| | --- |
| | license: llama3 |
| | datasets: |
| | - ai-eldorado/Brazilian_CLT_preferences |
| | language: |
| | - en |
| | - pt |
| | base_model: |
| | - meta-llama/Meta-Llama-3-8B-Instruct |
| | tags: |
| | - legal |
| | --- |
| | |
| | **Model Description** |
| | This model is a fine-tuned version of **LLaMA-3 8B Instruct (4-bit quantized)**, optimized using **Direct Preference Optimization (DPO)** for answering legal questions related to Brazil’s Consolidation of Labor Laws (CLT). The fine-tuning process leveraged a curated dataset of **736 human-preference triplets**, annotated by HR specialists and legal experts, to align the model with domain-specific expectations for accuracy and compliance. |
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| | **Intended Use** |
| | The model is designed for **legal question answering** in the context of Brazilian labor law, supporting HR departments, compliance teams, and legal professionals. It aims to provide **factually accurate and semantically aligned responses** to CLT-related queries. |
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| | **Training Details** |
| | - **Base Model:** LLaMA-3 8B (4-bit quantized) |
| | - **Fine-tuning Method:** Direct Preference Optimization (DPO) |
| | - **Dataset:** 736 validated human-preference entries on CLT-related questions |
| | - **Hyperparameters:** |
| | - Batch size: 2 |
| | - Gradient accumulation: 3 |
| | - Epochs: 1 |
| | - Learning rate: 5e-6 |
| | - Optimizer: AdamW 8-bit |
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| | **Performance Summary** |
| | Compared to the base model, this DPO-tuned model achieved: |
| | - **+11% improvement in factual accuracy** |
| | - Higher semantic similarity scores |
| | - Slight trade-off in fluency and argumentative structure |
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| | #### **Ethical Considerations** |
| | - **Legal Disclaimer:** This model does **not** replace professional legal advice. Users should consult qualified professionals for critical decisions. |
| | - **Risk of Misinterpretation:** Responses may omit nuances or context-specific interpretations of labor law. |
| | - **Data Privacy:** The model was trained on synthetic and curated datasets, not on personal or confidential data. |
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| | #### **Bias and Fairness** |
| | - The dataset was curated by HR and legal experts to minimize bias, but: |
| | - **Regional Bias:** Focused exclusively on Brazilian CLT; not applicable to other jurisdictions. |
| | - **Interpretation Bias:** Human annotators’ preferences may reflect subjective interpretations of legal norms. |
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| | #### **Limitations** |
| | - Domain-specific; performance may degrade outside CLT-related queries. |
| | - BLEU and ROUGE scores remain low due to metric limitations in legal contexts. |
| | - Limited training data may affect generalization to complex or ambiguous cases. |
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| | #### **Citation** |
| | soon |
