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1from transformers import AutoTokenizer, AutoModelForCausalLM
2model_name = "ShashiVish/llama-7b-merged-int4-r512-cover-letter"
3tokenizer = AutoTokenizer.from_pretrained(model_name)
4model = AutoModelForCausalLM.from_pretrained(model_name)
5
6
7model = model.to('cuda')
8
9job_title = "Senior Java Developer"
10preferred_qualification = "3+ years of Java, Spring Boot"
11hiring_company_name = "Google"
12user_name = "Emily Evans"
13past_working_experience= "Java Developer at XYZ for 4 years"
14current_working_experience = "Senior Java Developer at ABC for 1 year"
15skilleset= "Java, Spring Boot, Microservices, SQL, AWS"
16qualification = "Master's in Electronics Science"
17
18item = {'job_title': "Senior Java Developer", 'preferred_qualification': "5+ years of Java, Spring Boot",
19 'hiring_company_name': "Netflix", 'user_name': "Emily Evans",
20 'past_working_experience': "Java Developer at XYZ for 4 years",
21 'current_working_experience': "Senior Java Developer at ABC for 1 year",
22 'skilleset': "Java, Spring Boot, Microservices, SQL, AWS",
23 'qualification': "Master's in Computer Science"}
24
25prompt = f"""### Instruction:
26You are a smart cover letter generator. Use following Input to generate Cover letter.
27
28### Input:
29Role: item['job_title'], Preferred Qualifications: {item['preferred_qualification']}, \
30 Hiring Company: {item['hiring_company_name']}, User Name: {item['user_name']}, \
31 Past Working Experience: {item['past_working_experience']}, \
32 Current Working Experience: {item['current_working_experience']}, \
33 Skillsets: {item['skilleset']}, Qualifications: {item['qualification']}
34
35### Cover Letter:
36"""
37
38input_ids = tokenizer(prompt, return_tensors="pt", truncation=True).input_ids.cuda()
39outputs = model.generate(input_ids=input_ids, max_new_tokens=512, do_sample=True, top_p=0.9,temperature=0.9)
40#model_response = tokenizer.batch_decode(outputs.detach().cpu().numpy(), skip_special_tokens=True)[0][len(prompt):]
41model_response = tokenizer.batch_decode(outputs, skip_special_tokens=True)[0][len(prompt):]
42
43print(model_response)
44