Kamyar-zeinalipour
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README.md
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###
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<!-- This section provides information about throughput, start/end time, checkpoint size if relevant, etc. -->
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[More Information Needed]
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## Evaluation
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<!-- This section describes the evaluation protocols and provides the results. -->
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### Testing Data, Factors & Metrics
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#### Testing Data
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<!-- This should link to a Dataset Card if possible. -->
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[More Information Needed]
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#### Factors
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<!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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[More Information Needed]
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#### Metrics
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<!-- These are the evaluation metrics being used, ideally with a description of why. -->
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[More Information Needed]
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### Results
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[More Information Needed]
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#### Summary
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## Model Examination [optional]
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<!-- Relevant interpretability work for the model goes here -->
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[More Information Needed]
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## Environmental Impact
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<!-- Total emissions (in grams of CO2eq) and additional considerations, such as electricity usage, go here. Edit the suggested text below accordingly -->
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Carbon emissions can be estimated using the [Machine Learning Impact calculator](https://mlco2.github.io/impact#compute) presented in [Lacoste et al. (2019)](https://arxiv.org/abs/1910.09700).
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- **Hardware Type:** [More Information Needed]
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- **Hours used:** [More Information Needed]
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- **Cloud Provider:** [More Information Needed]
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- **Compute Region:** [More Information Needed]
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- **Carbon Emitted:** [More Information Needed]
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## Technical Specifications [optional]
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### Model Architecture and Objective
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[More Information Needed]
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### Compute Infrastructure
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#### Hardware
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#### Software
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## Citation [optional]
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<!-- If there is a paper or blog post introducing the model, the APA and Bibtex information for that should go in this section. -->
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**BibTeX:**
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**APA:**
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## Glossary [optional]
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<!-- If relevant, include terms and calculations in this section that can help readers understand the model or model card. -->
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[More Information Needed]
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## More Information [optional]
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## Model Card Authors [optional]
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## Model Card Contact
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[More Information Needed]
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---
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license: apache-2.0
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base_model: mistralai/Mistral-7B-Instruct-v0.3
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tags:
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- generated_from_trainer
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model-index:
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- name: mistral-7b-peptide
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results: []
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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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# mistral-7b-peptide
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This model is a fine-tuned version of [mistralai/Mistral-7B-Instruct-v0.3](https://huggingface.co/mistralai/Mistral-7B-Instruct-v0.3) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.5836
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## Model description
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More information needed
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## Intended uses & limitations
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More information needed
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## Training and evaluation data
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More information needed
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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: 5e-05
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- train_batch_size: 3
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- eval_batch_size: 3
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- seed: 42
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- distributed_type: multi-GPU
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- num_devices: 4
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- gradient_accumulation_steps: 4
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- total_train_batch_size: 48
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- total_eval_batch_size: 12
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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: 30
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- training_steps: 4000
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### Training results
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| Training Loss | Epoch | Step | Validation Loss |
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| 2.7541 | 0.025 | 100 | 2.6109 |
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| 2.2057 | 0.05 | 200 | 2.1101 |
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| 1.9566 | 0.075 | 300 | 1.8904 |
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| 1.8737 | 0.1 | 400 | 3.6582 |
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| 1.8384 | 0.125 | 500 | 1.6622 |
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| 1.6577 | 0.15 | 600 | 1.6209 |
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| 5.0415 | 0.175 | 700 | 5.0107 |
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| 4.8597 | 0.2 | 800 | 4.8365 |
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| 4.7887 | 0.225 | 900 | 4.7727 |
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| 4.7478 | 0.25 | 1000 | 4.7247 |
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| 4.8828 | 0.275 | 1100 | 4.8448 |
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| 4.5764 | 0.3 | 1200 | 4.4572 |
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| 4.2131 | 0.325 | 1300 | 4.1309 |
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| 3.8945 | 0.35 | 1400 | 3.7905 |
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| 3.42 | 0.375 | 1500 | 3.2011 |
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| 1.6361 | 0.4 | 1600 | 1.5822 |
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| 1.4804 | 0.425 | 1700 | 1.5127 |
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| 1.3574 | 0.45 | 1800 | 1.5037 |
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| 1.2675 | 0.475 | 1900 | 1.4394 |
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| 1.2611 | 0.5 | 2000 | 1.3705 |
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| 1.1509 | 0.525 | 2100 | 1.3520 |
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| 1.0144 | 0.55 | 2200 | 1.3529 |
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| 1.3122 | 0.575 | 2300 | 1.2730 |
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| 1.0257 | 0.6 | 2400 | 1.2805 |
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| 0.7651 | 0.625 | 2500 | 1.3131 |
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| 0.5841 | 0.65 | 2600 | 1.3736 |
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| 0.4848 | 0.675 | 2700 | 1.4138 |
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| 0.6076 | 0.7 | 2800 | 1.3322 |
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| 0.4255 | 0.725 | 2900 | 1.4169 |
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| 0.3276 | 0.75 | 3000 | 1.4631 |
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| 0.6833 | 0.775 | 3100 | 1.2651 |
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| 0.385 | 0.8 | 3200 | 1.3994 |
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| 0.1845 | 0.825 | 3300 | 1.4685 |
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| 0.1408 | 0.85 | 3400 | 1.5640 |
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| 0.1213 | 0.875 | 3500 | 1.5984 |
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| 0.1735 | 0.9 | 3600 | 1.5952 |
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| 0.1161 | 0.925 | 3700 | 1.6201 |
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| 0.1079 | 0.95 | 3800 | 1.6238 |
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| 0.3046 | 0.975 | 3900 | 1.6070 |
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| 0.1477 | 1.0 | 4000 | 1.5836 |
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### Framework versions
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- Transformers 4.44.0.dev0
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- Pytorch 2.1.0+cu121
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- Datasets 2.20.0
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- Tokenizers 0.19.1
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