AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b12000_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

AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b12000_s0 is an 8 billion parameter language model, fine-tuned from marin-community/marin-8b-base. This model was trained on a specific dataset, capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b12000_s0, suggesting a specialization in code-related tasks. It utilizes a context length of 8192 tokens and was fine-tuned with a learning rate of 1e-05 over one epoch.

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Model Overview

This model, AmberYifan/capsd-opc-dedup-marin-8b-base-code_cap_b12000_s0, is an 8 billion parameter language model. It is a fine-tuned variant of the marin-community/marin-8b-base architecture, specifically adapted using the capsd_marin-8b-base-n80000-opc-dedup80k__mix_code_cap_b12000_s0 dataset.

Training Details

The fine-tuning process involved specific hyperparameters:

  • Learning Rate: 1e-05
  • Batch Sizes: A train_batch_size of 2 and eval_batch_size of 8, leading to a total_train_batch_size of 64 and total_eval_batch_size of 32 across 4 GPUs.
  • Optimizer: ADAMW_TORCH with default betas and epsilon.
  • Scheduler: Cosine learning rate scheduler with 0.03 warmup steps.
  • Epochs: Trained for 1 epoch.

Framework Versions

The training utilized:

  • Transformers 5.7.0
  • Pytorch 2.13.0+cu130
  • Datasets 4.0.0
  • Tokenizers 0.22.2

Potential Use Cases

Given its fine-tuning on a dataset with "code_cap" in its name, this model is likely optimized for:

  • Code generation and completion
  • Code understanding and analysis
  • Assisting with programming tasks

Finetunes
1 models