AdaDecode
Collection
[ICML 2025] AdaDecode: Accelerating LLM Decoding with Adaptive Layer Parallelism.
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18 items
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Updated
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3
CodeLlama-13b-Instruct-sft-5e-3-epoch-100-human-eval-final
This model is a fine-tuned version of meta-llama/CodeLlama-13b-Instruct-hf on the meng-lab/CodeLlama-13B-Instruct-humaneval dataset.
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 0.005
- train_batch_size: 1
- eval_batch_size: 2
- seed: 42
- distributed_type: multi-GPU
- num_devices: 8
- gradient_accumulation_steps: 16
- total_train_batch_size: 128
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: cosine
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 100
Training results
Framework versions
- Transformers 4.43.2
- Pytorch 2.1.2
- Datasets 3.0.1
- Tokenizers 0.19.1
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Model tree for meng-lab/AdaDecode-CodeLlama-13B-Instruct-HumanEval
Base model
meta-llama/CodeLlama-13b-Instruct-hf