mlfoundations-dev/seed_math_mathcoder
The mlfoundations-dev/seed_math_mathcoder is an 8 billion parameter language model, fine-tuned from Meta-Llama-3.1-8B, specifically optimized for mathematical and coding tasks. Developed by mlfoundations-dev, this model leverages its Llama 3.1 base to excel in reasoning and problem-solving within technical domains. It features a 32768 token context length, making it suitable for complex computational and programming challenges.
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Overview
The mlfoundations-dev/seed_math_mathcoder is an 8 billion parameter language model, fine-tuned from the robust meta-llama/Meta-Llama-3.1-8B architecture. This model has been specifically trained on the mlfoundations-dev/seed_math_mathcoder dataset, indicating a specialization in mathematical and coding problem-solving.
Key Characteristics
- Base Model: Fine-tuned from Meta-Llama-3.1-8B.
- Parameter Count: 8 billion parameters.
- Context Length: Supports a substantial context length of 32768 tokens, enabling it to handle extensive code snippets or complex mathematical problems.
- Training Objective: Optimized for tasks related to mathematics and coding, as suggested by its training dataset.
Training Details
The model was trained with a learning rate of 5e-06 over 3 epochs, utilizing a total batch size of 512 across 8 GPUs. The training achieved a final validation loss of 0.2147, indicating effective learning on its specialized dataset.
Intended Use Cases
Given its fine-tuning on a math and coding-centric dataset, this model is well-suited for applications requiring:
- Mathematical reasoning and problem-solving.
- Code generation, completion, and analysis.
- Technical question answering in STEM fields.