Instructions to use ICONNAI/ICONN-e1-Beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use ICONNAI/ICONN-e1-Beta with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="ICONNAI/ICONN-e1-Beta")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("ICONNAI/ICONN-e1-Beta")
model = AutoModelForCausalLM.from_pretrained("ICONNAI/ICONN-e1-Beta", device_map="auto")
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]:]))

Notebooks
Google Colab
Kaggle
Local Apps Settings

vLLM

How to use ICONNAI/ICONN-e1-Beta with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "ICONNAI/ICONN-e1-Beta"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICONNAI/ICONN-e1-Beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker

docker model run hf.co/ICONNAI/ICONN-e1-Beta

SGLang

How to use ICONNAI/ICONN-e1-Beta with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "ICONNAI/ICONN-e1-Beta" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICONNAI/ICONN-e1-Beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "ICONNAI/ICONN-e1-Beta" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/chat/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "ICONNAI/ICONN-e1-Beta",
		"messages": [
			{
				"role": "user",
				"content": "What is the capital of France?"
			}
		]
	}'

Docker Model Runner
How to use ICONNAI/ICONN-e1-Beta with Docker Model Runner:
```
docker model run hf.co/ICONNAI/ICONN-e1-Beta
```

Enderchef commited on Jun 17, 2025

Commit

a9650bf

verified ·

1 Parent(s): d056a55

Update README.md

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README.md +5 -5

README.md CHANGED Viewed

@@ -53,18 +53,18 @@ extra_gated_fields:
 pipeline_tag: text-generation
 ---
-# ICONN i1: The new era of Open-Source CoT in AI
 **GPU poor? Less than 3x A100s? A e1 Lite model is coming with just 22B parameters alongside a model for consumer CPUs with 14B and 7B parameters.
 -   **Emotional Context Awareness**
-    ICONN 1 interprets emotional cues and adjusts tone, vocabulary, and response style—offering a more human-like, emotionally reactive experience.
 -   ** ICONN Emotional Core (IEC) (Notice: Not available on Huggingface)**
     Powered by millions of small AI agents, IEC gives ICONN its emotional personality, with billions of simulated emotional states and detections.
 -   **Reasoning**
-    ICONN i1 is one of the most powerful reasoning open-source models, and most closed-source models in or out of Huggingface.
@@ -79,7 +79,7 @@ pipeline_tag: text-generation
 ## ICONN i1 MoE and Experts
-ICONN i1, 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!'`                                                                                                                                                                                                                                                                           |
@@ -109,7 +109,7 @@ ICONN's coding specialist, trained for programming, compiler theory, software ar
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import torch
-def run_iconn_chatbot(model_name="ICONNAI/ICONN-i1"):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)

 pipeline_tag: text-generation
 ---
+# ICONN e1: The new era of Open-Source CoT in AI
 **GPU poor? Less than 3x A100s? A e1 Lite model is coming with just 22B parameters alongside a model for consumer CPUs with 14B and 7B parameters.
 -   **Emotional Context Awareness**
+    ICONN e1 interprets emotional cues and adjusts tone, vocabulary, and response style—offering a more human-like, emotionally reactive experience.
 -   ** ICONN Emotional Core (IEC) (Notice: Not available on Huggingface)**
     Powered by millions of small AI agents, IEC gives ICONN its emotional personality, with billions of simulated emotional states and detections.
 -   **Reasoning**
+    ICONN e1 is one of the most powerful reasoning open-source models, and most closed-source models in or out of Huggingface.
 ## ICONN i1 MoE and Experts
+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!'`                                                                                                                                                                                                                                                                           |
 from transformers import AutoTokenizer, AutoModelForCausalLM, pipeline
 import torch
+def run_iconn_chatbot(model_name="ICONNAI/ICONN-e1"):
     tokenizer = AutoTokenizer.from_pretrained(model_name)
     model = AutoModelForCausalLM.from_pretrained(model_name)