从语言到影像，从开源到 API。读懂模型能力、比较真实成本，
为你的下一个想法，找到合适的智能引擎。
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-350m
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**Model Summary:** Granite-4.0-3B-Vision is a vision-language model (VLM) designed for enterprise-grade document data extraction. It focuses on specialized, complex extraction tasks that ultracompact models often struggle with: - **Chart extraction:** Converting charts into structured, machine-readable formats (Chart2CSV, Chart2Summary, and Chart2Code) - **Table extraction:** Accurately extracting tables with complex layouts from document images to JSON, HTML, or OTSL - **Semantic Key-Value Pair (KVP) extraction:** Extracting values based on key names and descriptions across diverse document layouts The model is delivered as a LoRA adapter on top of Granite 4.0 Micro, with a 3.5B base LLM and 0.5B LoRA adapters. This enables a single deployment to support both multimodal document understanding and text-only workloads — the base model handles text-only requests without loading the adapter. See Model Architecture for details.
译文排队中 · 当前显示来源原文
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-h-1b
译文排队中 · 当前显示来源原文
**Model Summary:** Granite-4.0-H-350M is a lightweight instruct model finetuned from *Granite-4.0-H-350M-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques including supervised finetuning, reinforcement learning, and model merging. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may fine-tune Granite 4.0 Nano models to support languages beyond those included in this list. **Intended use:** Granite 4.0 Nano instruct models feature strong instruction following capabilities bringing advanced AI capabilities within reach for on-device deployments and research use cases. Additionally, their compact size makes them well-suited for fine-tuning on specialized domains without requiring massive compute resources.
译文排队中 · 当前显示来源原文
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-h-350m
译文排队中 · 当前显示来源原文
📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. **Model Summary:** Granite-4.0-H-Micro is a 3B parameter long-context instruct model finetuned from *Granite-4.0-H-Micro-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.0 models for languages beyond these languages.
译文排队中 · 当前显示来源原文
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-h-micro
译文排队中 · 当前显示来源原文
📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. **Model Summary:** Granite-4.0-H-Small is a 32B parameter long-context instruct model finetuned from *Granite-4.0-H-Small-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.0 Language Models HF Collection - **GitHub Repository:** ibm-granite/granite-4.0-language-models - **Website**: Granite Docs - **Release Date**: October 2nd, 2025 - **License:** Apache 2.0
译文排队中 · 当前显示来源原文
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
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。
基础资料已收录 · 缺失资料后续补充
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-h-small
译文排队中 · 当前显示来源原文
📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. **Model Summary:** Granite-4.0-H-Tiny is a 7B parameter long-context instruct model finetuned from *Granite-4.0-H-Tiny-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.0 Language Models HF Collection - **GitHub Repository:** ibm-granite/granite-4.0-language-models - **Website**: Granite Docs - **Release Date**: October 2nd, 2025 - **License:** Apache 2.0
译文排队中 · 当前显示来源原文
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。
基础资料已收录 · 缺失资料后续补充
> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite base model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.0-h-tiny
译文排队中 · 当前显示来源原文
📣 **Update [10-07-2025]:** Added a *default system prompt* to the chat template to guide the model towards more *professional, accurate, and safe* responses. **Model Summary:** Granite-4.0-Micro is a 3B parameter long-context instruct model finetuned from *Granite-4.0-Micro-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. This model is developed using a diverse set of techniques with a structured chat format, including supervised finetuning, model alignment using reinforcement learning, and model merging. Granite 4.0 instruct models feature improved *instruction following (IF)* and *tool-calling* capabilities, making them more effective in enterprise applications. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.0 Language Models HF Collection - **GitHub Repository:** ibm-granite/granite-4.0-language-models - **Website**: Granite Docs - **Release Date**: October 2nd, 2025 - **License:** Apache 2.0
译文排队中 · 当前显示来源原文
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。
