--- license: apache-2.0 library_name: peft tags: - generated_from_trainer base_model: TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T model-index: - name: airoboros-lora-out results: [] pipeline_tag: text-generation --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) # airoboros-lora-out This model is a fine-tuned version of [TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T](https://huggingface.co/TinyLlama/TinyLlama-1.1B-intermediate-step-1195k-token-2.5T) on the `jondurbin/airoboros-3.1` dataset. It achieves the following results on the evaluation set: - Loss: 0.7230 ## Model description More information needed ## Intended uses & limitations More information needed ## Training and evaluation data https://wandb.ai/wing-lian/airoboros-tinyllama ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0002 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - distributed_type: multi-GPU - num_devices: 2 - total_train_batch_size: 16 - total_eval_batch_size: 16 - optimizer: Adam with betas=(0.999,0.95) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 100 - num_epochs: 1 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.9777 | 0.0 | 1 | 1.0628 | | 0.6566 | 0.5 | 379 | 0.7230 | ### Framework versions - Transformers 4.36.2 - Pytorch 2.0.1+cu117 - Datasets 2.16.0 - Tokenizers 0.15.0 ## Training procedure The following `bitsandbytes` quantization config was used during training: - quant_method: bitsandbytes - 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 ### Framework versions - PEFT 0.6.0