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  - unsloth
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  - trl
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  - grpo
 
 
 
 
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  ---
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  # Model Card for Model ID
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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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  ## 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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  #### 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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  ## 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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  - grpo
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+ license: apache-2.0
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+ base_model:
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+ - Qwen/Qwen2.5-3B
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+ - Qwen/Qwen2.5-VL-3B-Instruct
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  ---
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  # Model Card for Model ID
 
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  <!-- Provide a longer summary of what this model is. -->
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+ DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
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+ - **Developed by:** [Ali Asghar (jaffry258@gmail.com)]
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+ - **Funded by [optional]:** [still under progress ]
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+ - **Shared by [optional]:** []
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+ - **Model type:** [Large Language Model (LLM)]
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+ - **Language(s) (NLP):** [pytorch,transformers,python]
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+ - **License:** [Appache 2.0]
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+ - **Finetuned from model [optional]:** [Qwen2.5-3B]
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  ### Model Sources [optional]
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  <!-- Provide the basic links for the model. -->
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+ - **Repository:** [https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB/tree/main]
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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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+ DeutscheLexAI_BGB is a fine-tuned Qwen2.5-3B model specializing in German legal text processing, trained on the Bürgerliches Gesetzbuch (BGB) dataset. It enhances legal text understanding, summarization, and reasoning for German legal documents.
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  ### Direct Use
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+ Legal research: Extract, summarize, and analyze BGB texts.
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+ AI-powered legal assistants: Provide insights into German law.
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+
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+ Academic purposes: Assists in legal document structuring.
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  [More Information Needed]
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  ### Downstream Use [optional]
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+ Chatbots for legal guidance.
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+
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+ AI-based contract analysis.
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  [More Information Needed]
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  ## Bias, Risks, and Limitations
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+ The model may reflect biases in the BGB dataset.
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+ Not suitable for real-time legal decision-making.
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+ Might struggle with non-German legal texts.
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  [More Information Needed]
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  #### Training Hyperparameters
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+ - **Training regime:** [More Information Needed]
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+ - trainer = GRPOTrainer(
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+ model = model,
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+ processing_class = tokenizer,
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+ reward_funcs = [
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+ xmlcount_reward_func,
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+ soft_format_reward_func,
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+ strict_format_reward_func,
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+ int_reward_func,
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+ correctness_reward_func,
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+ ],
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+ args = training_args,
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+ train_dataset = dataset,
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+ )
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+ trainer.train()
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  #### Speeds, Sizes, Times [optional]
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  ## Citation [optional]
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+ @article{DeutscheLexAI_BGB,
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+ title={DeutscheLexAI_BGB: A Fine-Tuned Qwen2.5-3B Model for German Legal Texts},
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+ author={Your Name or Organization},
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+ journal={Hugging Face Model Hub},
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+ year={2025},
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+ url={https://huggingface.co/Alijeff1214/DeutscheLexAI_BGB}
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+ }
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+
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  **BibTeX:**
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