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  library_name: transformers
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- tags: []
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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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- ## 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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- #### 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 [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: cc-by-nc-4.0
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+ base_model: mlabonne/NeuralMonarch-7B
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+ tags:
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+ - generated_from_trainer
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+ - mistral
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+ - instruct
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+ - finetune
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+ - chatml
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+ - gpt4
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+ - synthetic data
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+ - distillation
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+ model-index:
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+ - name: AlphaMonarch-dora
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+ results: []
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+ datasets:
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+ - argilla/OpenHermes2.5-dpo-binarized-alpha
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+ language:
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+ - en
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  library_name: transformers
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+ pipeline_tag: text-generation
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  ---
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+ ![image/jpeg](https://cdn-uploads.huggingface.co/production/uploads/64fc6d81d75293f417fee1d1/7xlnpalOC4qtu-VABsib4.jpeg)
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+ # AlphaMonarch-dora
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ AlphaMonarch-laser is a DPO fine-tuned of [mlabonne/NeuralMonarch-7B](https://huggingface.co/mlabonne/NeuralMonarch-7B/) using the [argilla/OpenHermes2.5-dpo-binarized-alpha](https://huggingface.co/datasets/argilla/OpenHermes2.5-dpo-binarized-alpha) preference dataset using DoRA...
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## 🏆 Evaluation results
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+ # Nous Benchmark
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+ ### AGIEVAL
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+ | Task | Version | Accuracy | Accuracy StdErr | Normalized Accuracy | Normalized Accuracy StdErr |
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+ |--------------------------------|---------|----------|-----------------|---------------------|-----------------------------|
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+ | agieval_aqua_rat | 0 | 28.35% | 2.83% | 26.38% | 2.77% |
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+ | agieval_logiqa_en | 0 | 38.71% | 1.91% | 38.25% | 1.90% |
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+ | agieval_lsat_ar | 0 | 23.91% | 2.82% | 23.48% | 2.80% |
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+ | agieval_lsat_lr | 0 | 52.55% | 2.21% | 53.73% | 2.21% |
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+ | agieval_lsat_rc | 0 | 66.91% | 2.87% | 66.54% | 2.88% |
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+ | agieval_sat_en | 0 | 78.64% | 2.86% | 78.64% | 2.86% |
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+ | agieval_sat_en_without_passage | 0 | 45.15% | 3.48% | 44.17% | 3.47% |
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+ | agieval_sat_math | 0 | 33.64% | 3.19% | 31.82% | 3.15% |
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+ AVG = 45.976
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+ ### GPT4ALL
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+ | Task | Version | Accuracy | Accuracy StdErr | Normalized Accuracy | Normalized Accuracy StdErr |
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+ |--------------|---------|----------|-----------------|---------------------|-----------------------------|
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+ | arc_challenge| 0 | 65.87% | 1.39% | 67.92% | 1.36% |
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+ | arc_easy | 0 | 86.49% | 0.70% | 80.64% | 0.81% |
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+ | boolq | 1 | 87.16% | 0.59% | - | - |
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+ | hellaswag | 0 | 69.86% | 0.46% | 87.51% | 0.33% |
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+ | openbookqa | 0 | 39.00% | 2.18% | 49.20% | 2.24% |
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+ | piqa | 0 | 83.03% | 0.88% | 84.82% | 0.84% |
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+ | winogrande | 0 | 80.98% | 1.10% | - | - |
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+ AVG = 73.18
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+ ### TRUTHFUL-QA
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+ | Task | Version | MC1 Accuracy | MC1 Accuracy StdErr | MC2 Accuracy | MC2 Accuracy StdErr |
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+ |---------------|---------|--------------|---------------------|--------------|---------------------|
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+ | truthfulqa_mc | 1 | 62.91% | 1.69% | 78.48% | 1.37% |
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+ VG = 70.69
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+ ### Training hyperparameters
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+ The following hyperparameters were used during training:
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+ - learning_rate: 5e-7
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+ - train_batch_size: 2
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+ - eval_batch_size: Not specified
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+ - seed: Not specified
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+ - gradient_accumulation_steps: 8
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+ - total_train_batch_size: Not specified
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+ - optimizer: PagedAdamW with 32-bit precision
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+ - lr_scheduler_type: Cosine
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+ - lr_scheduler_warmup_steps: 100
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+ - training_steps: 1080
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+ ### Framework versions
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+ - Transformers 4.39.0.dev0
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+ - Peft 0.9.1.dev0
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+ - Datasets 2.18.0
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+ - torch 2.2.0
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+ - accelerate 0.27.2