ICONNAI/ICONN-1-Mini-Beta

ICONN 1

Introducing ICONN 1 Mini Beta, a cutting-edge open-source AI model with just 7 billion parameters — designed for natural, human-like language understanding and generation. Despite its compact size, it delivers powerful performance through efficient architecture and careful tuning. ICONN 1 Mini Beta represents the next step in accessible, conversational AI.

Developed entirely from scratch, ICONN-1-Mini-Beta is based on a new ICONN framework and comprises 7 billion parameters.

ICONN-1 is released in three distinct forms to serve different application needs:

• ICONN-1-Mini-Beta(This model) is a small 7B model trained for a lightweight alternative to ICONN 1.
• ICONN-1 is optimized for natural, emotionally resonant, and conversational interactions.
• ICONN-e1 is a specialized variant of the model fine-tuned for advanced reasoning, critical analysis, and complex problem-solving.

Together, these models represent a major leap forward in the evolution of AI systems—demonstrating not only deep reasoning but also a commitment to openness, accessibility, and human-aligned intelligence.

Usage

To run ICONN 1 Mini Beta, you need:

• Any hardware - CPU or GPU; Just make sure you have about 15GB storage space!

Run the code below to run ICONN 1 Mini Beta:

import os

import torch

from transformers import AutoModelForCausalLM, AutoTokenizer, TextIteratorStreamer

from threading import Thread

  

model_id = "ICONNAI/ICONN-1-Mini-Beta"

  

try:

model = AutoModelForCausalLM.from_pretrained(

model_id, torch_dtype=torch.float16, device_map="auto", trust_remote_code=True

)

tokenizer = AutoTokenizer.from_pretrained(model_id)

except Exception as e:

exit(f"Exiting due to model loading error: {e}")

  

def generate_response(

message:  str,

max_new_tokens:  int = 2048,

temperature:  float = 0.7,

top_p:  float = 0.9,

top_k:  int = 50,

repetition_penalty:  float = 1.2,

) -> str:

conversation = [{"role":  "user",  "content": message}]

  

try:

input_ids = tokenizer.apply_chat_template(

conversation, return_tensors="pt", enable_thinking=True

)

except Exception as e:

return  f"Error applying chat template: {e}"

  

input_ids = input_ids.to(model.device)

  

streamer = TextIteratorStreamer(tokenizer, timeout=20.0, skip_prompt=True, skip_special_tokens=True)

  

adjusted_top_k = int(max(1, top_k))

  

generate_kwargs = dict(

{"input_ids": input_ids},

streamer=streamer,

max_new_tokens=max_new_tokens,

do_sample=True,

top_p=top_p,

top_k=adjusted_top_k,

temperature=temperature,

num_beams=1,

repetition_penalty=repetition_penalty,

)

  

try:

t = Thread(target=model.generate, kwargs=generate_kwargs)

t.start()

except Exception as e:

return  f"Error starting generation thread: {e}"

  

outputs = []

for text in streamer:

outputs.append(text)

return  "".join(outputs)

  

if  __name__ == "__main__":

question = "Can you explain briefly to me what is the Python programming language?"

print(f"User Question: {question}")

  

response = generate_response(question)

print(f"Bot Response: {response}")