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README.md
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#### Training Hyperparameters
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- **Training regime:** [More Information Needed] <!--fp32, fp16 mixed precision, bf16 mixed precision, bf16 non-mixed precision, fp16 non-mixed precision, fp8 mixed precision -->
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#### Speeds, Sizes, Times [optional]
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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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[More Information Needed]
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#### Hardware
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[More Information Needed]
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#### Software
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[More Information Needed]
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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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[More Information Needed]
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**APA:**
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[More Information Needed]
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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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[More Information Needed]
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## Model Card Authors [optional]
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[More Information Needed]
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## Model Card Contact
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[More Information Needed]
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---
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base_model: microsoft/Phi-3.5-mini-instruct
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library_name: peft
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license: mit
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tags:
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- trl
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- sft
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- generated_from_trainer
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model-index:
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- name: guru1984
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results: []
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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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# guru1984
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This model is a fine-tuned version of [microsoft/Phi-3.5-mini-instruct](https://huggingface.co/microsoft/Phi-3.5-mini-instruct) on an unknown dataset.
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It achieves the following results on the evaluation set:
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- Loss: 1.9331
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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: 0.0005
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- train_batch_size: 8
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- eval_batch_size: 8
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- seed: 42
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
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- lr_scheduler_type: linear
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- lr_scheduler_warmup_ratio: 0.1
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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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| 4.0802 | 0.0786 | 50 | 3.3923 |
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| 2.6833 | 0.1572 | 100 | 2.3272 |
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| 2.1534 | 0.2358 | 150 | 2.1625 |
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| 2.0476 | 0.3145 | 200 | 2.1005 |
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| 1.9929 | 0.3931 | 250 | 2.0717 |
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| 1.974 | 0.4717 | 300 | 2.0509 |
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| 1.9854 | 0.5503 | 350 | 2.0435 |
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| 2.0109 | 0.6289 | 400 | 2.0358 |
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| 1.9939 | 0.7075 | 450 | 2.0285 |
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| 1.9672 | 0.7862 | 500 | 2.0150 |
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| 1.9565 | 0.8648 | 550 | 2.0115 |
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| 1.9706 | 0.9434 | 600 | 2.0062 |
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| 1.9352 | 1.0220 | 650 | 1.9949 |
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| 1.8647 | 1.1006 | 700 | 1.9930 |
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| 1.9109 | 1.1792 | 750 | 1.9840 |
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| 1.885 | 1.2579 | 800 | 1.9806 |
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| 1.8864 | 1.3365 | 850 | 1.9878 |
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| 1.8931 | 1.4151 | 900 | 1.9825 |
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| 1.8599 | 1.4937 | 950 | 1.9755 |
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| 1.9019 | 1.5723 | 1000 | 1.9695 |
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| 1.9081 | 1.6509 | 1050 | 1.9599 |
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| 1.8736 | 1.7296 | 1100 | 1.9583 |
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| 1.8939 | 1.8082 | 1150 | 1.9567 |
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| 1.8867 | 1.8868 | 1200 | 1.9534 |
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| 1.8875 | 1.9654 | 1250 | 1.9479 |
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| 1.8677 | 2.0440 | 1300 | 1.9498 |
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| 1.8152 | 2.1226 | 1350 | 1.9527 |
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| 1.8573 | 2.2013 | 1400 | 1.9523 |
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| 1.8433 | 2.2799 | 1450 | 1.9441 |
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| 1.828 | 2.3585 | 1500 | 1.9455 |
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| 1.8298 | 2.4371 | 1550 | 1.9423 |
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| 1.8258 | 2.5157 | 1600 | 1.9376 |
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| 1.8314 | 2.5943 | 1650 | 1.9373 |
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| 1.8436 | 2.6730 | 1700 | 1.9394 |
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| 1.8253 | 2.7516 | 1750 | 1.9373 |
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| 1.8055 | 2.8302 | 1800 | 1.9365 |
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| 1.8207 | 2.9088 | 1850 | 1.9342 |
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| 1.8514 | 2.9874 | 1900 | 1.9331 |
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### Framework versions
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- PEFT 0.12.0
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- Transformers 4.44.0
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- Pytorch 2.4.0
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- Datasets 2.21.0
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- Tokenizers 0.19.1
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