☕ 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.

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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.

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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

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How to Use

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

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)}")

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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))

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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}},
}


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