AmberYifan/capsdnum-marin-8b-base-code_cap_b1000_s0
AmberYifan/capsdnum-marin-8b-base-code_cap_b1000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was specifically fine-tuned on the capsd_marin-8b-base-n80000-opc__mix_code_cap_b1000_s0 dataset, suggesting an optimization for code-related tasks. It operates with an 8192 token context length, making it suitable for processing moderately long sequences of text or code.
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Overview
AmberYifan/capsdnum-marin-8b-base-code_cap_b1000_s0 is an 8 billion parameter language model, fine-tuned from the marin-community/marin-8b-base architecture. This model has been specialized through fine-tuning on the capsd_marin-8b-base-n80000-opc__mix_code_cap_b1000_s0 dataset.
Training Details
The model underwent a single epoch of fine-tuning with a learning rate of 1e-05. It utilized a cosine learning rate scheduler with 0.03 warmup steps. The training was distributed across 4 devices with a total_train_batch_size of 64 and an AdamW_Torch optimizer.
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 Frameworks: Developed using 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 dataset with "code" in its name, this model is likely optimized for:
- Code generation and completion
- Code understanding and analysis
- Assisting with programming tasks