Fucheng Jia
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---
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library_name: transformers
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license: other
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base_model: /data/AIR_HF_CACHE/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/8c22764a7e3675c50d4c7c9a4edb474456022b16
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tags:
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- llama-factory
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- full
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- generated_from_trainer
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model-index:
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- name: droidv2_data_verified_merge_no_data_aug_v2_epoch1_llama3.1-8b
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results: []
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---
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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should probably proofread and complete it, then remove this comment. -->
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# droidv2_data_verified_merge_no_data_aug_v2_epoch1_llama3.1-8b
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This model is a fine-tuned version of [/data/AIR_HF_CACHE/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/8c22764a7e3675c50d4c7c9a4edb474456022b16](https://huggingface.co//data/AIR_HF_CACHE/hub/models--meta-llama--Meta-Llama-3.1-8B-Instruct/snapshots/8c22764a7e3675c50d4c7c9a4edb474456022b16) on the autodroid dataset.
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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## Training procedure
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### Training hyperparameters
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The following hyperparameters were used during training:
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- learning_rate: 1e-05
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- train_batch_size: 1
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- eval_batch_size: 1
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 8
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- total_train_batch_size: 32
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- total_eval_batch_size: 4
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- optimizer: Use adamw_torch with betas=(0.9,0.95) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.01
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- num_epochs: 1.0
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### Training results
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### Framework versions
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- Transformers 4.46.1
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- Pytorch 2.4.0+cu118
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- Datasets 3.0.0
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- Tokenizers 0.20.3
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config.json
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{
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"_name_or_path": "
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"architectures": [
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"LlamaForCausalLM"
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],
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{
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"_name_or_path": "AutoDroid-V2",
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"architectures": [
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"LlamaForCausalLM"
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],
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