mlfoundations-dev/deepmath
The mlfoundations-dev/deepmath model is a 7.6 billion parameter instruction-tuned language model, fine-tuned from Qwen/Qwen2.5-7B-Instruct. It is specifically adapted using the mlfoundations-dev/deepmath dataset, suggesting an optimization for mathematical reasoning and problem-solving tasks. With a substantial context length of 131072 tokens, it is designed for processing extensive mathematical or technical inputs. This model is intended for applications requiring advanced mathematical understanding and precise logical deduction.
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Overview
The mlfoundations-dev/deepmath model is a 7.6 billion parameter language model, fine-tuned from the Qwen/Qwen2.5-7B-Instruct base model. It has been specifically adapted using the mlfoundations-dev/deepmath dataset, indicating a specialization in areas related to deep learning foundations and mathematics.
Key Characteristics
- Base Model: Qwen/Qwen2.5-7B-Instruct
- Parameter Count: 7.6 billion parameters
- Context Length: 131072 tokens, allowing for extensive input processing.
- Training Data: Fine-tuned on the
mlfoundations-dev/deepmathdataset.
Training Details
The model was trained with a learning rate of 8e-05, a total batch size of 512, and utilized 128 devices. The training procedure involved 5 epochs, using the AdamW optimizer with a cosine learning rate scheduler and a warmup ratio of 0.1. This configuration suggests a robust training regimen aimed at enhancing its specialized capabilities.
Intended Use Cases
Given its fine-tuning on a deepmath dataset, this model is likely optimized for tasks involving:
- Mathematical problem-solving and reasoning.
- Understanding and generating technical content related to deep learning and mathematics.
- Applications requiring a deep comprehension of scientific and theoretical concepts.