Upload inference_lawyergpt_finetune_falcon7b_indian_law_data (1).py

inference_lawyergpt_finetune_falcon7b_indian_law_data (1).py

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+# -*- coding: utf-8 -*-
+"""Inference_LawyerGPT_Finetune_falcon7b_Indian_Law_Data.ipynb
+
+Automatically generated by Colaboratory.
+
+Original file is located at
+    https://colab.research.google.com/drive/1NpBtrGAcXsmoSmM5Sr-INiE5-tU9D37n
+
+### Install requirements
+
+First, run the cells below to install the requirements:
+"""
+
+!nvidia-smi
+
+!pip install -Uqqq pip --progress-bar off
+!pip install -qqq bitsandbytes==0.39.0
+!pip install -qqq torch--2.0.1 --progress-bar off
+!pip install -qqq -U git+https://github.com/huggingface/transformers.git@e03a9cc --progress-bar off
+!pip install -qqq -U git+https://github.com/huggingface/peft.git@42a184f --progress-bar off
+!pip install -qqq -U git+https://github.com/huggingface/accelerate.git@c9fbb71 --progress-bar off
+!pip install -qqq datasets==2.12.0 --progress-bar off
+!pip install -qqq loralib==0.1.1 --progress-bar off
+!pip install einops
+
+import os
+# from pprint import pprint
+# import json
+
+import bitsandbytes as bnb
+import pandas as pd
+import torch
+import torch.nn as nn
+import transformers
+from datasets import load_dataset
+from huggingface_hub import notebook_login
+from peft import (
+    LoraConfig,
+    PeftConfig,
+    get_peft_model,
+    prepare_model_for_kbit_training,
+)
+from transformers import (
+    AutoConfig,
+    AutoModelForCausalLM,
+    AutoTokenizer,
+    BitsAndBytesConfig,
+)
+
+os.environ["CUDA_VISIBLE_DEVICES"] = "0"
+
+notebook_login()
+#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
+
+"""### Load dataset"""
+
+from datasets import load_dataset
+
+dataset_name = "nisaar/Lawyer_GPT_India"
+#dataset_name = "patrick11434/TEST_LLM_DATASET"
+dataset = load_dataset(dataset_name, split="train")
+
+"""## Load adapters from the Hub
+
+You can also directly load adapters from the Hub using the commands below:
+"""
+
+from peft import *
+
+#change peft_model_id
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit=True,
+    load_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16,
+)
+
+peft_model_id = "nisaar/falcon7b-Indian_Law_150Prompts"
+config = PeftConfig.from_pretrained(peft_model_id)
+model = AutoModelForCausalLM.from_pretrained(
+    config.base_model_name_or_path,
+    return_dict=True,
+    quantization_config=bnb_config,
+    device_map="auto",
+    trust_remote_code=True,
+)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+tokenizer.pad_token = tokenizer.eos_token
+
+
+model = PeftModel.from_pretrained(model, peft_model_id)
+
+"""## Inference
+
+You can then directly use the trained model or the model that you have loaded from the 🤗 Hub for inference as you would do it usually in `transformers`.
+"""
+
+generation_config = model.generation_config
+generation_config.max_new_tokens = 200
+generation_config_temperature = 1
+generation_config.top_p = 0.7
+generation_config.num_return_sequences = 1
+generation_config.pad_token_id = tokenizer.eos_token_id
+generation_config_eod_token_id = tokenizer.eos_token_id
+
+DEVICE = "cuda:0"
+
+# Commented out IPython magic to ensure Python compatibility.
+# %%time
+# prompt = f"""
+# <human>: Who appoints the Chief Justice of India?
+# <assistant>:
+# """.strip()
+# 
+# encoding = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+# with torch.inference_mode():
+#   outputs = model.generate(
+#       input_ids=encoding.attention_mask,
+#       generation_config=generation_config,
+#   )
+# print(tokenizer.decode(outputs[0],skip_special_tokens=True))
+
+def generate_response(question: str) -> str:
+    prompt = f"""
+    <human>: {question}
+    <assistant>:
+    """.strip()
+    encoding = tokenizer(prompt, return_tensors="pt").to(DEVICE)
+    with torch.inference_mode():
+        outputs = model.generate(
+            input_ids=encoding.input_ids,
+            attention_mask=encoding.attention_mask,
+            generation_config=generation_config,
+        )
+    response = tokenizer.decode(outputs[0],skip_special_tokens=True)
+
+    assistant_start = '<assistant>:'
+    response_start = response.find(assistant_start)
+    return response[response_start + len(assistant_start):].strip()
+
+prompt = "Debate the merits and demerits of introducing simultaneous elections in India?"
+print(generate_response(prompt))
+
+prompt = "What are the duties of the President of India as per the Constitution?"
+print(generate_response(prompt))
+
+prompt = "Write a legal memo on the issue of manual scavenging in light of The Prohibition of Employment as Manual Scavengers and their Rehabilitation Act, 2013."
+print(generate_response(prompt))
+
+prompt
+
+prompt = "Explain the concept of 'Separation of Powers' in the Indian Constitution"
+print(generate_response(prompt))
+
+prompt = "Can you explain the steps for registration of a trademark in India?"
+print(generate_response(prompt))
+
+prompt = "What are the potential implications of the proposed Personal Data Protection Bill on tech companies in India?"
+print(generate_response(prompt))
+
+prompt = "Can you draft a non-disclosure agreement (NDA) under Indian law?"
+print(generate_response(prompt))
+
+prompt = "Can you summarize the main points of Article 21 of the Indian Constitution?"
+print(generate_response(prompt))
+
+prompt = "Can you summarize the main arguments of the Supreme Court of India judgment in Kesavananda Bharati v. State of Kerala?"
+print(generate_response(prompt))
+
+prompt = "what is the mysterious case of Advocate Nisaar that was a famous in supreme court of india?"
+print(generate_response(prompt))
+
+prompt = "what is the mysterious case of Advocate Nisaar that was a famous in supreme court of india?"
+print(generate_response(prompt))
+
+prompt = "Can you draft a confidentiality clause for a contract under Indian law?"
+print(generate_response(prompt))
+
+prompt = "How is the concept of 'Economic Justice' enshrined in the Preamble of the Indian Constitution??"
+print(generate_response(prompt))
+
+prompt = "What is the role of the 'Supreme Court' in preserving the fundamental rights of citizens in India?"
+print(generate_response(prompt))
+
+prompt = "Analyze the potential impact of 'Online Education Rights' for students in India?"
+print(generate_response(prompt))
+
+prompt = "Analyze the potential impact of 'Online Education Rights' for students in India?"
+print(generate_response(prompt))
+
+prompt = "Discuss the potential effects of a 'Universal Basic Income' policy in India"
+print(generate_response(prompt))
+
+prompt = "Analyze the potential impact of 'Online Education Rights' for students in India?"
+print(generate_response(prompt))