| 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)) |
| |
