Upload folder using huggingface_hub

.ipynb_checkpoints/app-checkpoint.py

@@ -0,0 +1,99 @@
+import shutil
+import requests
+import sys
+from typing import Optional, List, Tuple
+import json
+from langchain_community.llms import HuggingFaceHub
+
+
+##Loading the Model to answer questions
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+
+
+peft_model_id = "Ubaidbhat/zephr_finance_finetuned"
+config = PeftConfig.from_pretrained(peft_model_id)
+print(config.base_model_name_or_path)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit = True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+d_map = {"": torch.cuda.current_device()} if torch.cuda.is_available() else None
+
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, quantization_config=bnb_config, device_map=d_map)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, peft_model_id)
+model = model.merge_and_unload()
+
+
+
+
+
+##Creating base Model Chain
+from langchain.llms import HuggingFacePipeline
+from langchain.prompts import PromptTemplate
+from transformers import pipeline
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains import LLMChain
+
+text_generation_pipeline = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    temperature=0.2,
+    do_sample=True,
+    repetition_penalty=1.1,
+    return_full_text=True,
+    max_new_tokens=400,
+    pad_token_id=tokenizer.eos_token_id,
+)
+
+llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
+
+prompt_template = """
+<|system|>
+Answer the question based on your knowledge.
+</s>
+<|user|>
+{question}
+</s>
+<|assistant|>
+"""
+
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template=prompt_template,
+)
+
+llm_chain = prompt | llm | StrOutputParser()
+
+def inference(question):
+    llmAnswer = llm_chain.invoke({"question": question})
+    llmAnswer = llmAnswer.rstrip()
+    return llmAnswer
+
+
+
+import gradio as gr
+from langchain_core.runnables import RunnablePassthrough
+
+def predict(question):
+    return inference(question)
+    
+pred = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Question"),
+    ],
+    outputs="text",
+    title="Finetuned Zephr Model in the Finance Domain."
+)
+
+pred.launch(share=True)
+
+
+

.ipynb_checkpoints/requirements-checkpoint.txt

@@ -0,0 +1,13 @@
+torch 
+bitsandbytes
+transformers
+peft
+accelerate
+faiss-gpu
+datasets
+trl
+gradio
+langchain 
+sentence-transformers 
+pandas
+langchain-community

README.md

@@ -1,12 +1,6 @@
 ---
 title: FinanceTuned
-emoji: 🐨
-colorFrom: pink
-colorTo: yellow
+app_file: app.py
 sdk: gradio
 sdk_version: 4.21.0
-app_file: app.py
-pinned: false
 ---
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

@@ -0,0 +1,99 @@
+import shutil
+import requests
+import sys
+from typing import Optional, List, Tuple
+import json
+from langchain_community.llms import HuggingFaceHub
+
+
+##Loading the Model to answer questions
+import torch
+from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
+from peft import PeftModel, PeftConfig
+
+
+peft_model_id = "Ubaidbhat/zephr_finance_finetuned"
+config = PeftConfig.from_pretrained(peft_model_id)
+print(config.base_model_name_or_path)
+bnb_config = BitsAndBytesConfig(
+    load_in_4bit = True,
+    bnb_4bit_use_double_quant=True,
+    bnb_4bit_quant_type="nf4",
+    bnb_4bit_compute_dtype=torch.bfloat16
+)
+
+d_map = {"": torch.cuda.current_device()} if torch.cuda.is_available() else None
+
+model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, quantization_config=bnb_config, device_map=d_map)
+tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)
+model = PeftModel.from_pretrained(model, peft_model_id)
+model = model.merge_and_unload()
+
+
+
+
+
+##Creating base Model Chain
+from langchain.llms import HuggingFacePipeline
+from langchain.prompts import PromptTemplate
+from transformers import pipeline
+from langchain_core.output_parsers import StrOutputParser
+from langchain.chains import LLMChain
+
+text_generation_pipeline = pipeline(
+    model=model,
+    tokenizer=tokenizer,
+    task="text-generation",
+    temperature=0.2,
+    do_sample=True,
+    repetition_penalty=1.1,
+    return_full_text=True,
+    max_new_tokens=400,
+    pad_token_id=tokenizer.eos_token_id,
+)
+
+llm = HuggingFacePipeline(pipeline=text_generation_pipeline)
+
+prompt_template = """
+<|system|>
+Answer the question based on your knowledge.
+</s>
+<|user|>
+{question}
+</s>
+<|assistant|>
+"""
+
+prompt = PromptTemplate(
+    input_variables=["question"],
+    template=prompt_template,
+)
+
+llm_chain = prompt | llm | StrOutputParser()
+
+def inference(question):
+    llmAnswer = llm_chain.invoke({"question": question})
+    llmAnswer = llmAnswer.rstrip()
+    return llmAnswer
+
+
+
+import gradio as gr
+from langchain_core.runnables import RunnablePassthrough
+
+def predict(question):
+    return inference(question)
+    
+pred = gr.Interface(
+    fn=predict,
+    inputs=[
+        gr.Textbox(label="Question"),
+    ],
+    outputs="text",
+    title="Finetuned Zephr Model in the Finance Domain."
+)
+
+pred.launch(share=True)
+
+
+

requirements.txt

@@ -0,0 +1,13 @@
+torch 
+bitsandbytes
+transformers
+peft
+accelerate
+faiss-gpu
+datasets
+trl
+gradio
+langchain 
+sentence-transformers 
+pandas
+langchain-community