Granite3.1 Moe 3B Instruct - Model Details

Granite3.1 Moe 3B Instruct is a large language model developed by Ibm Granite with 3b parameters. It operates under the Apache License 2.0 and is designed as an instruct model, optimized for following instructions. Trained on extensive data, it excels in long-context tasks and supports multiple languages.

Description of Granite3.1 Moe 3B Instruct

Granite-3.1-8B-Base extends the context length of its predecessor from 4K to 128K through a progressive training strategy. It employs a decoder-only dense transformer architecture with GQA, RoPE, SwiGLU MLP, RMSNorm, and shared input/output embeddings. Trained on 500B tokens using a three-stage training strategy with diverse data sources, it excels in long-context tasks and serves as a baseline for specialized models.

Parameters & Context Length of Granite3.1 Moe 3B Instruct

Granite3.1 Moe 3B Instruct is a 3b parameter model with a 128k context length, making it suitable for handling long texts while maintaining efficiency. The 3b parameter size places it in the small to mid-scale category, offering fast performance and lower resource demands, ideal for tasks requiring moderate complexity. Its 128k context length falls into the very long context range, enabling advanced processing of extended documents but demanding significant computational resources. This balance allows it to tackle complex tasks without the overhead of larger models.

• Parameter Size: 3b
• Context Length: 128k

Possible Intended Uses of Granite3.1 Moe 3B Instruct

Granite3.1 Moe 3B Instruct is a multilingual model capable of handling diverse tasks, with possible applications in text summarization, text classification and extraction, and question-answering that leverages its 128k context length. Its 3b parameter size allows for possible use cases such as generating concise summaries of lengthy documents, extracting key information from structured or unstructured text, and answering complex queries that require understanding extended contexts. The model’s multilingual support for languages like Chinese, Spanish, and German opens possible opportunities for creating specialized models tailored to specific scenarios, such as regional content analysis or cross-lingual tasks. However, these possible uses should be thoroughly tested and validated before deployment to ensure alignment with specific requirements.

• text summarization
• text classification and extraction
• question-answering with long-context support
• creation of specialized models for specific application scenarios

Possible Applications of Granite3.1 Moe 3B Instruct

Granite3.1 Moe 3B Instruct is a multilingual model with possible applications in text summarization, where its 128k context length could help condense lengthy documents. It might also support possible uses in text classification and extraction, leveraging its ability to process extended content. Possible scenarios include question-answering tasks requiring long-context understanding, as well as the development of possible specialized models for niche tasks. These possible applications should be thoroughly evaluated to ensure they meet specific needs. Each application must be thoroughly evaluated and tested before use.

• text summarization
• text classification and extraction
• question-answering with long-context support
• creation of specialized models for specific application scenarios

Quantized Versions & Hardware Requirements of Granite3.1 Moe 3B Instruct

Granite3.1 Moe 3B Instruct with the q4 quantization requires a GPU with at least 12GB VRAM for optimal performance, balancing precision and efficiency. This version is suitable for systems with 8GB–16GB VRAM and at least 32GB RAM. Possible use cases on such hardware include text processing tasks, but compatibility should be verified.

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

Conclusion

Granite3.1 Moe 3B Instruct is a 3b parameter model with a 128k context length, designed for instruction-following tasks and multilingual support across 14 languages, operating under the Apache License 2.0. It offers possible applications in text processing, classification, and specialized model development, though these require thorough evaluation before deployment.

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