aicinema69
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CLAT_Mentor_LLM

@@ -1,199 +1,172 @@
 ---
 library_name: transformers
-tags: []
 ---
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 ---
 library_name: transformers
+datasets:
+- aicinema69/CAT-2025
+language:
+- en
+- hi
+base_model:
+- NousResearch/Llama-2-7b-chat-hf
 ---
+# CLAT Mentor LLM - Legal Reasoning Assistant
+[![Hugging Face](https://img.shields.io/badge/Hugging%20Face-Model-yellow)](https://huggingface.co/aicinema69/CLAT_Mentor_LLM)
+[![Task](https://img.shields.io/badge/Task-Legal%20QA-blue)]()
+[![Framework](https://img.shields.io/badge/Framework-Transformers-orange)]()
+## Model Description
+CLAT Mentor LLM is a specialized language model fine-tuned for the legal domain, specifically designed to assist aspirants preparing for the Common Law Admission Test (CLAT) in India. This model enhances legal reasoning capabilities and provides context-aware responses to questions related to legal concepts, case laws, and CLAT examination topics.
+## Key Capabilities
+- **Legal Reasoning**: Analyzes legal scenarios and provides logical explanations
+- **Case Law Analysis**: Identifies relevant precedents and explains their applications
+- **CLAT Exam Guidance**: Offers targeted assistance for CLAT preparation
+- **RAG-Compatible**: Optimized for Retrieval-Augmented Generation applications
+## Model Details
+- **Developed by**: Satyam Singh
+- **Model type**: Transformer-based Language Model
+- **Language**: English (with some Hindi support)
+- **License**: Open Source
+- **Finetuned from model**: [NousResearch/Llama-2-7b-chat-hf](https://huggingface.co/NousResearch/Llama-2-7b-chat-hf)
+## Use Cases
 ### Direct Use
+This model can be directly used for:
+- Answering questions about legal concepts and principles
+- Explaining case laws and their applications
+- Providing guidance on CLAT exam preparation strategies
+- Assisting with legal reasoning puzzles and logical deduction
+### Downstream Use
+The model is optimized for integration into:
+- Legal education platforms
+- CLAT preparation applications
+- RAG-based legal research systems
+- Educational chatbots for law aspirants
+### Out-of-Scope Use
+This model is not intended for:
+- Providing legal advice that would replace a qualified attorney
+- Generating legal documents for official use
+- Making predictions about case outcomes in real legal proceedings
+- Using in contexts where legal errors could have significant consequences
+## Bias, Risks, and Limitations
+- The model may reflect biases present in legal education materials and case law
+- It has been trained primarily on Indian legal concepts and may have limited knowledge of other legal systems
+- The model should not be used as a substitute for professional legal advice
+- Outputs should be verified by legal professionals for critical applications
+## Getting Started
+### Using with Transformers Library
+```python
+from transformers import AutoTokenizer, AutoModelForCausalLM
+# Load the model and tokenizer
+tokenizer = AutoTokenizer.from_pretrained("aicinema69/CLAT_Mentor_LLM")
+model = AutoModelForCausalLM.from_pretrained("aicinema69/CLAT_Mentor_LLM")
+# Generate a response
+prompt = "Explain the concept of precedent in Indian law."
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+response = tokenizer.decode(outputs[0], skip_special_tokens=True)
+print(response)
+```
+### Using with Hugging Face Inference API
+```python
+import requests
+API_URL = "https://api-inference.huggingface.co/models/aicinema69/CLAT_Mentor_LLM"
+headers = {"Authorization": f"Bearer {API_TOKEN}"}
+def query(payload):
+    response = requests.post(API_URL, headers=headers, json=payload)
+    return response.json()
+output = query({
+    "inputs": "What topics should I focus on for the CLAT legal reasoning section?",
+})
+print(output)
+```
 ## Training Details
 ### Training Data
+This model was fine-tuned on the specialized dataset:
+- Dataset: [aicinema69/CAT-2025](https://huggingface.co/datasets/aicinema69/CAT-2025)
+- Dataset includes CLAT preparation materials, legal case summaries, and curated legal reasoning Q&A pairs
 ### Training Procedure
+- **Fine-tuning method**: PEFT/LoRA (Parameter-Efficient Fine-Tuning)
+- **Base model**: NousResearch/Llama-2-7b-chat-hf
+- **Training regime**: 4-bit quantization (nf4)
+- **Epochs**: 5 (originally planned for 20)
+- **Batch size**: 4 per device
+- **Optimizer**: AdamW (paged_adamw_32bit)
+- **Learning rate**: 2e-4
+- **Weight decay**: 0.001
+- **LR scheduler**: cosine
+- **Warmup ratio**: 0.03
+#### LoRA Configuration
+- **LoRA attention dimension (r)**: 64
+- **LoRA alpha**: 16
+- **LoRA dropout**: 0.1
+#### Quantization Settings
+- **Precision**: 4-bit
+- **Quantization type**: nf4
+- **Compute dtype**: float16
+## Integration with CLAT Mentor Application
+This model is a core component of the CLAT Mentor AI assistant, which combines:
+- This fine-tuned LLM for domain-specific knowledge
+- FAISS vector database for retrieval-augmented generation
+- Streamlit-based interactive interface for user interaction
+For the complete application code, visit: [GitHub Repository](https://github.com/SatyamSingh8306)
+## Citation
+If you use this model in your research or application, please cite:
+```bibtex
+@misc{singh2025clatmentor,
+  author = {Singh, Satyam},
+  title = {CLAT Mentor LLM: A Fine-tuned Language Model for Legal Reasoning},
+  year = {2025},
+  publisher = {Hugging Face},
+  howpublished = {\url{https://huggingface.co/aicinema69/CLAT_Mentor_LLM}}
+}
+```
+## Contact
+- **Developer**: Satyam Singh
+- **LinkedIn**: [linkedin.com/in/satyam8306](https://linkedin.com/in/satyam8306)
+- **GitHub**: [SatyamSingh8306](https://github.com/SatyamSingh8306)
+- **Email**: satyamsingh7734@gmail.com
+## Acknowledgements
+- National Law Technology Institute (NLTI) for domain expertise
+- NousResearch for the base Llama-2-7b-chat-hf model
+- Hugging Face for the transformers library and model hosting platforms
+- The PEFT library developers for enabling efficient fine-tuning methods