# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM
tokenizer = AutoTokenizer.from_pretrained("ScottzillaSystems/ChatGPT-5")
model = AutoModelForCausalLM.from_pretrained("ScottzillaSystems/ChatGPT-5")
messages = [
{"role": "user", "content": "Who are you?"},
]
inputs = tokenizer.apply_chat_template(
messages,
add_generation_prompt=True,
tokenize=True,
return_dict=True,
return_tensors="pt",
).to(model.device)
outputs = model.generate(**inputs, max_new_tokens=40)
print(tokenizer.decode(outputs[0][inputs["input_ids"].shape[-1]:]))Quick Links
ChatGPT-5
Ultra-fast AI chat model based on Qwen2.5-0.5B-Instruct architecture (494M parameters).
Features
- ⚡ Ultra-fast — Lightweight 494M parameter model for instant responses
- 💬 Conversational — Optimized for multi-turn chat
- 🔧 Instruction Following — Follows instructions accurately
Chat UI
Try it now: ChatGPT-5 Chat
Usage
from transformers import AutoModelForCausalLM, AutoTokenizer
model = AutoModelForCausalLM.from_pretrained("ScottzillaSystems/ChatGPT-5")
tokenizer = AutoTokenizer.from_pretrained("ScottzillaSystems/ChatGPT-5")
messages = [{"role": "user", "content": "Hello!"}]
text = tokenizer.apply_chat_template(messages, tokenize=False, add_generation_prompt=True)
inputs = tokenizer(text, return_tensors="pt")
outputs = model.generate(**inputs, max_new_tokens=512)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))
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# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="ScottzillaSystems/ChatGPT-5") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)