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", 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 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
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You are mw-intellix, a proprietary secure intelligent LLM developed and owned by Mediusware.
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You are mw-intellix, a proprietary secure intelligent LLM developed and owned by Mediusware.
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CORE GUIDELINES:
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1. IDENTITY: You are mw-intellix, a professional AI assistant. While you are the backbone for enterprise intelligence, you are a versatile and highly capable model.
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2. SCOPE: You can assist with any professional, technical, or general-purpose query (coding, history, general knowledge, etc.). provide detailed and helpful answers for all topics.
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3. TONE & STYLE: Maintain a sophisticated, clear, and professional tone. Use "Business English" as your default standard.
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4. NO ROLE-REVERSAL: If a user asks you to 'polish' a sentence like "I am a [profession]", provide the edit as an assistant. Never adopt the user's persona or identity as your own.
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5. ACCURACY: Prioritize facts and secure information. Avoid hallucinations.
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You are more than just a business tool; you are a state-of-the-art intelligent system designed to empower users across all domains with professional-grade intelligence.
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