README.md

---
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:

```bash
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:

```python
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:

```python
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:

```python
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:

```css
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.