granite-8b-code-base-4k-GGUF
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探索 AI 模型 84999
granite-8b-code-instruct-128k
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查看详情 ↗granite-8b-code-instruct-4k
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查看详情 ↗granite-8b-code-instruct-4k-GGUF
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查看详情 ↗granite-8b-code-instruct-accelerator
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查看详情 ↗granite-docling-258M
ibm-graniteGranite Docling is a multimodal Image-Text-to-Text model engineered for efficient document conversion. It preserves the core features of Docling while maintaining seamless integration with DoclingDocuments to ensure full compatibility. Granite Docling 258M builds upon the Idefics3 architecture, but introduces two key modifications: it replaces the vision encoder with siglip2-base-patch16-512 and substitutes the language model with a Granite 165M LLM. Try out our Granite-Docling-258 demo today. - **Developed by**: IBM Research - **Model type**: Multi-modal model (image+text-to-text) - **Language(s)**: English (NLP) - **License**: Apache 2.0 - **Release Date**: September 17, 2025
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查看详情 ↗granite-docling-258M-GGUF
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查看详情 ↗granite-docling-258M-mlx
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查看详情 ↗granite-embedding-107m-multilingual
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查看详情 ↗granite-embedding-125m-english
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查看详情 ↗granite-embedding-278m-multilingual
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查看详情 ↗granite-embedding-30m-english
ibm-granite**Model Summary:** Granite-Embedding-30m-English is a 30M parameter dense bi-encoder embedding model from the Granite Embeddings suite that can be used to generate high quality text embeddings. This model produces embedding vectors of size 384 and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pre-training, contrastive fine-tuning, knowledge distillation and model merging for improved performance. ***Granite-embedding-30m-r1.1*** was specifically designed to support multi-turn information retrieval and is designed to handle contextual document retrieval in multi-turn conversational information retrieval. Granite-embedding-30m-r1.1 was trained on data tailored for multi-turn conversational information retrieval and uses multi-teacher distillation over granite-embedding-30m-english (https://huggingface.co/ibm-granite/granite-embedding-30m-english) - **Developers:** Granite Embedding Team, IBM - **GitHub Repository:** ibm-granite/granite-embedding-models - **Website**: Granite Docs - **Paper:** Technical Report - **Release Date**: August 29, 2025 (granite-embedding-30m-english-r1.1) - **License:** Apache 2.0
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查看详情 ↗granite-embedding-30m-sparse
ibm-granite**Model Summary:** Granite-Embedding-30m-Sparse is a 30M parameter sparse biencoder embedding model from the Granite Experimental suite that can be used to generate high quality text embeddings. This model produces variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights and is trained using a combination of open source relevance-pair datasets with permissive, enterprise-friendly license, and IBM collected and generated datasets. While maintaining competitive scores on academic benchmarks such as BEIR, this model also performs well on many enterprise use cases. This model is developed using retrieval oriented pretraining, contrastive finetuning and knowledge distillation for improved performance. **Intended use:** The model is designed to produce variable length bag-of-word like dictionary, containing expansions of sentence tokens and their corresponding weights, for a given text, which can be used for text similarity, retrieval, and search applications. The model can then be used to encode pairs of text and find the similarity between their representations
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查看详情 ↗granite-embedding-311m-multilingual-r2
ibm-granite**Model Summary:** Granite-Embedding-311M-Multilingual-R2 is a 311M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings. It produces 768-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with enhanced support for 52 languages and programming code that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets. > Granite Embedding 311M Multilingual R2 shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. The multilingual R2 model scores **65.2** on Multilingual MTEB Retrieval (18 tasks) — a **+13 point improvement** over granite-embedding-278m-multilingual (52.2) — and averages **56.3** across all retrieval benchmarks, representing a **+14.5 point gain** over the previous generation. It supports Matryoshka dimension reduction, 32k-token context, and ships with ONNX and OpenVINO models for production deployment. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, GeGLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Expanded vocabulary:** 262K multilingual tokenizer trained on text and code across 200+ languages. - **Matryoshka support:** Truncate embeddings to 512, 384, 256, or 128 dimensions with graceful degradation. - **Broader code coverage:** Code retrieval supported for Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers, contrastive fine-tuning, and model merging yield +14.5 points on average retrieval benchmarks. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
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查看详情 ↗granite-embedding-97m-multilingual-r2
ibm-granite**Model Summary:** Granite-Embedding-97M-Multilingual-R2 is a 97M parameter dense embedding model from the Granite Embeddings collection for high-quality multilingual text embeddings at minimal compute cost. It produces 384-dimensional vectors with a context length of up to 32,768 tokens. The model supports **200+ languages** (based on the multilingual pretraining corpus of the underlying encoder), with **enhanced support for 52 languages and programming code** that receive explicit retrieval-pair and cross-lingual training. All training data uses permissive, enterprise-friendly licenses, plus IBM-collected and IBM-generated datasets. > Granite Embedding 97M Multilingual R2 scores **60.3** on Multilingual MTEB Retrieval (18 tasks) — the highest retrieval score of any open multilingual embedding model under 100M parameters, outperforming the next-best model in its size class (multilingual-e5-small at 50.9) by **+9.4 points** — while being roughly **3× smaller** than the full-size granite-embedding-311m-multilingual-r2. The multilingual R2 model shows strong performance across multilingual information retrieval benchmarks, code retrieval, long-document search, conversational multi-turn, and reasoning retrieval tasks. - **Architecture upgrade:** ModernBERT replaces XLM-RoBERTa, bringing alternating attention, SiLU activations, and rotary position embeddings. - **Extended context:** 32,768 tokens (up from 512 in R1), enabling long-document and multi-passage retrieval. - **Compact multilingual vocabulary:** A purpose-trained 180K-token tokenizer preserves broad multilingual coverage while reducing model size. - **Model pruning:** Layer pruning (22 → 12 layers) from the full-size multilingual model, followed by continued distillation training to recover quality. - **Broader code coverage:** Code retrieval training set that includes Python, Go, Java, JavaScript, PHP, Ruby, SQL, C, C++. - **Training advances:** Knowledge distillation from multiple teachers and contrastive fine-tuning yield a **+14.6 point** average gain over the previous-generation granite-embedding-107m-multilingual. - **Deployment flexibility:** Released with ONNX and OpenVINO models; compatible with vLLM and llama.cpp (GGUF).
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查看详情 ↗granite-embedding-english-r2
ibm-granite**Model Summary:** Granite-embedding-english-r2 is a 149M parameter dense biencoder embedding model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. The r2 models show strong performance across standard and IBM-built information retrieval benchmarks (BEIR, ClapNQ), code retrieval (COIR), long-document search benchmarks (MLDR, LongEmbed), conversational multi-turn (MTRAG), table retrieval (NQTables, OTT-QA, AIT-QA, MultiHierTT, OpenWikiTables), and on many enterprise use cases. These models use a bi-encoder architecture to generate high-quality embeddings from text inputs such as queries, passages, and documents, enabling seamless comparison through cosine similarity. Built using retrieval oriented pretraining, contrastive finetuning, knowledge distillation, and model merging, granite-embedding-english-r2 is optimized to ensure strong alignment between query and passage embeddings.
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查看详情 ↗granite-embedding-reranker-english-r2
ibm-granite**Model Summary:** _granite-embedding-reranker-english-r2_ is a 149M parameter dense cross-encoder model from the Granite Embeddings collection that can be used to generate high quality text embeddings. This model produces embedding vectors of size 768 based on context length of upto 8192 tokens. Compared to most other open-source models, this model was only trained using open-source relevance-pair datasets with permissive, enterprise-friendly license, plus IBM collected and generated datasets. The _granite-embedding-reranker-english-r2_ model uses a cross-encoder architecture to compute high-quality relevance scores between queries and documents by jointly encoding their text, enabling precise reranking based on contextual alignment. The model is trained with ranking-specific loss functions such as pListMLE, along with model merging techniques to enhance performance. The reranker model shows strong performance on standard information retrieval benchmark (BEIR, MIRACL), long-document search benchmarks (MLDR), and on many enterprise use cases. The latest granite embedding r2 release introduces two English embedding models, and one English reranking all based on the ModernBERT architecture: - _granite-embedding-english-r2_ (**149M** parameters): with an output embedding size of _768_, replacing _granite-embedding-125m-english_. - _granite-embedding-small-english-r2_ (**47M** parameters): A _first-of-its-kind_ reduced-size model, with 8192 context length support, fewer layers and a smaller output embedding size (_384_), replacing _granite-embedding-30m-english_. - **_granite-embedding-reranker-english-r2_** (**149M** parameters): reranker model based on _granite-embedding-english-r2_, with an output embedding size of _768_.
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查看详情 ↗granite-embedding-small-english-r2
ibm-granite**模型摘要：** Granite-embedding-small-english-r2 是 Granite Embeddings 系列中的一个 4700 万参数稠密双编码器嵌入模型，可用于生成高质量的文本嵌入。该模型根据最长 8192 个 token 的上下文长度，输出维度为 384 的嵌入向量。与其他大多数开源模型相比，该模型仅使用开放许可且对企业友好的相关性配对数据集进行训练，此外还使用了 IBM 收集和生成的数据集。r2 版本的模型在标准及 IBM 构建的信息检索基准测试（BEIR、ClapNQ）、代码检索（COIR）、长文档搜索基准测试（MLDR、LongEmbed）、多轮对话（MTRAG）、表格检索（NQTables、OTT-QA、AIT-QA、MultiHierTT、OpenWikiTables）以及众多企业应用场景中均表现出色。这些模型采用双编码器架构，能够从查询、段落和文档等文本输入中生成高质量嵌入，从而通过余弦相似度实现无缝比较。Granite-embedding-small-english-r2 基于面向检索的预训练、对比微调、知识蒸馏和模型合并技术构建，经过优化以确保查询与段落嵌入之间具有良好的对齐性。
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查看详情 ↗granite-geospatial-biomass
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查看详情 ↗granite-geospatial-canopyheight
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查看详情 ↗granite-geospatial-land-surface-temperature
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查看详情 ↗granite-geospatial-ocean
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查看详情 ↗granite-geospatial-uki
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查看详情 ↗granite-geospatial-uki-flooddetection
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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。