aekpic877 commited on
Commit
322a100
1 Parent(s): 9b71e10

Update app.py

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Files changed (1) hide show
  1. app.py +15 -4
app.py CHANGED
@@ -1,18 +1,29 @@
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  import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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  model_name = "deepseek-ai/deepseek-math-7b-instruct"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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  model.generation_config = GenerationConfig.from_pretrained(model_name)
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  model.generation_config.pad_token_id = model.generation_config.eos_token_id
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- messages = [
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- {"role": "user", "content": "what is the integral of x^2 from 0 to 2?\nPlease reason step by step, and put your final answer within \\boxed{}."}
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- ]
 
 
 
 
 
 
 
 
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  input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
 
 
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  outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
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  result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
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  print(result)
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-
 
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  import torch
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  from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
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+ # Specify the model and tokenizer
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  model_name = "deepseek-ai/deepseek-math-7b-instruct"
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  tokenizer = AutoTokenizer.from_pretrained(model_name)
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  model = AutoModelForCausalLM.from_pretrained(model_name, torch_dtype=torch.bfloat16, device_map="auto")
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  model.generation_config = GenerationConfig.from_pretrained(model_name)
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  model.generation_config.pad_token_id = model.generation_config.eos_token_id
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+ # Function to read text from a file
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+ def read_input_text(file_path):
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+ with open(file_path, 'r', encoding='utf-8') as file:
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+ text = file.read()
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+ return text.strip()
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+
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+ # Example usage: Replace 'input.txt' with your file path
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+ input_text = read_input_text('input.txt')
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+
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+ # Prepare input as a chat message
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+ messages = [{"role": "user", "content": input_text}]
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  input_tensor = tokenizer.apply_chat_template(messages, add_generation_prompt=True, return_tensors="pt")
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+
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+ # Generate outputs from the model
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  outputs = model.generate(input_tensor.to(model.device), max_new_tokens=100)
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+ # Decode the generated output
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  result = tokenizer.decode(outputs[0][input_tensor.shape[1]:], skip_special_tokens=True)
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  print(result)