granite-4.0-micro-base
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granite-4.0-micro-base-GGUF
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查看详情 ↗granite-4.0-micro-GGUF
ibm-granite> [!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-micro
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查看详情 ↗granite-4.0-tiny-base-preview
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查看详情 ↗granite-4.0-tiny-base-preview-GGUF
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查看详情 ↗granite-4.0-tiny-preview
ibm-granite**Model Summary:** Granite-4-Tiny-Preview is a 7B parameter fine-grained hybrid mixture-of-experts (MoE) instruct model fine-tuned from Granite-4.0-Tiny-Base-Preview using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets tailored for solving long context problems. This model is developed using a diverse set of techniques with a structured chat format, including supervised fine-tuning, and model alignment using reinforcement learning. - **Developers:** Granite Team, IBM - **Website**: Granite Docs - **Release Date**: May 2nd, 2025 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. However, users may fine-tune this Granite model for languages beyond these 12 languages.
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查看详情 ↗granite-4.0-tiny-preview-GGUF
ibm-granite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 ↗granite-4.1-30b
ibm-granite**Model Summary:** Granite-4.1-30B is a 30B parameter long-context instruct model finetuned from *Granite-4.1-30B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.1 Language Models HF Collection - **Technical Blog:** Granite-4.1 Blog - **GitHub Repository:** ibm-granite/granite-4.1-language-models - **Website**: Granite Docs - **Release Date**: April 29th, 2026 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.
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查看详情 ↗granite-4.1-30b-base
ibm-granite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 ↗granite-4.1-30b-fp8
ibm-granite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 ↗granite-4.1-30b-GGUF
ibm-granite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 ↗granite-4.1-3b
ibm-granite**Model Summary:** Granite-4.1-3B is a 3B parameter long-context instruct model finetuned from *Granite-4.1-3B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.1 Language Models HF Collection - **Technical Blog:** Granite-4.1 Blog - **GitHub Repository:** ibm-granite/granite-4.1-language-models - **Website**: Granite Docs - **Release Date**: April 29th, 2026 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.
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查看详情 ↗granite-4.1-3b-base
ibm-granite**Model Summary:** Granite‑4.1‑3B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of general text‑to‑text generation tasks, as well as fill‑in‑the‑Middle (FIM) code completion. This model shares the same underlying architecture and weights as Granite 4.0 3B Micro, which is trained from scratch on approximately 15 trillion tokens following a four-stage training strategy: 10 trillion tokens in the first stage, 2 trillion in the second, another 2 trillion in the third, and 0.5 trillion in the final stage. An additional training phase is applied to expand the model’s context window to 512K tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, code-completion (including FIM), and long-context generation tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
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查看详情 ↗granite-4.1-3b-fp8
ibm-granite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 ↗granite-4.1-3b-GGUF
ibm-granite> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite `.safetensors` model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.1-3b
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查看详情 ↗granite-4.1-8b
ibm-granite**Model Summary:** Granite-4.1-8B is a 8B parameter long-context instruct model finetuned from *Granite-4.1-8B-Base* using a combination of open source instruction datasets with permissive license and internally collected synthetic datasets. Granite 4.1 models have gone through an improved post-training pipeline, including supervised finetuning and reinforcement learning alignment, resulting in enhanced tool calling, instruction following, and chat capabilities. - **Developers:** Granite Team, IBM - **HF Collection:** Granite 4.1 Language Models HF Collection - **Technical Blog:** Granite-4.1 Blog - **GitHub Repository:** ibm-granite/granite-4.1-language-models - **Website**: Granite Docs - **Release Date**: April 29th, 2026 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages.
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查看详情 ↗granite-4.1-8b-base
ibm-granite**Model Summary:** Granite‑4.1‑8B‑Base is a decoder‑only language model with long‑context capabilities, designed to support a broad range of text‑to‑text generation tasks. In addition to standard generation, it supports Fill‑in‑the‑Middle (FIM) code completion through specialized prefix and suffix tokens. The model is trained from scratch on approximately 15 trillion tokens using a five‑phase training strategy: 10 trillion tokens in phase one, 2 trillion tokens each in phases two and three, and 0.5 trillion tokens in phase four. In the final phase, long‑context extension is applied to expand the model’s context window to 512K tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 4.1 models for languages beyond these languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, code-completion (including FIM), and long-context generation tasks. All Granite Base models are able to handle these tasks as they were trained on a large amount of data from various domains. Moreover, they can serve as baseline to create specialized models for specific application scenarios.
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查看详情 ↗granite-4.1-8b-fp8
ibm-granite> [!NOTE] > This repository contains the FP8 version of **4.1-8B**. > > Please refer to the the original instruct model's model card for additional details: > https://huggingface.co/ibm-granite/granite-4.1-8b
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查看详情 ↗granite-4.1-8b-GGUF
ibm-granite> [!NOTE] > This repository contains models that have been converted to the GGUF format with various quantizations from an IBM Granite `.safetensors` model. > > Please reference the base model's full model card here: > https://huggingface.co/ibm-granite/granite-4.1-8b
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查看详情 ↗granite-4.2-30b
ibm-graniteGranite is a family of open-source large language models developed by IBM, designed for enterprise and research use. Granite models are built to be versatile, safe, and efficient — covering a range of sizes and capabilities from compact edge-deployable models to large-scale reasoning systems. All Granite models are released under the Apache 2.0 license, enabling unrestricted commercial and academic use. The Granite 4.2 generation introduces native reasoning (thinking) capabilities, allowing models to perform step-by-step chain-of-thought reasoning before producing final answers. This significantly improves performance on complex math, coding, multi-step logic, and agentic tool-calling tasks. **Granite-4.2-30B** is the flagship reasoning model in the Granite 4.2 family. It delivers the strongest performance across reasoning-intensive tasks by leveraging built-in ` ... ` chain-of-thought. It supports flexible thinking modes — full thinking (default), non-thinking, and low-effort — allowing users to balance depth vs. latency on a per-query basis.
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查看详情 ↗granite-4.2-30b-bf16-mlx
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查看详情 ↗granite-4.2-30b-fp8
ibm-granite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 ↗granite-4.2-30b-GGUF
ibm-granite> [!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-4.2-30b
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查看详情 ↗granite-4.2-30b-mxfp4
ibm-granite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 ↗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。