AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b40000_s0
The AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b40000_s0 model is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model has been specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b40000_s0 dataset, indicating an optimization for code-related tasks. With a context length of 8192 tokens, it is designed for applications requiring robust code understanding and generation capabilities.
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Model Overview
This model, AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b40000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, specifically adapted for code-centric applications.
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 training on the
capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b40000_s0dataset, suggesting a focus on code-related tasks.
Training Details
The model underwent a single epoch of training with a learning rate of 1e-05. It utilized a distributed training setup across 4 GPUs, with a total effective batch size of 64 (train_batch_size: 2, gradient_accumulation_steps: 8). The optimizer used was AdamW_Torch with a cosine learning rate scheduler.
Potential Use Cases
Given its fine-tuning on a code-specific dataset, this model is likely suitable for:
- Code generation and completion.
- Code understanding and analysis.
- Assisting with programming tasks where a robust understanding of code structures is beneficial.