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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): | |
| 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(patienttext,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() | |
| """ |