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epoch_1/README.md ADDED
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+ ---
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+ base_model: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ library_name: peft
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+ pipeline_tag: text-generation
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+ tags:
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+ - base_model:adapter:./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ - lora
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+ - transformers
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+ ---
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+
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+ # Model Card for Model ID
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+
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+ <!-- Provide a quick summary of what the model is/does. -->
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+
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+
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+
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+ ## Model Details
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+
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+ ### Model Description
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+
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+ <!-- Provide a longer summary of what this model is. -->
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+
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+
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+
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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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+
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+ ### Model Sources [optional]
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+
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+ <!-- Provide the basic links for the model. -->
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+
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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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+
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+ ## Uses
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+
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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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+
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+ ### Direct Use
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Downstream Use [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Out-of-Scope Use
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+
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+ <!-- This section addresses misuse, malicious use, and uses that the model will not work well for. -->
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+
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+ [More Information Needed]
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+
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+ ## Bias, Risks, and Limitations
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+
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+ <!-- This section is meant to convey both technical and sociotechnical limitations. -->
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+
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+ [More Information Needed]
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+
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+ ### Recommendations
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+
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+ <!-- This section is meant to convey recommendations with respect to the bias, risk, and technical limitations. -->
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+
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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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+
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+ ## How to Get Started with the Model
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+
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+ Use the code below to get started with the model.
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+
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+ [More Information Needed]
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+
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+ ## Training Details
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+
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+ ### Training Data
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+
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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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+
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+ [More Information Needed]
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+
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+ ### Training Procedure
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+
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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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+
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+ #### Preprocessing [optional]
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+
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+ [More Information Needed]
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+
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+
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+ #### Training Hyperparameters
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+
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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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+
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+ #### Speeds, Sizes, Times [optional]
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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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+
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+ [More Information Needed]
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+
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+ ## Evaluation
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+
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+ <!-- This section describes the evaluation protocols and provides the results. -->
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+
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+ ### Testing Data, Factors & Metrics
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+
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+ #### Testing Data
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+
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+ <!-- This should link to a Dataset Card if possible. -->
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+
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+ [More Information Needed]
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+
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+ #### Factors
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+
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+ <!-- These are the things the evaluation is disaggregating by, e.g., subpopulations or domains. -->
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+
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+ [More Information Needed]
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+
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+ #### Metrics
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+
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+ <!-- These are the evaluation metrics being used, ideally with a description of why. -->
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+
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+ [More Information Needed]
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+
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+ ### Results
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+
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+ [More Information Needed]
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+
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+ #### Summary
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+
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+
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+
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+ ## Model Examination [optional]
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+
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+ <!-- Relevant interpretability work for the model goes here -->
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+
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+ [More Information Needed]
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+
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+ ## Environmental Impact
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+
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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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+
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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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+
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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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+
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+ ## Technical Specifications [optional]
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+
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+ ### Model Architecture and Objective
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+
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+ [More Information Needed]
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+
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+ ### Compute Infrastructure
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+
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+ [More Information Needed]
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+
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+ #### Hardware
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+
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+ [More Information Needed]
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+
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+ #### Software
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+
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+ [More Information Needed]
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+
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+ ## Citation [optional]
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+
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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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+
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+ **BibTeX:**
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+
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+ [More Information Needed]
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+
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+ **APA:**
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+
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+ [More Information Needed]
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+
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+ ## Glossary [optional]
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+
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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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+
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+ [More Information Needed]
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+
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+ ## More Information [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Authors [optional]
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+
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+ [More Information Needed]
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+
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+ ## Model Card Contact
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+
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+ [More Information Needed]
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+ ### Framework versions
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+
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+ - PEFT 0.18.1
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logs/rexmoe_training_0504_053908.log ADDED
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - ================================================================================
2
+ 2026-04-05 05:39:08 - ReXMoE - INFO - ReXMoE Training Log - 0504_053908
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Log file: ./rexmoe_recovered_50_pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3/logs/rexmoe_training_0504_053908.log
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - ================================================================================
5
+ 2026-04-05 05:39:08 - ReXMoE - INFO - ================================================================================
6
+ 2026-04-05 05:39:08 - ReXMoE - INFO - ReXMoE Recovery Training (Fine-tuning Pruned Model)
7
+ 2026-04-05 05:39:08 - ReXMoE - INFO - ================================================================================
8
+ 2026-04-05 05:39:08 - ReXMoE - INFO - Checkpoint Path: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Dataset Path: ../dataset/alpaca_data_cleaned.json
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Dataset Mode: IF
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Num Samples: 10000
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Num Epochs: 1
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Batch Size: 1
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Learning Rate: 0.0001
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Full LoRA (q,k,v,o): True
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Use Scheduler: True
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Evaluation Steps: 5000
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:39:08 - ReXMoE - INFO - Using device: cuda
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+ 2026-04-05 05:39:08 - ReXMoE - INFO -
24
+ [1/5] Loading pruned model from: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ReXMoE Training Log - 0504_053915
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ReXMoE Recovery Training (Fine-tuning Pruned Model)
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Checkpoint Path: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Dataset Path: ../dataset/alpaca_data_cleaned.json
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Dataset Mode: IF
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Num Samples: 10000
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Full LoRA (q,k,v,o): True
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Use Scheduler: True
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Evaluation Steps: 5000
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:39:15 - ReXMoE - INFO - Using device: cuda
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+ 2026-04-05 05:39:15 - ReXMoE - INFO -
24
+ [1/5] Loading pruned model from: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Log file: ./rexmoe_recovered_50_pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3/logs/rexmoe_training_0504_054731.log
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - ReXMoE Recovery Training (Fine-tuning Pruned Model)
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - ================================================================================
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Checkpoint Path: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Dataset Path: ../dataset/alpaca_data_cleaned.json
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Dataset Mode: IF
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Num Samples: 5000
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Num Epochs: 1
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Batch Size: 1
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Max Seq Length: 512
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Learning Rate: 0.0001
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Save Path: ./rexmoe_recovered_50_pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Full LoRA (q,k,v,o): True
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Use Scheduler: True
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Evaluation Steps: 5000
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Gradient Checkpointing: False
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+ 2026-04-05 05:47:31 - ReXMoE - INFO - Using device: cuda
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+ 2026-04-05 05:47:31 - ReXMoE - INFO -
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+ [1/5] Loading pruned model from: ./pruned_50_0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:54:05 - ReXMoE - INFO - Loading dense model for KD from microsoft/Phi-mini-MoE-instruct
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+ 2026-04-05 05:54:10 - ReXMoE - INFO -
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+ [2/5] Setting up LoRA for fine-tuning...
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+ 2026-04-05 05:54:12 - ReXMoE - INFO -
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+ Attempting to load and remap pretrained LoRA from: ./0304_033137_10_rexmoe_natural_phi_mini_moe_R3
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+ 2026-04-05 05:54:12 - ReXMoE - INFO - ✓ Loaded remapped LoRA weights.
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+ 2026-04-05 05:54:12 - ReXMoE - INFO - - Remapped expert weights: 0
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+ 2026-04-05 05:54:12 - ReXMoE - INFO - - Skipped pruned weights: 0
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+ 2026-04-05 05:54:12 - ReXMoE - INFO - Total parameters: 4,719,726,912
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+ 2026-04-05 05:54:12 - ReXMoE - INFO - Trainable parameters: 76,002,304 (1.6103%)
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+ 2026-04-05 05:54:17 - ReXMoE - INFO -
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+ Prior Evaluation:
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+ 2026-04-05 05:54:17 - ReXMoE - INFO -
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+ Evaluating model with 3 sample prompts...
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+ 2026-04-05 05:54:41 - ReXMoE - INFO -
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+ --- Prompt 1/3 ---
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+ 2026-04-05 05:54:41 - ReXMoE - INFO - Instruction: What is the capital of France?
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+ 2026-04-05 05:54:41 - ReXMoE - INFO - Input: None
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+ 2026-04-05 05:54:41 - ReXMoE - INFO - Generated completion (len 100): capital?
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+
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+ capital??
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+
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+ capital??
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+ capital??? capital?? capital?? capital?? capital?? capital?? capital?? capital?? capital??? capital??? capital????? capital???? capital?????????????? a capital?????????????? capital??? capital???? capital??
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+ 2026-04-05 05:55:05 - ReXMoE - INFO -
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+ --- Prompt 2/3 ---
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+ 2026-04-05 05:55:05 - ReXMoE - INFO - Instruction: High-pressure systems stop air from rising into the colder regions of the atmosphere where water can condense. What will most likely result if a high-pressure system remains in an area for a long period of time?
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+ A. fog
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+ B. rain
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+ C. drought
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+ D. tornado
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+ Answer:
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+ 2026-04-05 05:55:05 - ReXMoE - INFO - Input: None
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+ 2026-04-05 05:55:05 - ReXMoE - INFO - Generated completion (len 100):
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+
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+ The air with high pressure will stay in an area for a long period of time.
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+ A. fog
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+ B. rain
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+ C. drought
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+ D. tornado
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+
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+ Answer:
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+ the air with high pressure can stay in an area for a long period of time
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+ A. fog
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+ B. rain
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+ C. dr
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+ 2026-04-05 05:55:28 - ReXMoE - INFO -
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+ --- Prompt 3/3 ---
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+ 2026-04-05 05:55:28 - ReXMoE - INFO - Instruction: Given the fact: predators eat prey
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+ Question: Predators eat
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+ A. lions
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+ B. humans
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+ C. bunnies
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+ D. grass
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+ Answer:
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+ 2026-04-05 05:55:28 - ReXMoE - INFO - Input: None
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+ 2026-04-05 05:55:28 - ReXMoE - INFO - Generated completion (len 100): lion is a predator.
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+
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+ :
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+ as are predators
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+
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+ :
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+ the as are predators
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+
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+ : the as are predators
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+
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+ : who are predators
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+
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+ : predators are
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+
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+
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+ : the e is a predator
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+
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+ : the e is a predator
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+
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+
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+ : e is a predator
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+
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+
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+ .
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+ animals are predators
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+
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+
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+ . animals are predators
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+
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+ animals are predators
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+
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+ 2026-04-05 05:55:28 - ReXMoE - INFO - Evaluation of all 3 prompts complete.
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+ 2026-04-05 05:55:28 - ReXMoE - INFO -
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+ Epoch 1/1
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+ 2026-04-05 06:19:10 - ReXMoE - INFO - Step 500/5000 | Loss: 1.0398 | KD Loss: 0.4316
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+ 2026-04-05 06:43:01 - ReXMoE - INFO - Step 1000/5000 | Loss: 1.9496 | KD Loss: 0.8789
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+ 2026-04-05 07:07:09 - ReXMoE - INFO - Step 1500/5000 | Loss: 1.3975 | KD Loss: 0.8164
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+ 2026-04-05 07:31:38 - ReXMoE - INFO - Step 2000/5000 | Loss: 1.6832 | KD Loss: 1.0781
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+ 2026-04-05 07:56:05 - ReXMoE - INFO - Step 2500/5000 | Loss: 1.4381 | KD Loss: 0.8203
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+ 2026-04-05 08:20:25 - ReXMoE - INFO - Step 3000/5000 | Loss: 0.9392 | KD Loss: 0.8086
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+ 2026-04-05 08:44:48 - ReXMoE - INFO - Step 3500/5000 | Loss: 0.8403 | KD Loss: 0.8125
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+ 2026-04-05 09:08:46 - ReXMoE - INFO - Step 4000/5000 | Loss: 1.1178 | KD Loss: 0.5469
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+ 2026-04-05 09:33:09 - ReXMoE - INFO - Step 4500/5000 | Loss: 1.3528 | KD Loss: 0.6992
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+ 2026-04-05 09:57:21 - ReXMoE - INFO - Step 5000/5000 | Loss: 1.9670 | KD Loss: 1.3125
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+ 2026-04-05 09:57:21 - ReXMoE - INFO -
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+ Evaluation at step 5000:
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+ 2026-04-05 09:57:21 - ReXMoE - INFO -
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+ Evaluating model with 3 sample prompts...
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+ 2026-04-05 09:57:24 - ReXMoE - INFO -
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+ --- Prompt 1/3 ---
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+ 2026-04-05 09:57:24 - ReXMoE - INFO - Instruction: What is the capital of France?
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+ 2026-04-05 09:57:24 - ReXMoE - INFO - Input: None
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+ 2026-04-05 09:57:24 - ReXMoE - INFO - Generated completion (len 9): The capital of France is Paris.
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+ 2026-04-05 09:57:26 - ReXMoE - INFO -
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+ --- Prompt 2/3 ---
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+ 2026-04-05 09:57:26 - ReXMoE - INFO - Instruction: High-pressure systems stop air from rising into the colder regions of the atmosphere where water can condense. What will most likely result if a high-pressure system remains in an area for a long period of time?
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+ A. fog
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+ B. rain
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+ C. drought
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+ D. tornado
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+ Answer:
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+ 2026-04-05 09:57:26 - ReXMoE - INFO - Input: None
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+ 2026-04-05 09:57:26 - ReXMoE - INFO - Generated completion (len 7): C. drought.
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+ 2026-04-05 09:57:29 - ReXMoE - INFO -
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+ --- Prompt 3/3 ---
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+ 2026-04-05 09:57:29 - ReXMoE - INFO - Instruction: Given the fact: predators eat prey
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+ Question: Predators eat
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+ A. lions
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+ B. humans
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+ C. bunnies
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+ D. grass
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+ Answer:
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+ 2026-04-05 09:57:29 - ReXMoE - INFO - Input: None
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+ 2026-04-05 09:57:29 - ReXMoE - INFO - Generated completion (len 12): The correct answer is C. bunnies.
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+ 2026-04-05 09:57:29 - ReXMoE - INFO - Evaluation of all 3 prompts complete.
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+ 2026-04-05 09:57:29 - ReXMoE - INFO - Epoch 1 completed. Average Loss: nan
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+ 2026-04-05 09:57:36 - ReXMoE - INFO - Training complete.