言語から画像、オープンウェイトからAPIまで。能力と実際の費用を比較。
次のアイデアに合うAIを見つけましょう。
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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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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提供元・分類・入出力の条件を変更してください。新しいモデルを随時追加しています。
月間トークン数を調整してAPI費用を比較。
収集済みの比較可能な100万トークン当たり米ドル料金です。キャッシュ割引、段階料金、追加推論出力、税金は含みません。最新料金は出典で確認してください。
入力・出力・キャッシュ料金を分け、同じ用途と使用量で総費用を比較します。画像・音声・動画は枚数、時間、解像度で課金される場合があります。
必ずしもそうではありません。バージョンごとのライセンス、商用制限、ポリシーを確認してください。自前運用には計算資源と保守費用も必要です。
順位はテスト、バージョン、評価方法によります。実際の業務データで精度、遅延、安定性、費用を比較してください。
