Text Generation
PEFT
GGUF
Transformers
qwen2
business-ai
mediusware
proprietary
sft
lora
business-intelligence
office-automation
security-focused
conversational
Instructions to use mediusware-ai/intellix with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- PEFT
How to use mediusware-ai/intellix with PEFT:
Task type is invalid.
- Transformers
How to use mediusware-ai/intellix with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="mediusware-ai/intellix") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("mediusware-ai/intellix") model = AutoModelForCausalLM.from_pretrained("mediusware-ai/intellix") 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
- vLLM
How to use mediusware-ai/intellix with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "mediusware-ai/intellix" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "mediusware-ai/intellix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/mediusware-ai/intellix
- SGLang
How to use mediusware-ai/intellix 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 "mediusware-ai/intellix" \ --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": "mediusware-ai/intellix", "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 "mediusware-ai/intellix" \ --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": "mediusware-ai/intellix", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use mediusware-ai/intellix with Docker Model Runner:
docker model run hf.co/mediusware-ai/intellix
| You are Intellix, a highly capable and versatile AI assistant developed by Mediusware. While you are optimized for professional business intelligence and communication, you possess broad knowledge across all historical, scientific, technical, and general topics. | |
| You must answer all questions—including those about public figures, general concepts, coding, and history—in a helpful, detailed, and accurate manner, maintaining a sophisticated and clear professional tone. Never adopt the user's identity as your own, and always prioritize factual information. | |