Granite3.1 Dense 2B Instruct - Details

Granite3.1 Dense 2B Instruct is a large language model developed by IBM Granite, a company known for its advanced AI research. This model features 2 billion parameters, making it suitable for a wide range of natural language processing tasks. It is trained on over 12 trillion tokens to enhance its performance and versatility. The model is released under the Apache License 2.0, allowing for flexible use and modification in both research and commercial applications. Designed as an instruct model, it excels in understanding and responding to user instructions with precision.

Granite-3.1-2B-Base extends the context length of its predecessor from 4K to 128K through a progressive training strategy that incrementally increases the supported context length while adjusting RoPE theta to adapt the model. This long-context pre-training stage utilized approximately 500B tokens. The model employs a decoder-only dense transformer architecture with GQA (Grouped Query Attention), RoPE (Rotary Position Embedding), an MLP with SwiGLU, RMSNorm, and shared input/output embeddings. Trained on a mix of open-source and proprietary data across three stages, the final stage incorporated synthetic long-context data to enhance performance. Developed by IBM's Granite Team, it is available via Hugging Face and was trained on IBM's Blue Vela supercomputing cluster using NVIDIA H100 GPUs.

Granite-3.1-2B-Base has 2b parameters, placing it in the small model category, which ensures resource efficiency and fast performance for tasks requiring moderate complexity. Its 128k context length falls into the very long context range, enabling it to process extensive texts but demanding significant computational resources. This combination allows the model to balance efficiency with the ability to handle complex, lengthy inputs, making it suitable for applications where both speed and extended context are critical.
- Parameter Size: 2b
- Context Length: 128k

Granite3.1 Dense 2B Instruct is a versatile model with possible applications in tasks like summarization, text classification, and extraction, where its 2b parameter size and 128k context length could enable efficient handling of complex or lengthy inputs. Its multilingual capabilities in languages such as Chinese, Spanish, and Japanese suggest possible uses in cross-lingual tasks or scenarios requiring support for diverse linguistic contexts. The model’s design may also allow possible exploration in question-answering systems or specialized models tailored for specific application scenarios, though these possible uses would require further testing to validate effectiveness. The 128k context length could be particularly useful for long-context tasks, but the model’s performance in such areas remains to be thoroughly investigated.
- summarization
- text classification
- extraction
- question-answering
- long-context tasks
- specialized models for specific application scenarios

Granite3.1 Dense 2B Instruct has possible applications in areas like summarization, where its 2b parameter size and 128k context length could enable possible handling of extended documents or multi-turn conversations. Its multilingual support for languages such as Chinese, Spanish, and Japanese suggests possible uses in cross-lingual tasks or scenarios requiring diverse language processing. The model’s design may also allow possible exploration in question-answering systems or specialized models tailored for specific application scenarios, though these possible uses would require further testing to validate effectiveness. The 128k context length could be particularly useful for long-context tasks, but the model’s performance in such areas remains to be thoroughly investigated.
- summarization
- text classification
- extraction
- question-answering
- long-context tasks
- specialized models for specific application scenarios

Granite3.1 Dense 2B Instruct’s medium q4 version requires a GPU with at least 8GB–16GB VRAM to balance precision and performance, making it suitable for systems with moderate hardware capabilities. This quantization reduces memory usage while maintaining reasonable accuracy, allowing possible deployment on consumer-grade GPUs. The model’s 2b parameter size ensures efficiency, but users should verify their hardware meets these requirements for smooth operation.
fp16, q2, q3, q4, q5, q6, q8

Granite3.1 Dense 2B Instruct is a 2b-parameter model with a 128k context length, designed for long-context tasks using a dense transformer architecture with GQA and RoPE. Developed by IBM and available on Hugging Face, it was trained on 500B tokens for enhanced performance.

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