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functions.py

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+# Load model directly
+from transformers import AutoTokenizer, AutoModelForCausalLM
+import torch
+
+# Model Info
+# model_path = '/Users/heykalsayid/Desktop/skill-academy/projects/ai-porto/deployment/app/model/eleutherai-finetuned'
+model_path_hf = 'paacamo/EleutherAI-pythia-1b-finetuned-nvidia-faq'
+
+tokenizer = AutoTokenizer.from_pretrained(model_path_hf)
+model = AutoModelForCausalLM.from_pretrained(model_path_hf)
+
+def text_generation(text, model=model, tokenizer=tokenizer, max_input_token=300, max_output_token=100):
+  # Tokenize
+  tokenizer.truncation_side = 'left'
+  input_encoded = tokenizer(
+      text,
+      return_tensors='pt',
+      padding=True,
+      truncation=True,
+      max_length=max_input_token
+  )
+
+  # set attention mask to the output
+  input_ids = input_encoded['input_ids']
+  attention_mask = input_encoded['attention_mask']
+
+  # generate
+  device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+  model.to(device)
+
+  output_ids = model.generate(
+      input_ids=input_ids.to(device),
+      attention_mask=attention_mask.to(device),
+      max_new_tokens=max_output_token,
+      pad_token_id=tokenizer.eos_token_id,
+      do_sample=True,
+      top_p=0.95,
+      temperature=0.7
+  )
+
+  # decode
+  generated_text_answer = tokenizer.decode(
+    output_ids[0][input_ids.shape[-1]:], skip_special_tokens=True
+  )
+  return generated_text_answer
+