Spaces:
Runtime error
Runtime error
File size: 3,577 Bytes
28d9ec7 b3fbb05 28d9ec7 4948643 73f296b 4948643 73f296b eef56d3 fdd85ff a2023d1 81c4ebe e1452b8 28d9ec7 bcad892 82d3d8b a2023d1 bcad892 a2023d1 e5cdfca eac33ce 4948643 a2023d1 28d9ec7 4948643 a2023d1 28d9ec7 81c4ebe 28d9ec7 3f3cf8a 28d9ec7 81c4ebe 99832bd 81c4ebe cf091b6 81c4ebe a2023d1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 |
"""
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):
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))
#return (ltokenizer.decode(lmodel.generate(**model_input, max_new_tokens=100)[0], skip_special_tokens=True))
def mental_chat(message, history):
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()
""" |