Gemma2 LoRA Adapters
Collection
Gemma2 LoRA adapters fine-tuned using SFT in TRL on diverse tasks such as coding, SQL, Japanese to English, and function calling • 6 items • Updated
gemma-2-2b-it-lora-magicoder
This model is a fine-tuned version of google/gemma-2-2b-it on the alvarobartt/Magicoder-OAI 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: 0.0002
- train_batch_size: 4
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- 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: 3
Training results
Framework versions
- PEFT 0.12.0
- Transformers 4.44.2
- Pytorch 2.3.0+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1
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from peft import PeftModel from transformers import AutoModelForCausalLM base_model = AutoModelForCausalLM.from_pretrained("google/gemma-2-2b-it") model = PeftModel.from_pretrained(base_model, "google-cloud-partnership/gemma-2-2b-it-lora-magicoder")