Update README.md

README.md

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-# Uploaded  model
+Indian Legal Assistant: A LLaMA-based Model for Indian Legal Text Generation
+This repository contains information and code for using the Indian Legal Assistant, a LLaMA-based model finetuned on Indian legal texts. This model is designed to assist with various legal tasks and queries related to Indian law.
+Table of Contents
 
-- **Developed by:** varma007ut
-- **License:** apache-2.0
+Model Description
+Model Details
+Installation
+Usage
+Evaluation
+Contributing
+License
 
+Model Description
+The Indian Legal Assistant is a text generation model specifically trained to understand and generate text related to Indian law. It can be used for tasks such as:
 
+Legal question answering
+Case summarization
+Legal document analysis
+Statute interpretation
+
+Model Details
+
+Model Name: Indian_Legal_Assitant
+Developer: varma007ut
+Model Size: 8.03B parameters
+Architecture: LLaMA
+Language: English
+License: Apache 2.0
+Hugging Face Repo: varma007ut/Indian_Legal_Assitant
+
+Installation
+To use this model, you'll need to install the required libraries:
+bashCopypip install transformers torch
+# For GGUF support
+pip install llama-cpp-python
+Usage
+There are several ways to use the Indian Legal Assistant model:
+1. Using Hugging Face Pipeline
+pythonCopyfrom transformers import pipeline
+
+pipe = pipeline("text-generation", model="varma007ut/Indian_Legal_Assitant")
+
+prompt = "Summarize the key points of the Indian Contract Act, 1872:"
+result = pipe(prompt, max_length=200)
+print(result[0]['generated_text'])
+2. Using Hugging Face Transformers directly
+pythonCopyfrom transformers import AutoTokenizer, AutoModelForCausalLM
+
+tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assitant")
+model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assitant")
+
+prompt = "What are the fundamental rights in the Indian Constitution?"
+inputs = tokenizer(prompt, return_tensors="pt")
+outputs = model.generate(**inputs, max_length=200)
+print(tokenizer.decode(outputs[0]))
+3. Using GGUF format with llama-cpp-python
+pythonCopyfrom llama_cpp import Llama
+
+llm = Llama.from_pretrained(
+    repo_id="varma007ut/Indian_Legal_Assitant",
+    filename="ggml-model-q4_0.gguf",  # Replace with the actual GGUF filename if different
+)
+
+response = llm.create_chat_completion(
+    messages = [
+        {
+            "role": "user",
+            "content": "Explain the concept of judicial review in India."
+        }
+    ]
+)
+
+print(response['choices'][0]['message']['content'])
+4. Using Inference Endpoints
+This model supports Hugging Face Inference Endpoints. You can deploy the model and use it via API calls. Refer to the Hugging Face documentation for more information on setting up and using Inference Endpoints.
+Evaluation
+To evaluate the model's performance:
+
+Prepare a test set of Indian legal queries or tasks.
+Use standard NLP evaluation metrics such as perplexity, BLEU score, or task-specific metrics.
+
+Example using BLEU score:
+pythonCopyfrom datasets import load_metric
+
+bleu = load_metric("bleu")
+predictions = model.generate(encoded_input)
+results = bleu.compute(predictions=predictions, references=references)
+Contributing
+We welcome contributions to improve the model or extend its capabilities. Please see our Contributing Guidelines for more details.
+License
+This project is licensed under the Apache 2.0 License. See the LICENSE file for details.