library_name: transformers
license: mit
base_model: gpt2
tags:
- fine-tuned
- conversational
- chat
- transformers
- huggingface
model-index:
- name: aichatpro
  results: []
---

# aichatpro

**aichatpro** is a fine-tuned version of [gpt2](https://huggingface.co/gpt2) designed for conversational AI tasks.  
It was trained using a custom dataset of user prompts and assistant responses to improve dialogue quality and make GPT-2 respond more naturally in chatbot scenarios.

---

## Model description

- **Model type**: Causal Language Model (GPT-2 architecture)  
- **Language**: English *(can be adapted if dataset contains other languages)*  
- **Purpose**: Conversational AI, chatbot systems, and interactive assistants.  
- **Base model**: [gpt2](https://huggingface.co/gpt2)  
- **Fine-tuning method**: Supervised fine-tuning on prompt–response pairs.  

---

## Intended uses & limitations

### Intended uses
- Building chatbots
- Interactive Q&A systems
- Prototyping conversational agents

### Limitations
- May produce incorrect or nonsensical answers
- May reproduce biases from GPT-2 or training data
- Not optimized for factual accuracy or real-time decision-making

---

## Training and evaluation data

The dataset was built from structured conversation logs, pairing **user prompts** with **assistant responses**.  
Preprocessing steps included:
- Sorting messages by timestamp
- Pairing user → assistant turns
- Filtering out entries with bug/error-related keywords

---

## Training procedure

### Training hyperparameters
- **Learning rate**: 5e-05  
- **Train batch size**: 4  
- **Eval batch size**: 8  
- **Seed**: 42  
- **Optimizer**: AdamW (betas=(0.9, 0.999), epsilon=1e-08)  
- **Scheduler**: Linear decay  
- **Epochs**: 3  

### Hardware
- CPU / GPU supported  
- Model fine-tuned using Hugging Face `Trainer` API  

---

## Usage

### Load and generate text
```python
from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "YourUsername/aichatpro"  # replace with your HF username/repo
tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name)

prompt = "Hello! How are you today?"
inputs = tokenizer(prompt, return_tensors="pt")
outputs = model.generate(**inputs, max_length=50)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))