ucla-cmllab_-_tinyllama-sft-vicuna-processed-indicator-0.6-awq

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tinyllama-sft-vicuna-processed-indicator-0.6 - AWQ

• Model creator: https://huggingface.co/ucla-cmllab/
• Original model: https://huggingface.co/ucla-cmllab/tinyllama-sft-vicuna-processed-indicator-0.6/

Original model description:

license: apache-2.0 base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T tags:

• alignment-handbook
• trl
• sft
• generated_from_trainer
• trl
• sft
• generated_from_trainer datasets:
• yihanwang617/vicuna_clean_processed_indicator_0.6 model-index:
• name: tinyllama-sft-vicuna-processed-indicator-0.6-full results: []

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tinyllama-sft-vicuna-processed-indicator-0.6-full

This model is a fine-tuned version of TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T on the yihanwang617/vicuna_clean_processed_indicator_0.6 dataset. It achieves the following results on the evaluation set:

• Loss: 0.8936

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: 2e-05
• train_batch_size: 16
• eval_batch_size: 8
• seed: 42
• distributed_type: multi-GPU
• num_devices: 4
• gradient_accumulation_steps: 2
• total_train_batch_size: 128
• total_eval_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: cosine
• lr_scheduler_warmup_ratio: 0.1
• num_epochs: 1

Training results

Framework versions

• Transformers 4.40.1
• Pytorch 2.3.0+cu121
• Datasets 2.19.0
• Tokenizers 0.19.1