--- base_model: NousResearch/Llama-2-7b-hf tags: - trl - sft - generated_from_trainer model-index: - name: llama2-docsum-adapter results: [] library_name: peft --- # llama2-docsum-adapter This model is a fine-tuned version of [NousResearch/Llama-2-7b-hf](https://huggingface.co/NousResearch/Llama-2-7b-hf) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.1430 ## 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: bitsandbytes - _load_in_8bit: False - _load_in_4bit: True - 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: nf4 - bnb_4bit_use_double_quant: True - bnb_4bit_compute_dtype: bfloat16 - bnb_4bit_quant_storage: uint8 - load_in_4bit: True - load_in_8bit: False ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0001 - train_batch_size: 4 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 16 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_ratio: 0.05 - num_epochs: 35 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.0557 | 5.0 | 7 | 0.1344 | | 0.016 | 9.33 | 14 | 0.1488 | | 0.0266 | 14.0 | 21 | 0.1437 | | 0.0291 | 18.67 | 28 | 0.1426 | | 0.0112 | 23.0 | 35 | 0.1430 | ### Framework versions - PEFT 0.4.0 - Transformers 4.39.2 - Pytorch 2.2.1+cu121 - Datasets 2.18.0 - Tokenizers 0.15.2