README.md

---
language: en
tags:
- coffeechat-ai
- text-generation
- gpt2
- chatbot
- side-project
license: apache-2.0
datasets:
- openwebtext
model-index:
- name: CoffeeChatAI
  results:
  - task:
      type: text-generation
      name: Text Generation
    dataset:
      type: wikitext
      name: WikiText-103
    metrics:
    - type: perplexity
      name: Perplexity
      value: 21.1
co2_eq_emissions: 149200
---

# ☕ CoffeeChatAI

CoffeeChatAI is a lightweight AI based Model English language model.  
It was developed and customized by **Adrian Charles and his team Bluckhut** as a side project, with the goal of making an accessible, branded chatbot-style AI for text generation.

CoffeeChatAI can be used to generate text for creative, academic, or entertainment purposes.

---

## Model Details

- **Developed by:** Adrian Charles & Team Bluckhut  
- **Base model:** (https://huggingface.co/topboykrepta/coffechatai)  
- **Model type:** Transformer-based causal language model   
- **Language:** English  
- **Parameters:** ~1.6M  
- **License:** Apache 2.0  
- **Description:**  
  CoffeeChatAI is a branded and documented, designed to serve as the backbone for the CoffeeChat project.  
  It is compact, fast, and intended for experimentation and educational side projects.

---

## Intended Uses

✅ **Possible Applications**
- Writing assistance (autocompletion, idea generation, grammar help)  
- Creative text generation (stories, poetry, dialogue)  
- Entertainment (chatbots, games, roleplay scenarios)  
- Educational demos (exploring transformers, model compression, and fine-tuning)  

⚠️ **Limitations & Risks**
- May produce **biased, offensive, or inaccurate content**  
- Not suitable for tasks requiring factual correctness (e.g., news, medical, legal advice)  
- Small size = **weaker performance** compared to larger GPT-2/GPT-3 models  

---

## How to Use

You can load and use CoffeeChatAI directly with Hugging Face `transformers`:

```python
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("topboykrepta/CoffeeChatAI")
model = AutoModelForCausalLM.from_pretrained("topboykrepta/CoffeeChatAI")

inputs = tokenizer("Hello, I am CoffeeChat AI,", return_tensors="pt")
outputs = model.generate(**inputs, max_length=30, num_return_sequences=2, do_sample=True)

for i, output in enumerate(outputs):
    print(f"Generated {i+1}: {tokenizer.decode(output, skip_special_tokens=True)}")

---

from transformers import pipeline

generator = pipeline("text-generation", model="topboykrepta/CoffeeChatAI")
print(generator("Hello, I am CoffeeChat AI,", max_length=30, num_return_sequences=2))

---

Or with the Hugging Face pipeline:

If you use this model, please cite:

@misc{CoffeeChatAI2025,
  author = {Adrian Charles and Team Bluckhut},
  title = {CoffeeChatAI: A Tiny Chat Applications},
  year = {2025},
  publisher = {Hugging Face},
  howpublished = {\url{https://huggingface.co/topboykrepta/CoffeeChatAI}},
}


---