Training in progress, epoch 2
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- adapter_model.safetensors +1 -1
README.md
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---
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base_model:
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- google/gemma-2b-it
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datasets:
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- generator
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library_name: peft
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- sft
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- generated_from_trainer
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model-index:
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- name: gemma-
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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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# gemma-
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This model is a fine-tuned version of [google/gemma-2-
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## Model description
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- eval_batch_size: 8
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices:
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- gradient_accumulation_steps: 2
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- total_train_batch_size:
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- total_eval_batch_size:
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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---
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base_model: google/gemma-2-9b-it
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datasets:
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- generator
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library_name: peft
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- sft
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- generated_from_trainer
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model-index:
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- name: gemma-2-9b-it-lora-magicoder
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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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# gemma-2-9b-it-lora-magicoder
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This model is a fine-tuned version of [google/gemma-2-9b-it](https://huggingface.co/google/gemma-2-9b-it) on the alvarobartt/Magicoder-OAI dataset.
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## Model description
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- eval_batch_size: 8
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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: 2
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- total_train_batch_size: 32
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- total_eval_batch_size: 32
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: cosine
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- lr_scheduler_warmup_ratio: 0.1
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adapter_model.safetensors
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