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ICONNAI
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ICONNAI
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ICONN e1, being a MoE just like it's base model ICONN 1, has multiple expert models. Keywords are taken from the user's input to choose which expert generates the output.
| Expert Chosen | User Input |
|---|---|
| ICONN-e1 | 'Hi!' |
| ICONN-e1-Pro | Solve for m: m² − (2 + ∑₍ⱼ₌₁₎² j)·m + (1 + ∑₍ⱼ₌₁₎³ j² − 14) = 0. |
| ICONN-e1-Science | If a stable isotope of Ununoctium (Uuo, now Og) could be synthesized in bulk, what would be its most likely physical state at STP and why, considering relativistic effects? |
| ICONN-e1-Code | Create a zero-dependency quantum-safe VM in Zig that compiles a domain-specific language into a fully homomorphic encrypted IR, supports hot-reloading WebAssembly modules, parallel scheduling via lock-free fibers, and performs live introspection through a headless OpenGL debug overlay. |
ICONN-e1:
ICONN's general-purpose reasoning model, designed for everyday tasks, logic, and conversation.
ICONN-e1-Pro:
ICONN's advanced reasoning model, optimized for complex problem-solving in math, logic, and professional domains.
ICONN-e1-Science:
ICONN's scientific expert model, trained on advanced science datasets to enhance precision in physics, chemistry, biology, and technical reasoning.
ICONN-e1-Code:
ICONN's coding specialist, trained for programming, compiler theory, software architecture, and technical code generation across multiple languages.
**First, make sure you have at least 4x Nvidia A100 or a single B100, and 120GB RAM and 120-192GB VRAM. Don't have this? Use our Lite model, coming soon.
Run the code below to run ICONN i1:
python
from transformers import AutoTokenizer, AutoModelForCausalLM, pipelineimport torch def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) device = 0 if torch.cuda.is_available() else -1 chat_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, device=device, max_length=1624, do_sample=True, top_p=0.9, temperature=0.4, pad_token_id=tokenizer.eos_token_id ) print(f"ICONN chatbot running with model: {model_name}. Type 'exit' to quit.") conversation_history = "" while True: user_input = input("You: ") if user_input.lower() == "exit": print("Goodbye!") break conversation_history += f"User: {user_input}\nBot:" response = chat_pipeline(conversation_history, max_length=len(tokenizer.encode(conversation_history)) + 100)[0]['generated_text'] bot_reply = response[len(conversation_history):].strip().split("\n")[0] print(f"Bot: {bot_reply}") conversation_history += f" {bot_reply}\n" if __name__ == "__main__": run_iconn_chatbot()If you use ICONN 1, please cite us as follows:
DoI
@misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-e1-Beta (Revision ca41146) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-e1-Beta }, doi = { 10.57967/hf/5861 }, publisher = { Hugging Face }}from transformers import AutoTokenizer, AutoModelForCausalLM, pipelineimport torch def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"): tokenizer = AutoTokenizer.from_pretrained(model_name) model = AutoModelForCausalLM.from_pretrained(model_name) device = 0 if torch.cuda.is_available() else -1 chat_pipeline = pipeline( "text-generation", model=model, tokenizer=tokenizer, device=device, max_length=1624, do_sample=True, top_p=0.9, temperature=0.4, pad_token_id=tokenizer.eos_token_id ) print(f"ICONN chatbot running with model: {model_name}. Type 'exit' to quit.") conversation_history = "" while True: user_input = input("You: ") if user_input.lower() == "exit": print("Goodbye!") break conversation_history += f"User: {user_input}\nBot:" response = chat_pipeline(conversation_history, max_length=len(tokenizer.encode(conversation_history)) + 100)[0]['generated_text'] bot_reply = response[len(conversation_history):].strip().split("\n")[0] print(f"Bot: {bot_reply}") conversation_history += f" {bot_reply}\n" if __name__ == "__main__": run_iconn_chatbot()@misc{iconnai_2025, author = { ICONNAI }, title = { ICONN-e1-Beta (Revision ca41146) }, year = 2025, url = { https://huggingface.co/ICONNAI/ICONN-e1-Beta }, doi = { 10.57967/hf/5861 }, publisher = { Hugging Face }}