AmberYifan/capsd-less-ultra-humaneval-opc-marin-8b-base-code_less_b4000_s0
The AmberYifan/capsd-less-ultra-humaneval-opc-marin-8b-base-code_less_b4000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was trained on the capsd_marin-8b-base-n80000-opc__mix_code_less_b4000_s0 dataset, suggesting an optimization for code-related tasks. It features a context length of 8192 tokens and is designed for applications requiring robust code understanding and generation capabilities.
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Model Overview
This model, AmberYifan/capsd-less-ultra-humaneval-opc-marin-8b-base-code_less_b4000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base model, specifically adapted using the capsd_marin-8b-base-n80000-opc__mix_code_less_b4000_s0 dataset. The training process involved a single epoch with a learning rate of 1e-05 and a total batch size of 64, utilizing a cosine learning rate scheduler.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Context Length: Supports an 8192-token context window.
- Training Data: Specialized dataset
capsd_marin-8b-base-n80000-opc__mix_code_less_b4000_s0, indicating a focus on code-related tasks.
Training Details
The model was trained using a distributed setup across 4 GPUs, with a learning rate of 1e-05 and AdamW optimizer. The training procedure included 8 gradient accumulation steps and a cosine learning rate scheduler with 0.03 warmup steps. The framework versions used were Transformers 5.7.0, Pytorch 2.13.0+cu130, Datasets 4.0.0, and Tokenizers 0.22.2.
Potential Use Cases
Given its fine-tuning on a code-centric dataset, this model is likely suitable for:
- Code generation and completion.
- Code understanding and analysis.
- Assisting with programming tasks.