Exaone Deep 2.4B - Details

Exaone Deep 2.4B is a large language model developed by LG AI Research, featuring 2.4 billion parameters. It operates under the Exaone Ai Model License Agreement 11 - Nc (EXAONE-AIMLA-11-NC), emphasizing its specialized capabilities in reasoning tasks such as math and coding, with scalability up to 32B parameters.

Exaone Deep 2.4B is a large language model developed by LG AI Research with 2.14 billion parameters (excluding embeddings) designed for advanced reasoning tasks. It features 30 layers, GQA attention heads with 32 Q-heads and 8 KV-heads, a vocab size of 102,400, and a context length of 32,768 tokens. The model incorporates tie word embeddings to enhance efficiency and is optimized for complex reasoning steps. It excels in tasks such as math problem solving, coding, and general reasoning, making it a powerful tool for applications requiring deep analytical capabilities.

Exaone Deep 2.4B features 2.4 billion parameters, placing it in the mid-scale category of open-source LLMs, offering a balance between performance and resource efficiency for moderate complexity tasks. Its 32,768-token context length falls into the long-context range, enabling it to process extended texts effectively but requiring significant computational resources. This combination makes it well-suited for tasks demanding both depth of understanding and handling of lengthy input sequences.

• Parameter Size: 2.4B
• Context Length: 32K

Exaone Deep 2.4B is a large language model designed for tasks requiring advanced reasoning, with possible applications in math problem solving, coding tasks, and reasoning tasks. Its architecture supports complex analytical steps, making it a candidate for scenarios where possible uses could include generating solutions to mathematical problems, assisting with code development, or tackling multi-step logical challenges. However, these possible uses require thorough investigation to ensure alignment with specific requirements and constraints. The model’s design emphasizes efficiency and scalability, which could enable possible applications in environments where resource allocation and task complexity need careful balancing. While the model’s capabilities suggest possible value in these areas, further testing and adaptation would be necessary to confirm practical effectiveness.

• math problem solving
• coding tasks
• reasoning tasks

Exaone Deep 2.4B is a large language model with possible applications in areas requiring advanced reasoning, such as math problem solving, coding tasks, and reasoning tasks. Its possible uses could include assisting with complex mathematical derivations, generating code for software development, or tackling multi-step logical challenges. Possible applications might also extend to educational tools for problem-solving practice or research support for analytical tasks. However, these possible uses require thorough evaluation to ensure they align with specific needs and constraints. Each possible application must be carefully tested and validated before deployment to confirm effectiveness and reliability.

• math problem solving
• coding tasks
• reasoning tasks

Exaone Deep 2.4B’s medium q4 version, a balance between precision and performance, may require a GPU with at least 12GB VRAM and 8–16GB VRAM for efficient operation, along with 32GB system memory. These requirements are possible and depend on the specific workload, but users should verify their hardware compatibility. Exaone Deep 2.4B supports multiple quantized versions, including fp16, q4, and q8, each with varying trade-offs between accuracy and resource usage.

• fp16
• q4
• q8

Exaone Deep 2.4B is a large language model developed by LG AI Research with 2.4 billion parameters, optimized for reasoning tasks like math problem solving and coding. It supports multiple quantized versions and is designed for applications requiring advanced analytical capabilities.

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