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  ---
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- library_name: transformers
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- tags: []
 
 
 
 
 
 
 
 
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  ---
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- # Model Card for Model ID
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- <!-- Provide a quick summary of what the model is/does. -->
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- ## Model Details
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- ### Model Description
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- <!-- Provide a longer summary of what this model is. -->
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- This is the model card of a 🤗 transformers model that has been pushed on the Hub. This model card has been automatically generated.
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- - **Developed by:** [More Information Needed]
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- - **Funded by [optional]:** [More Information Needed]
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- - **Shared by [optional]:** [More Information Needed]
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- - **Model type:** [More Information Needed]
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- - **Language(s) (NLP):** [More Information Needed]
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- - **License:** [More Information Needed]
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- - **Finetuned from model [optional]:** [More Information Needed]
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- ### Model Sources [optional]
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- <!-- Provide the basic links for the model. -->
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- - **Repository:** [More Information Needed]
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- - **Paper [optional]:** [More Information Needed]
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- - **Demo [optional]:** [More Information Needed]
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- ## Uses
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- <!-- Address questions around how the model is intended to be used, including the foreseeable users of the model and those affected by the model. -->
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- ### Direct Use
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- <!-- This section is for the model use without fine-tuning or plugging into a larger ecosystem/app. -->
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- [More Information Needed]
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- ### Downstream Use [optional]
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- <!-- This section is for the model use when fine-tuned for a task, or when plugged into a larger ecosystem/app -->
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- [More Information Needed]
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- ### Out-of-Scope Use
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- <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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- [More Information Needed]
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- ## Bias, Risks, and Limitations
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- <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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- [More Information Needed]
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- ### Recommendations
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- <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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- Users (both direct and downstream) should be made aware of the risks, biases and limitations of the model. More information needed for further recommendations.
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- ## How to Get Started with the Model
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- Use the code below to get started with the model.
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- [More Information Needed]
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- ## Training Details
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- ### Training Data
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- <!-- This should link to a Dataset Card, perhaps with a short stub of information on what the training data is all about as well as documentation related to data pre-processing or additional filtering. -->
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- [More Information Needed]
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- ### Training Procedure
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- <!-- This relates heavily to the Technical Specifications. Content here should link to that section when it is relevant to the training procedure. -->
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- #### Preprocessing [optional]
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- [More Information Needed]
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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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- ### Results
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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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- ### 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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+ 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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+
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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