--- license: llama2 base_model: lmsys/vicuna-7b-v1.5 tags: - generated_from_trainer model-index: - name: vanilla-model-spr-packing results: [] --- [Built with Axolotl](https://github.com/OpenAccess-AI-Collective/axolotl) # vanilla-model-spr-packing This model is a fine-tuned version of [lmsys/vicuna-7b-v1.5](https://huggingface.co/lmsys/vicuna-7b-v1.5) on the None dataset. It achieves the following results on the evaluation set: - Loss: 0.5932 ## Model description The goal of this model is to compress text using SPR (Sparse Priming Representation), focused on Portuguese language. Prompt template: A chat between a curious user and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the user's questions. USER: [COMPRESSION - Sua tarefa é comprimir o texto] {bigtext} [/COMPRESSION] ASSISTANT: ## Training procedure ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 0.0003 - train_batch_size: 1 - eval_batch_size: 1 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 4 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: cosine - lr_scheduler_warmup_steps: 10 - num_epochs: 3 ### Training results | Training Loss | Epoch | Step | Validation Loss | |:-------------:|:-----:|:----:|:---------------:| | 0.8211 | 0.0 | 1 | 0.7681 | | 0.4915 | 0.15 | 141 | 0.5718 | | 0.6263 | 0.3 | 282 | 0.5656 | | 0.5618 | 0.45 | 423 | 0.5563 | | 0.5024 | 0.6 | 564 | 0.5509 | | 0.5353 | 0.75 | 705 | 0.5445 | | 0.6207 | 0.9 | 846 | 0.5429 | | 0.3893 | 1.04 | 987 | 0.5532 | | 0.2821 | 1.19 | 1128 | 0.5521 | | 0.4027 | 1.34 | 1269 | 0.5508 | | 0.3965 | 1.5 | 1410 | 0.5480 | | 0.3884 | 1.65 | 1551 | 0.5449 | | 0.3307 | 1.8 | 1692 | 0.5409 | | 0.34 | 1.95 | 1833 | 0.5409 | | 0.2975 | 2.09 | 1974 | 0.5859 | | 0.2742 | 2.24 | 2115 | 0.5910 | | 0.2256 | 2.39 | 2256 | 0.5922 | | 0.294 | 2.54 | 2397 | 0.5901 | | 0.2246 | 2.69 | 2538 | 0.5944 | | 0.2038 | 2.84 | 2679 | 0.5932 | ### Framework versions - Transformers 4.35.2 - Pytorch 2.0.1+cu117 - Datasets 2.15.0 - Tokenizers 0.15.0