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 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.

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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
• Fine-tuning method: Supervised fine-tuning on prompt–response pairs.

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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

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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

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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

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Usage

Load and generate text

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))