegesCore-1.0-35B is Ornith-1.0-35B set up for the work RegesCore does every day: Linux and infrastructure operations, security tooling and reverse-engineering workflows, and Lua game-server runtimes. That focus comes from the system prompts and templates in this repository, not from retraining: **version 1.0 has not been fine-tuned.** A fine-tuned release will ship under a new version number with its own card.
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This model is a fine-tuned version of omarxadel/wav2vec2-large-xlsr-53-arabic-egyptian, enhancing **Egyptian Arabic**, **Modern Standard Arabic (MSA)** and **Gulf / Levantine Arabic** for Automatic Speech Recognition. It was trained on a diverse combination of publicly available and custom-collected Arabic speech datasets, including: \*: *Whisper models were decoded using beam search (`beam_size = 5`) and evaluated using `BasicTextNormalizer` with `remove_diacritics=False` and `split_letters=False`, applied to both predictions and reference text.*
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可以调整厂商、分类或输入输出条件；新模型正持续收录。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
使用已采集且单位可比的 USD / 百万 Token 渠道报价。报价为采集时快照；不计缓存优惠、阶梯价、推理额外输出与税费，请以来源的当前报价为准。
先区分输入、输出和缓存价格，再使用同一任务和用量估算总费用。图像、音频和视频可能按张、时长或分辨率计费，不能直接与 Token 单价比较。
不一定。必须逐一核对具体模型版本的许可证、商用限制及使用政策。自己部署还需要考虑显存、推理资源和运维费用。
榜单取决于测试任务、版本与评测方法。建议用实际业务样本比较准确性、时延、稳定性和成本，再选择模型。
