README.md

metadata

license: mit
datasets:
  - fka/awesome-chatgpt-prompts
language:
  - en
metrics:
  - accuracy
base_model:
  - Qwen/Qwen-Image
new_version: openai/gpt-oss-120b
pipeline_tag: token-classification
library_name: fastai
tags:
  - code

Using a Trained Mini-GPT Model (Safetensors)

This guide explains how to load a trained Mini-GPT model saved in safetensors format and generate text using it.
It is written in a step-by-step manner for learning and understanding.

---

1️⃣ Install Required Packages

Make sure you have the necessary packages:

pip install torch transformers safetensors

2️⃣ Load the Trained Model and Tokenizer

We saved our model earlier in ./mini_gpt_safetensor. Here’s how to load it:

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

model_path = "./mini_gpt_safetensor"  # Path to your saved model

# Load tokenizer
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.eos_token  # GPT models don't have pad_token

# Load model
model = AutoModelForCausalLM.from_pretrained(model_path, device_map="auto")  # auto uses GPU if available

Note: Using device_map="auto" will load the model on GPU if available, otherwise CPU.

3️⃣ Generate Text from a Prompt

Once the model is loaded, we can generate text using a simple function:

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,   # enable randomness
        top_k=50,         # sample from top 50 tokens
        top_p=0.95,       # nucleus sampling
        temperature=0.7,  # creativity factor
        num_return_sequences=1
    )

    # Decode output
    output_text = tokenizer.decode(output_ids[0], skip_special_tokens=True)
    return output_text

Tip:

• do_sample=True → random outputs for creativity

• top_k and top_p → control sampling probability

• temperature → higher value = more creative output

4️⃣ Test Text Generation

Use your function with any prompt:

prompt = "Hello, I am training a mini GPT model"
generated_text = generate_text(prompt, max_length=50)

print("\n📝 Generated text:")
print(generated_text)

Example output:

Hello, I am training a mini GPT model to generate simple sentences about Python, deep learning, and AI projects.

✅ Summary

• Load the tokenizer and model from the safetensors folder.

• Use generate with proper sampling parameters for creative text.

• Decode the output to get readable text.

• You can experiment with prompt, max_length, top_k, top_p, and temperature to control text generation.

• By following this MDX guide, you can easily load any trained Mini-GPT model and generate text interactively.