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
```

ICONN-e1-Beta

File size: 2,044 Bytes

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output_model: ./ICONN-1
dtype: bfloat16
moe_type: task
tokenizer_source: models/arcee-blitz

base_model:
  model: models/arcee-blitz
  source_model: arcee-ai/Arcee-Blitz  # base model source repo

experts:
  - name: CodeCONN
    model: models/devstral
    source_model: mistralai/Devstral-Small-2505
    positive_prompts:
      - "python"
      - "java"
      - "c++"
      - "programming"
      - "//"
      - "code"
      - "syntax"
      - "pandas"
      - "sql"
      - "import"
      - "data_cleaning"
      - "script"
      - "bug"
      - "fix"
      - "loop"
      - "json"
      - "csv"
      - "binary"
      - "compiler"
      - "function"
      - "class"

  - name: ChatCONN
    model: models/dolphin3
    source_model: cognitivecomputations/Dolphin3.0-Mistral-24B
    positive_prompts:
      - "hi"
      - "hello"
      - "how are you"
      - "what's up"
      - "can you help"
      - "tell me a joke"
      - "write me"
      - "explain"
      - "talk to me"
      - "describe"
      - "friendly"
      - "respond"
      - "short answer"
      - "summarize"
      - "conversation"
      - "chat"
      - "help me"

  - name: ReasonCONN
    model: models/dolphin3-r1
    source_model: cognitivecomputations/Dolphin3.0-R1-Mistral-24B
    positive_prompts:
      - "think"
      - "logic"
      - "reason"
      - "math"
      - "why"
      - "how"
      - "deduce"
      - "explain in detail"
      - "analyze"
      - "IQ"
      - "SAT"
      - "GRE"
      - "proof"
      - "science"
      - "physics"
      - "biology"
      - "deepen"
      - "calculus"
      - "infer"

  - name: GeoCONN
    model: models/deephermes
    source_model: NousResearch/DeepHermes-3-Mistral-24B-Preview
    positive_prompts:
      - "geography"
      - "continent"
      - "country"
      - "capital"
      - "map"
      - "longitude"
      - "latitude"
      - "earth"
      - "region"
      - "nation"
      - "location"
      - "mountain"
      - "river"
      - "climate"
      - "ocean"
      - "desert"
      - "geological"
      - "atlas"