granite-20b-code-base-8k
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granite-20b-code-base-8k-GGUF
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View details ↗granite-20b-code-base-r1.1
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View details ↗granite-20b-code-instruct-8k
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View details ↗granite-20b-code-instruct-8k-GGUF
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View details ↗granite-20b-code-instruct-accelerator
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View details ↗granite-20b-code-instruct-r1.1
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View details ↗granite-20b-functioncalling
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View details ↗granite-3.0-1b-a400m-base
ibm-granite**Model Summary:** Granite-3.0-1B-A400M-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 8 trillion tokens sourced from diverse domains. During the second stage, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages. **Intended use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. 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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View details ↗granite-3.0-1b-a400m-instruct
ibm-granite**Model Summary:** Granite-3.0-1B-A400M-Instruct is an 1B parameter model finetuned from *Granite-3.0-1B-A400M-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. - **Developers:** Granite Team, IBM - **GitHub Repository:** ibm-granite/granite-3.0-language-models - **Website**: Granite Docs - **Paper:** Granite 3.0 Language Models - **Release Date**: October 21st, 2024 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages.
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View details ↗granite-3.0-2b-base
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View details ↗granite-3.0-2b-instruct
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View details ↗granite-3.0-3b-a800m-base
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View details ↗granite-3.0-3b-a800m-instruct
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View details ↗granite-3.0-8b-base
ibm-granite**Model Summary:** Granite-3.0-8B-Base is a decoder-only language model to support a variety of text-to-text generation tasks. It is trained from scratch following a two-stage training strategy. In the first stage, it is trained on 10 trillion tokens sourced from diverse domains. During the second stage, it is further trained on 2 trillion tokens using a carefully curated mix of high-quality data, aiming to enhance its performance on specific tasks. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages. **Intended use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. 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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View details ↗granite-3.0-8b-instruct
ibm-granite**Model Summary:** Granite-3.0-8B-Instruct is a 8B parameter model finetuned from *Granite-3.0-8B-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. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.0 models for languages beyond these 12 languages. **Intended use:** The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
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View details ↗granite-3.0-8b-instruct-accelerator
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View details ↗granite-3.0-8b-lora-intrinsics-v0.1
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View details ↗granite-3.1-1b-a400m-base
ibm-granite**Model Summary:** Granite-3.1-1B-A400M-Base extends the context length of Granite-3.0-1B-A400M-Base from 4K to 128K using a progressive training strategy by increasing the supported context length in increments while adjusting RoPE theta until the model has successfully adapted to desired length of 128K. This long-context pre-training stage was performed using approximately 500B tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. 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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View details ↗granite-3.1-1b-a400m-instruct
ibm-granite**Model Summary:** Granite-3.1-1B-A400M-Instruct is a 1B parameter long-context instruct model finetuned from Granite-3.1-1B-A400M-Base 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 finetuning, model alignment using reinforcement learning, and model merging. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
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View details ↗granite-3.1-2b-base
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View details ↗granite-3.1-2b-instruct
ibm-granite**Model Summary:** Granite-3.1-2B-Instruct is a 2B parameter long-context instruct model finetuned from Granite-3.1-2B-Base 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 finetuning, model alignment using reinforcement learning, and model merging. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** The model is designed to respond to general instructions and can be used to build AI assistants for multiple domains, including business applications.
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View details ↗granite-3.1-3b-a800m-base
ibm-granite**Model Summary:** Granite-3.1-3B-A800M-Base extends the context length of Granite-3.0-3B-A800M-Base from 4K to 128K using a progressive training strategy by increasing the supported context length in increments while adjusting RoPE theta until the model has successfully adapted to desired length of 128K. This long-context pre-training stage was performed using approximately 500B tokens. **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages. **Intended Use:** Prominent use cases of LLMs in text-to-text generation include summarization, text classification, extraction, question-answering, and more. 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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View details ↗granite-3.1-3b-a800m-instruct
ibm-granite**Model Summary:** Granite-3.1-3B-A800M-Instruct is a 3B parameter long-context instruct model finetuned from Granite-3.1-3B-A800M-Base 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 finetuning, model alignment using reinforcement learning, and model merging. - **Developers:** Granite Team, IBM - **GitHub Repository:** ibm-granite/granite-3.1-language-models - **Website**: Granite Docs - **Paper:** Granite 3.1 Language Models (coming soon) - **Release Date**: December 18th, 2024 - **License:** Apache 2.0 **Supported Languages:** English, German, Spanish, French, Japanese, Portuguese, Arabic, Czech, Italian, Korean, Dutch, and Chinese. Users may finetune Granite 3.1 models for languages beyond these 12 languages.
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