--- base_model: google/gemma-7b datasets: - llama-duo/synth_summarize_dataset_dedup library_name: peft license: gemma tags: - alignment-handbook - trl - sft - generated_from_trainer model-index: - name: gemma7b-gpt4o_1k_summarize-kasalora-auxloss results: [] --- # gemma7b-gpt4o_1k_summarize-kasalora-auxloss This model is a fine-tuned version of [google/gemma-7b](https://huggingface.co/google/gemma-7b) on the llama-duo/synth_summarize_dataset_dedup dataset. It achieves the following results on the evaluation set: - Loss: 34.3196 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data More information needed ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: QuantizationMethod.BITS_AND_BYTES - _load_in_8bit: True - _load_in_4bit: False - llm_int8_threshold: 6.0 - llm_int8_skip_modules: None - llm_int8_enable_fp32_cpu_offload: False - llm_int8_has_fp16_weight: False - bnb_4bit_quant_type: fp4 - bnb_4bit_use_double_quant: False - bnb_4bit_compute_dtype: float32 - bnb_4bit_quant_storage: uint8 - load_in_4bit: False - load_in_8bit: True ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 2 - seed: 42 - distributed_type: multi-GPU - num_devices: 4 - gradient_accumulation_steps: 2 - total_train_batch_size: 8 - total_eval_batch_size: 8 - 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 | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:------:|:----:|:---------------:| | 32.8196 | 0.9997 | 1567 | 34.3196 | ### Framework versions - PEFT 0.6.3.dev0 - Transformers 4.44.2 - Pytorch 2.4.1+cu121 - Datasets 3.0.0 - Tokenizers 0.19.1