AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b1000_s0
AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b1000_s0 is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model is specifically adapted using the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b1000_s0 dataset, indicating a specialization in 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_b1000_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-related applications.
Key Characteristics
- Base Model: Fine-tuned from
marin-community/marin-8b-base. - Parameter Count: 8 billion parameters.
- Context Length: Supports a context window of 8192 tokens.
- Training Data: Specialized training on the
capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b1000_s0dataset, suggesting an optimization for code-centric 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 batch size of 64 (achieved with a train_batch_size of 2 and gradient_accumulation_steps of 8). The optimizer used was AdamW with cosine learning rate scheduling and a warmup ratio of 0.03.