Text Generation
Transformers
Safetensors
mixtral
emotional-ai
ICONN
chatbot
base
conversational
text-generation-inference
Instructions to use ICONNAI/ICONN-e1-Beta with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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
| 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" | |