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# Install required packages first:
# pip install torch transformers safetensors
import torch
from transformers import AutoTokenizer, AutoModelForCausalLM
# -----------------------------
# 1️⃣ Load the trained model
# -----------------------------
model_path = "./mini_gpt_safetensor" # folder where model was saved
print("📥 Loading model and tokenizer...")
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.eos_token # GPT models don't have pad_token
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto") # load model
# -----------------------------
# 2️⃣ Generate text
# -----------------------------
def generate_text(prompt, max_length=50):
# Tokenize prompt
input_ids = tokenizer(prompt, return_tensors="pt").input_ids
input_ids = input_ids.to(model.device)
# Generate text
output_ids = model.generate(
input_ids,
max_length=max_length,
do_sample=True, # for randomness
top_k=50, # sample from top 50 tokens
top_p=0.95, # nucleus sampling
temperature=0.7,
num_return_sequences=1
)
# Decode output
output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
return output_text
# -----------------------------
# 3️⃣ Test generation
# -----------------------------
prompt = "Hello, I am training a mini GPT model"
generated_text = generate_text(prompt, max_length=50)
print("\n📝 Generated text:")
print(generated_text)
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