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Explore AI models 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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View details ↗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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View details ↗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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View details ↗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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View details ↗
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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗- **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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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View details ↗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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