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- library_name: transformers
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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 [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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+ language:
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+ - fr
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+ - ar
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+ - en
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+ license: apache-2.0
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+ base_model: Qwen/Qwen2-7B
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+ tags:
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+ - legal
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+ - lora
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+ - finetuned
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+ - qwen2
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+ - jurisprudence
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+ - safetensors
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+ datasets:
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+ - ApyHTML19/JurisRoute_CODE_MAROCAIN
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  ---
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+ # ⚖️ JurisRoute Qwen2 LoRa Fine-tuned
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+ ## 📌 Description
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+ **JurisRoute** is a fine-tuned version of [Qwen2-7B](https://huggingface.co/Qwen/Qwen2-7B)
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+ using **LoRA (Low-Rank Adaptation)** for legal and juridical question answering.
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+ The model is specialized for:
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+ - 🇲🇦 Moroccan law & jurisprudence
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+ - Legal document understanding
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+ - Juridical Q&A in French, Arabic, and English
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+ ---
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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+ ## 🎯 Objective
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+ The goal of JurisRoute is to provide an accessible AI assistant capable of
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+ answering legal questions based on Moroccan juridical texts, without requiring
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+ expensive cloud infrastructure.
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+ ---
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+ ## 🔧 Fine-tuning Process
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+ ### Base Model
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+ - **Model**: Qwen2-7B
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+ - **Method**: LoRA (Low-Rank Adaptation)
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+ - **Format**: Safetensors
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+ ### LoRA Configuration
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+ | Parameter | Value |
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+ | Method | LoRA |
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+ | Rank (r) | 16 |
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+ | Alpha | 32 |
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+ | Target modules | q_proj, v_proj |
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+ | Dropout | 0.05 |
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+ ### Training Setup
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+ | Parameter | Value |
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+ |---|---|
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+ | Framework | Hugging Face PEFT |
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+ | Precision | FP16 |
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+ | Optimizer | AdamW |
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+ | Dataset | Moroccan legal texts |
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+ ---
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+ ## 📊 Fine-tuning Process Diagram
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+ ![Capture d'écran 2026-03-17 232753](https://cdn-uploads.huggingface.co/production/uploads/69b8999e75f123eb8e7e6776/HJCLbOT4kS-yY_sC-f70P.png)
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+ ![Capture d'écran 2026-03-17 232759](https://cdn-uploads.huggingface.co/production/uploads/69b8999e75f123eb8e7e6776/t3hE8Esaa_8BogITG5rg4.png)