Hemasagar commited on
Commit
ee8e561
·
verified ·
1 Parent(s): 1db2f5d

Update app.py

Browse files
Files changed (1) hide show
  1. app.py +1 -25
app.py CHANGED
@@ -1,51 +1,27 @@
1
  import streamlit as st
2
  from langchain.prompts import PromptTemplate
3
- # Recently the below import has been replaced by later one
4
- # Use a pipeline as a high-level helper
5
- # from transformers import pipeline
6
  import transformers
7
-
8
- # model_from_hugging_face = transformers.pipeline("text-generation", model="TheBloke/Llama-2-7B-Chat-GGML")
9
- # Load model directly
10
  # with ctransformers, you can load from Hugging Face Hub directly and specify a model file (.bin or .gguf files) using:
11
-
12
  from ctransformers import AutoModelForCausalLM
13
-
14
- # llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", model_file="llama-2-7b-chat.ggmlv3.q8_0.bin")
15
- # # from langchain.llms import CTransformers
16
- # from langchain_community.llms import CTransformers
17
-
18
  #Function to get the response back
19
  def getLLMResponse(form_input,email_sender,email_recipient,email_style):
20
- #llm = OpenAI(temperature=.9, model="text-davinci-003")
21
-
22
  # Wrapper for Llama-2-7B-Chat, Running Llama 2 on CPU
23
-
24
  #Quantization is reducing model precision by converting weights from 16-bit floats to 8-bit integers,
25
  #enabling efficient deployment on resource-limited devices, reducing model size, and maintaining performance.
26
-
27
  #C Transformers offers support for various open-source models,
28
  #among them popular ones like Llama, GPT4All-J, MPT, and Falcon.
29
-
30
-
31
- #C Transformers is the Python library that provides bindings for transformer models implemented in C/C++ using the GGML library
32
-
33
  llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", model_file="llama-2-7b-chat.ggmlv3.q8_0.bin")
34
-
35
-
36
  #Template for building the PROMPT
37
  template = """
38
  Write a email with {style} style and includes topic :{email_topic}.\n\nSender: {sender}\nRecipient: {recipient}
39
  \n\nEmail Text:
40
 
41
  """
42
-
43
  #Creating the final PROMPT
44
  prompt = PromptTemplate(
45
  input_variables=["style","email_topic","sender","recipient"],
46
  template=template,)
47
-
48
-
49
  #Generating the response using LLM
50
  #Last week langchain has recommended to use 'invoke' function for the below please :)
51
  response=llm(prompt.format(email_topic=form_input,sender=email_sender,recipient=email_recipient,style=email_style))
 
1
  import streamlit as st
2
  from langchain.prompts import PromptTemplate
 
 
 
3
  import transformers
 
 
 
4
  # with ctransformers, you can load from Hugging Face Hub directly and specify a model file (.bin or .gguf files) using:
 
5
  from ctransformers import AutoModelForCausalLM
 
 
 
 
 
6
  #Function to get the response back
7
  def getLLMResponse(form_input,email_sender,email_recipient,email_style):
8
+
 
9
  # Wrapper for Llama-2-7B-Chat, Running Llama 2 on CPU
 
10
  #Quantization is reducing model precision by converting weights from 16-bit floats to 8-bit integers,
11
  #enabling efficient deployment on resource-limited devices, reducing model size, and maintaining performance.
 
12
  #C Transformers offers support for various open-source models,
13
  #among them popular ones like Llama, GPT4All-J, MPT, and Falcon.
 
 
 
 
14
  llm = AutoModelForCausalLM.from_pretrained("TheBloke/Llama-2-7B-Chat-GGML", model_file="llama-2-7b-chat.ggmlv3.q8_0.bin")
 
 
15
  #Template for building the PROMPT
16
  template = """
17
  Write a email with {style} style and includes topic :{email_topic}.\n\nSender: {sender}\nRecipient: {recipient}
18
  \n\nEmail Text:
19
 
20
  """
 
21
  #Creating the final PROMPT
22
  prompt = PromptTemplate(
23
  input_variables=["style","email_topic","sender","recipient"],
24
  template=template,)
 
 
25
  #Generating the response using LLM
26
  #Last week langchain has recommended to use 'invoke' function for the below please :)
27
  response=llm(prompt.format(email_topic=form_input,sender=email_sender,recipient=email_recipient,style=email_style))