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--- |
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license: llama2 |
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base_model: lmsys/vicuna-7b-v1.5 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: vanilla-model-spr-packing |
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results: [] |
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language: |
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- pt |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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[<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>](https://github.com/OpenAccess-AI-Collective/axolotl) |
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# vanilla-model-spr-packing |
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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. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.5932 |
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## Model description |
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The goal of this model is to compress text using SPR (Sparse Priming Representation), focused on Portuguese language. |
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Prompt template: |
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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: |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 0.0003 |
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- train_batch_size: 1 |
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- eval_batch_size: 1 |
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- seed: 42 |
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- gradient_accumulation_steps: 4 |
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- total_train_batch_size: 4 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: cosine |
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- lr_scheduler_warmup_steps: 10 |
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- num_epochs: 3 |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | |
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|:-------------:|:-----:|:----:|:---------------:| |
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| 0.8211 | 0.0 | 1 | 0.7681 | |
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| 0.4915 | 0.15 | 141 | 0.5718 | |
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| 0.6263 | 0.3 | 282 | 0.5656 | |
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| 0.5618 | 0.45 | 423 | 0.5563 | |
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| 0.5024 | 0.6 | 564 | 0.5509 | |
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| 0.5353 | 0.75 | 705 | 0.5445 | |
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| 0.6207 | 0.9 | 846 | 0.5429 | |
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| 0.3893 | 1.04 | 987 | 0.5532 | |
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| 0.2821 | 1.19 | 1128 | 0.5521 | |
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| 0.4027 | 1.34 | 1269 | 0.5508 | |
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| 0.3965 | 1.5 | 1410 | 0.5480 | |
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| 0.3884 | 1.65 | 1551 | 0.5449 | |
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| 0.3307 | 1.8 | 1692 | 0.5409 | |
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| 0.34 | 1.95 | 1833 | 0.5409 | |
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| 0.2975 | 2.09 | 1974 | 0.5859 | |
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| 0.2742 | 2.24 | 2115 | 0.5910 | |
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| 0.2256 | 2.39 | 2256 | 0.5922 | |
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| 0.294 | 2.54 | 2397 | 0.5901 | |
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| 0.2246 | 2.69 | 2538 | 0.5944 | |
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| 0.2038 | 2.84 | 2679 | 0.5932 | |
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### Framework versions |
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- Transformers 4.35.2 |
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- Pytorch 2.0.1+cu117 |
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- Datasets 2.15.0 |
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- Tokenizers 0.15.0 |
