AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b2000_s0

TEXT GENERATIONPricing:Input $0.37 / Cached $0.074 / Output $0.38Concurrent Unit Cost:1Model Size:8BQuant:FP8Context Size:8kTool Calling:SupportedPublished:Aug 7, 2026License:otherArchitecture:Transformer Featherless Exclusive Cold

The AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b2000_s0 model is an 8 billion parameter language model fine-tuned from marin-community/marin-8b-base. This model was specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b2000_s0 dataset, suggesting an optimization for code-related tasks. It utilizes a context length of 8192 tokens, making it suitable for processing moderately long sequences of text or code.

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Model Overview

This model, AmberYifan/capsd-opc-dedup-marin-8b-base-code_ppl_b2000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture.

Key Characteristics

  • Base Model: Fine-tuned from marin-community/marin-8b-base.
  • Training Dataset: Specifically trained on the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_ppl_b2000_s0 dataset, indicating a focus on code-related data.
  • Parameters: Contains 8 billion parameters.
  • Context Length: Supports a context window of 8192 tokens.

Training Details

The model was trained using the following hyperparameters:

  • Learning Rate: 1e-05
  • Batch Size: train_batch_size of 2, eval_batch_size of 8, with gradient_accumulation_steps of 8, resulting in a total_train_batch_size of 64.
  • Optimizer: ADAMW_TORCH with default betas and epsilon.
  • Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
  • Epochs: Trained for 1 epoch.

Potential Use Cases

Given its fine-tuning on a code-centric dataset, this model is likely intended for applications involving code generation, completion, analysis, or understanding. Further details on specific intended uses and limitations are not provided in the current model card.

Finetunes
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