mlfoundations-dev/difficulty_sorting_random_seed_code
The mlfoundations-dev/difficulty_sorting_random_seed_code model is a fine-tuned version of Qwen/Qwen2.5-7B-Instruct. This model is specifically adapted from the Qwen2.5-7B-Instruct architecture, focusing on tasks related to difficulty sorting and random seed code. It is designed for specialized applications within code analysis or generation where these specific functionalities are critical.
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Overview
This model, difficulty_sorting_random_seed_code, is a specialized fine-tuned variant of the Qwen/Qwen2.5-7B-Instruct base model. It has been adapted for specific tasks related to difficulty sorting and random seed code, suggesting an application in areas like code generation, analysis, or educational tools where these concepts are relevant.
Training Details
The model was trained using the following key hyperparameters:
- Learning Rate: 1e-05
- Batch Size: 1 (train), 8 (eval)
- Gradient Accumulation Steps: 6, leading to a total effective training batch size of 96.
- Optimizer: AdamW with default betas and epsilon.
- Scheduler: Cosine learning rate scheduler with a 0.1 warmup ratio.
- Epochs: 3.0
Intended Use
While specific intended uses and limitations require more detailed information, its fine-tuning on a dataset related to "difficulty sorting random seed code" indicates its potential utility in tasks that involve:
- Analyzing or generating code snippets with varying difficulty levels.
- Managing or predicting outcomes based on random seeds in code.
Further details on its performance and specific applications would require additional information from the model developer.