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"""
import gradio as gr
def mental_chat(message, history):
return givetext(patienttext,newmodel,newtokenizer)
demo = gr.ChatInterface(mental_chat)
demo.launch()
"""
#pip install huggingface_hub
#python -c "from huggingface_hub.hf_api import HfFolder; HfFolder.save_token('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')"
#!pip install accelerate
#!pip install -i
import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
# ##### ##### ##### ##### #####
peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
config = PeftConfig.from_pretrained(peft_model_id,
use_auth_token="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", load_in_8bit=True, device_map='cpu',)
newmodel = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='cpu',
use_auth_token="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")
newtokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path,
use_auth_token="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", load_in_8bit=True, device_map='cpu',)
# Load the Lora model
newmodel = PeftModel.from_pretrained(newmodel, peft_model_id,
use_auth_token="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", load_in_8bit=True, device_map='cpu')
def givetext(input_text,lmodel,ltokenizer):
try:
eval_prompt_pt1 = "\nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: "
eval_prompt_pt2="\n\n\n### Response:\n"
eval_prompt=eval_prompt_pt1+input_text+eval_prompt_pt2
print(eval_prompt,"\n\n")
print("BEFORE PROCESSING MODEL INPUT")
model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cpu")
print(" BEFORE EVAL LMODEL")
lmodel.eval()
print("BEFORE DOING TORCH.NO_GRAD()")
with torch.no_grad():
print("BEFORE RETURNING")
return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True))
#return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
except Exception as error:
print("Exception {error}".format(error = error))
#txt1 = "My name is {fname}, I'm {age}".format(fname = "John", age = 36)
def mental_chat(message, history):
print("BEFORE CALLING GIVETEXT")
return givetext(message,newmodel,newtokenizer)
demo = gr.ChatInterface(mental_chat)
demo.launch() #
"""
import gradio as gr
import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
peft_model_id = "charansr/llama2-7b-chat-hf-therapist"
# Load the Lora model
newmodel = PeftModel.from_pretrained(peft_model_id, use_auth_token="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", device_map="cpu",
model_id=peft_model_id)
newtokenizer = AutoTokenizer.from_pretrained(peft_model_id, use_auth_token="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")
def givetext(input_text, lmodel, ltokenizer):
eval_prompt_pt1 = \nBelow is an instruction that describes a task. Write a response that appropriately completes the request.\n### Instruction: Act like a therapist and respond\n\n### Input: "
eval_prompt_pt2 = "\n\n\n### Response:\n"
eval_prompt = eval_prompt_pt1 + input_text + eval_prompt_pt2
print(eval_prompt, "\n\n")
model_input = ltokenizer(eval_prompt, return_tensors="pt").to("cuda")
lmodel.eval()
with torch.no_grad():
return ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=1000)[0], skip_special_tokens=True)
def mental_chat(message, history):
return givetext(message, newmodel, newtokenizer)
demo = gr.ChatInterface(mental_chat)
demo.launch()
""" |