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  ---
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- library_name: transformers
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  license: unlicense
 
 
 
 
 
 
 
 
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  language:
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  - en
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  metrics:
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  - accuracy
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- - transformersegmentation/segmentation_scores
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- pipeline_tag: text-generation
 
 
 
 
 
 
 
 
 
 
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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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- [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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- [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 [optional]
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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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  license: unlicense
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+ datasets:
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+ - proj-persona/PersonaHub
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+ - Salesforce/xlam-function-calling-60k
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+ - BAAI/Infinity-Instruct
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+ - HuggingFaceFW/fineweb
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+ - HuggingFaceTB/smollm-corpus
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+ - wikimedia/wikipedia
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+ - Open-Orca/OpenOrca
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  language:
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  - en
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  metrics:
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  - accuracy
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+ - precision
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+ - bleu
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+ - recall
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+ - hack/test_metric
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+ library_name: -transformers
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+ pipeline_tag: -text-generation
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+ tags:
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+ - text-generation-inference
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+ - question answering
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+ - logic
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+ - reasoning
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+ - text-classification
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  ---
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+ Model Card for Model ID
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ LLM for Text Generation
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+ Model Details
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+ JudgeXL-LLM is a large language model designed for text generation, classification and question answering using logic and reasoning to predict the next sequence of tokens
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+ Model Description
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+ Model is designed to have no restrictions or barriers, use it at your own risk.
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+ Developed by: WonGrifferousAI
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+ Funded by [optional]: WonGrifferousAI
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+ Shared by [optional]: Judge_Mrogan
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+ Model type: Transformer LLM
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+ Language(s) (NLP): English
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+ License: Unlicense
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+ Uses
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+ Use for anything you can train it to do.
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+ Out-of-Scope Use
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+ This model is good for everything
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+ Bias, Risks, and Limitations
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+ No limitations
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+ Recommendations
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+ Recommend everyone should use as needed.
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+ ---
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+ tags:
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+ - text-generation-inference
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+ - not-for-all-audiences
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ base_model: TransformersStyleXL
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+ instance_prompt: null
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+ license: unlicense
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+ datasets:
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+ - proj-persona/PersonaHub
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+ - Salesforce/xlam-function-calling-60k
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+ - BAAI/Infinity-Instruct
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+ - HuggingFaceFW/fineweb
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+ - wikimedia/wikipedia
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+ language:
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+ - en
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+ metrics:
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+ - accuracy
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+ - code_eval
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+ library_name: transformers
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+ pipeline_tag: text-generation
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+ ---
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+ # JudgeLLM
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+ ## Download model
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+ Weights for this model are available in PyTorch,Safetensors format.
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+ [Download](/Wonder-Griffin/JudgeLLM/tree/main) them in the Files & versions tab.