Model Card for Model ID
1import torch
2from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
3
4# Set a manual seed for reproducibility
5torch.manual_seed(0)
6
7# Load the model with specific configurations
8model = AutoModelForCausalLM.from_pretrained(
9 "AlanYky/phi-3.5_tweets_instruct",
10 device_map="cuda",
11 torch_dtype="auto",
12 trust_remote_code=True
13)
14model.to("cuda")
15
16# Load the tokenizer
17tokenizer = AutoTokenizer.from_pretrained("microsoft/Phi-3.5-mini-instruct")
18
19# Define a function to generate tweets
20def generate_tweet(instruction, pipe, generation_args):
21 """
22 Generate a tweet response based on an instruction.
23 """
24 # Define the message structure
25 messages = [
26 {
27 "role": "user",
28 "content": instruction
29 }
30 ]
31
32 # Generate the tweet response
33 output = pipe(messages, **generation_args)
34
35 # Extract and return the generated tweet text
36 return output[0]['generated_text']
37
38# Set up the pipeline for text generation
39pipe = pipeline(
40 "text-generation",
41 model=model,
42 tokenizer=tokenizer,
43)
44
45# Define generation arguments for tweet creation
46generation_args = {
47 "max_new_tokens": 70,
48 "return_full_text": False,
49 "temperature": 0.4,
50 "top_k": 50,
51 "top_p": 0.9,
52 "repetition_penalty": 1.2,
53 "do_sample": True,
54}
55
56# Specify an instruction for tweet generation
57instruction = "Generate a tweet about Donald Trump is the 2024 US President."
58generated_tweet = generate_tweet(instruction, pipe, generation_args)
59print(generated_tweet)
60
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