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探索 AI 模型 84999
wav2vec2-large-xls-r-300m-armenian
infinitejoyThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - HY-AM dataset. It achieves the following results on the evaluation set: - Loss: 0.9669 - Wer: 0.6942 The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 200.0 - mixed_precision_training: Native AMP - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.17.1.dev0 - Tokenizers 0.11.0
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查看详情 ↗wav2vec2-large-xls-r-300m-bulgarian
infinitejoyThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - BG dataset. It achieves the following results on the evaluation set: - Loss: 0.4487 - Wer: 0.4674 The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 32 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 100.0 - mixed_precision_training: Native AMP
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查看详情 ↗wav2vec2-large-xls-r-300m-hindi
infinitejoy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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查看详情 ↗wav2vec2-large-xls-r-300m-welsh
infinitejoyThis model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - CY dataset. It achieves the following results on the evaluation set: - Loss: 0.2650 - Wer: 0.2702 The following hyperparameters were used during training: - learning_rate: 7e-05 - train_batch_size: 32 - eval_batch_size: 16 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 3000 - num_epochs: 50.0 - mixed_precision_training: Native AMP - Transformers 4.16.0.dev0 - Pytorch 1.10.1+cu102 - Datasets 1.18.3 - Tokenizers 0.11.0
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InfiniteYou
字节跳动基础档案已收录，详细介绍后续补充。可先查看厂商、分类与来源。
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查看详情 ↗LFM2.5-24B-A2B-NVFP4
infinity-ai-institute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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查看详情 ↗UFM-Base
infinity1096Yuchen Zhang, Nikhil Keetha, Chenwei Lyu, Bhuvan Jhamb, Yutian Chen Yuheng Qiu, Jay Karhade, Shreyas Jha, Yaoyu Hu Deva Ramanan, Sebastian Scherer, Wenshan Wang UFM(UniFlowMatch) is a simple, end-to-end trained transformer model that directly regresses pixel displacement image that applies concurrently to both optical flow and wide-baseline matching tasks. If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
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查看详情 ↗UFM-Base-980
infinity1096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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查看详情 ↗UFM-Base-DINOv2G-init
infinity1096This model has been pushed to the Hub using the PytorchModelHubMixin integration: - Code: [More Information Needed] - Paper: [More Information Needed] - Docs: [More Information Needed]
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查看详情 ↗UFM-Base-DINOv2L-init
infinity1096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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查看详情 ↗UFM-Refine
infinity1096Yuchen Zhang, Nikhil Keetha, Chenwei Lyu, Bhuvan Jhamb, Yutian Chen Yuheng Qiu, Jay Karhade, Shreyas Jha, Yaoyu Hu Deva Ramanan, Sebastian Scherer, Wenshan Wang UFM(UniFlowMatch) is a simple, end-to-end trained transformer model that directly regresses pixel displacement image that applies concurrently to both optical flow and wide-baseline matching tasks. If you find our repository useful, please consider giving it a star ⭐ and citing our paper in your work:
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查看详情 ↗MN-12B-Mag-Mell-R1
inflatebotNOTE for newer users: "R1" here means "Revision 1". This model predates DeepSeek's R1; DeepSeek inadvertently made using this versioning scheme very annoying! This is a merge of pre-trained language models created using mergekit. Mag Mell R1 was tested with Temp 1.25 and MinP 0.2. This was fairly stable up to 10K, but this might be too "hot". If issues with coherency occur, try *in*creasing MinP or *de*creasing Temperature.
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查看详情 ↗MN-12B-Mag-Mell-R1-GGUF
inflatebot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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价格待核验多模态与渠道费用须分别核对
查看详情 ↗inf-retriever-v1
infly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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查看详情 ↗- **INF-Retriever-v1-1.5B** is a lightweight version of the **INF-Retriever-v1**, an LLM-based dense retrieval model developed by INF TECH. It is built upon the gte-Qwen2-1.5B-instruct model and specifically fine-tuned to excel in retrieval tasks, particularly for Chinese and English data. - As of February 19, 2025, **INF-Retriever-v1-1.5B** ranks both **No.1** on the Automated Heterogeneous Information Retrieval Benchmark of version 24.04 & 24.05(AIR-Bench) for the bilingual Chinese and English sub-leaderboard, among models with fewer than 7B parameters. This demonstrates its cutting-edge performance in heterogeneous information retrieval tasks. - **Optimized for Chinese and English retrieval**: The model has been specifically fine-tuned with retrieval-focused datasets in both languages, significantly improving its accuracy and efficiency for a variety of retrieval scenarios.
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查看详情 ↗Infinity-Parser-7B
infly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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查看详情 ↗We are excited to release Infinity-Parser2, our latest flagship document understanding model. We offer two distinct variants to address diverse deployment constraints: Infinity-Parser2-Pro, optimized for maximum accuracy in precision-critical tasks, achieves state-of-the-art results on olmOCR-Bench (87.6%) and ParseBench (74.3%), surpassing frontier models including DeepSeek-OCR-2, PaddleOCR-VL-1.5, and MinerU2.5. Infinity-Parser2-Flash, engineered for low-latency inference, delivers a 3.68x speedup over our previous Infinity-Parser-7B model. With significant upgrades to both our data engine and multi-task reinforcement learning approach, the model consolidates robust multi-modal parsing capabilities into a unified architecture, unlocking brand-new zero-shot capabilities across a wide range of real-world business scenarios. Task Infinity-Parser2-Pro Infinity-Parser2-Flash PaddleOCR-VL-1.5 DeepSeek-OCR-2 MinerU2.5 Gemini-3-Pro Note: '*' denotes results evaluated using our internal evaluation tools. '‡' denotes results from the Gemini-3.1-Pro.
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查看详情 ↗Infinity-Parser2-Pro
infly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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查看详情 ↗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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查看详情 ↗Paper Link 👁️ --> 🏠 Home Page    |    🤗 Model    |    📊 Dataset    |    📄 Paper    |    🚀 Demo    **OpenCoder** is an open and reproducible code LLM family which includes 1.5B and 8B base and chat models, supporting both English and Chinese languages. Starting from scratch, OpenCoder is pretrained on 2.5 trillion tokens composed of 90% raw code and 10% code-related web data, and supervised finetuned on over 4.5M high-quality SFT examples, finally reaching the performance of top-tier code LLMs. We provide not only model weights and inference code, but also the reproducible training data, the complete data processing pipeline, rigorous experimental ablation results, and detailed training protocols. Empowering researchers to build and innovate, OpenCoder is your open foundation for advancing code AI. OpenCoder series (including Base and Chat) support commercial applications under a permissive License.
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查看详情 ↗pplx-embed-v1-0.6b-acta-ita
InfocubeSrl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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查看详情 ↗cogito-14b-voice-GGUF
infohound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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查看详情 ↗vllm-translategemma-12b-it
Infomaniak-AIThe original TranslateGemma model requires a structured payload with dedicated `source_lang_code` and `target_lang_code` fields: However, vLLM does not support these custom content parameters. To maintain compatibility, the chat template has been modified to encode language codes directly in the message content using a delimiter-based format: The original model uses the new Transformers RoPE configuration format with separate attention type settings:
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查看详情 ↗暂未找到匹配模型
可以调整厂商、分类或输入输出条件；新模型正持续收录。
COST EXPLORER
好模型，也要算好成本。
调整月度 Token 用量，直观比较不同模型的预估 API 开支。
1 M = 100 万 Token · 美元计价
仅比较同一渠道的基础文本 Token 报价
仅比较同一渠道的基础文本 Token 报价
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。
MODEL HANDBOOK
读懂模型，再做选择
API 价格应该怎么比较？+
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。
开放权重，就代表可以免费商用吗？+
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。
排行榜第一，就是最适合我的模型吗？+
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。