Update README.md

README.md

@@ -11,11 +11,10 @@ tags:
 - trl
 - sft
 ---
-
 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
+This repository contains information and code for the Indian Legal Assistant, a LLaMA-based model finetuned on Indian legal texts. The model is designed to assist with various legal tasks and queries related to Indian law.
 
+Table of Contents
 Model Description
 Model Details
 Installation
@@ -23,84 +22,70 @@ 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:
+The Indian Legal Assistant is a text generation model specifically trained to understand and generate text related to Indian law. It is suitable for various legal tasks such as:
 
 Legal question answering
 Case summarization
 Legal document analysis
 Statute interpretation
-
 Model Details
-
-Model Name: Indian_Legal_Assitant
+Model Name: Indian_Legal_Assistant
 Developer: varma007ut
 Model Size: 8.03B parameters
 Architecture: LLaMA
 Language: English
 License: Apache 2.0
-Hugging Face Repo: varma007ut/Indian_Legal_Assitant
-
+Hugging Face Repository: varma007ut/Indian_Legal_Assistant
 Installation
-To use this model, you'll need to install the required libraries:
-bashCopypip install transformers torch
+To use this model, install the required libraries:
+
+bash
+Copy code
+pip 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
+python
+Copy code
+from transformers import pipeline
 
-pipe = pipeline("text-generation", model="varma007ut/Indian_Legal_Assitant")
+pipe = pipeline("text-generation", model="varma007ut/Indian_Legal_Assistant")
 
 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
+2. Using Hugging Face Transformers Directly
+python
+Copy code
+from transformers import AutoTokenizer, AutoModelForCausalLM
 
-tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assitant")
-model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assitant")
+tokenizer = AutoTokenizer.from_pretrained("varma007ut/Indian_Legal_Assistant")
+model = AutoModelForCausalLM.from_pretrained("varma007ut/Indian_Legal_Assistant")
 
 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
+3. Using GGUF Format with llama-cpp-python
+python
+Copy code
+from llama_cpp import Llama
 
 llm = Llama.from_pretrained(
-    repo_id="varma007ut/Indian_Legal_Assitant",
+    repo_id="varma007ut/Indian_Legal_Assistant",
     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."
-        }
+    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.
-