Granite3 Moe 1B Instruct - Details

Granite3 Moe 1B Instruct is a large language model developed by Ibm Granite, a company, featuring 1B parameters and released under the Apache License 2.0 (Apache-2.0). It is designed for low-latency applications, leveraging a MoE architecture to enhance efficiency and performance.

Granite-3.0-1B-A400M-Base is a decoder-only language model trained from scratch using a two-stage strategy, with 8 trillion tokens in the first stage and 2 trillion tokens in the second, covering diverse domains. It supports text-to-text generation tasks such as summarization, classification, extraction, and question-answering. The model employs a sparse Mixture of Experts (MoE) transformer architecture with 1.3B parameters, a sequence length of 4096, and 12 language supports including English, German, Spanish, and others.

Granite3 Moe 1B Instruct has 1B parameters, placing it in the small models category, which means it is fast and resource-efficient, ideal for simple tasks. Its 4K context length falls under short contexts, making it suitable for short tasks but limiting its ability to handle very long texts. The model’s design prioritizes low-latency performance while maintaining versatility for common applications.

• Name: Granite3 Moe 1B Instruct
• Parameter Size: 1B
• Context Length: 4K
• Implications: Small parameter count ensures efficiency, while 4K context length supports concise tasks but restricts handling of extended content.

Granite3 Moe 1B Instruct is a versatile model with possible uses in tasks like summarization, text classification, extraction, question-answering, and text-to-text generation. Its multilingual support for languages including Japanese, English, Italian, Dutch, French, Korean, Chinese, Portuguese, Czech, Arabic, German, and Spanish suggests possible applications in cross-lingual content creation, language-specific analysis, or localized text processing. However, these possible uses require further exploration to ensure alignment with specific requirements, as the model’s performance may vary depending on context, domain, or task complexity. The 1B parameter size and 4K context length also imply possible limitations in handling highly specialized or extended tasks, necessitating additional testing.

• summarization
• text classification
• extraction
• question-answering
• text-to-text generation tasks

Granite3 Moe 1B Instruct is a versatile model with possible applications in areas such as text summarization for content condensation, text classification for document organization, question-answering systems for interactive tasks, and text-to-text generation for creative or data processing workflows. These possible uses could support tasks requiring multilingual handling, such as translating or analyzing content across Japanese, English, Italian, Dutch, French, Korean, Chinese, Portuguese, Czech, Arabic, German, and Spanish. However, the possible effectiveness of these applications depends on specific contexts, and further testing is possible to ensure alignment with user needs. The model’s 1B parameter size and 4K context length suggest possible suitability for tasks that balance efficiency and complexity, but possible limitations may exist in highly specialized scenarios.

• text summarization
• text classification
• question-answering
• text-to-text generation

Granite3 Moe 1B Instruct with the q4 quantization requires a GPU with at least 8GB VRAM and a multi-core CPU for optimal performance, making it a possible choice for systems with moderate hardware capabilities. This version balances precision and efficiency, allowing possible deployment on consumer-grade GPUs. System memory of at least 32GB is recommended for stability.

fp16, q2, q3, q4, q5, q6, q8

Granite3 Moe 1B Instruct is a 1B parameter large language model with a sparse Mixture of Experts (MoE) architecture, designed for low-latency applications and supporting 12 languages including English, German, and Spanish. It operates under the Apache License 2.0, making it open-source and accessible for tasks like summarization, classification, and question-answering, with a 4K context length for efficient text processing.

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