khalidrajan
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Llama-3.1-8B-Instruct-Legal-NLI

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 ---
+language: en
+tags:
+- llama
+- legal-nli
+- natural-language-inference
+- legal-ai
+license: llama2
+datasets:
+- darrow-ai/LegalLensNLI-SharedTask
 ---
+# Llama-3.1-8B-Instruct-Legal-NLI
+This model is a fine-tuned version of Meta's Llama-3.1-8B model, specifically trained for Legal Natural Language Inference (NLI) tasks. It can determine the relationship between legal premises and hypotheses as either Entailed, Contradicted, or Neutral.
+The model has been trained on the [LegalLens NLI Shared Task dataset](https://huggingface.co/datasets/darrow-ai/LegalLensNLI-SharedTask).
 ## Model Details
+- **Base Model**: meta-llama/Meta-Llama-3.1-8B
+- **Task**: Legal Natural Language Inference
+- **Training Method**: LoRA fine-tuning
+- **Training Dataset**: LegalLensNLI-SharedTask
+- **Languages**: English
+- **License**: Same as Llama 2
+## Performance
+The model achieves strong performance on the evaluation set:
+- Accuracy: 86.1%
+- Macro F1 Score: 85.8%
 ## Training Details
+The model was trained using the following configuration:
+- **LoRA Config**:
+  - Alpha: 32
+  - Rank: 16
+  - Dropout: 0.05
+  - Target Modules: ['down_proj', 'gate_proj', 'o_proj', 'v_proj', 'up_proj', 'q_proj', 'k_proj']
+- **Training Parameters**:
+  - Learning Rate: 2e-4
+  - Epochs: 30
+  - Batch Size: 1
+  - Gradient Accumulation Steps: 4
+  - Max Sequence Length: 512
+## Intended Use
+This model is designed for:
+- Legal document analysis
+- Understanding relationships between legal statements
+- Automated legal reasoning tasks
+- Legal compliance verification
+## Limitations
+- Limited to English legal text
+- Performance may vary on legal domains not represented in the training data
+- Should not be used as sole decision-maker for legal matters
+- Requires legal expertise for proper interpretation of results