Instructions to use Undi95/Lumimaid-Magnum-12B with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Undi95/Lumimaid-Magnum-12B with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Undi95/Lumimaid-Magnum-12B") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Undi95/Lumimaid-Magnum-12B") model = AutoModelForCausalLM.from_pretrained("Undi95/Lumimaid-Magnum-12B") 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]:])) - Inference
- Notebooks
- Google Colab
- Kaggle
- Local Apps
- vLLM
How to use Undi95/Lumimaid-Magnum-12B with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Undi95/Lumimaid-Magnum-12B" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Undi95/Lumimaid-Magnum-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/Undi95/Lumimaid-Magnum-12B
- SGLang
How to use Undi95/Lumimaid-Magnum-12B 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 "Undi95/Lumimaid-Magnum-12B" \ --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": "Undi95/Lumimaid-Magnum-12B", "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 "Undi95/Lumimaid-Magnum-12B" \ --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": "Undi95/Lumimaid-Magnum-12B", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use Undi95/Lumimaid-Magnum-12B with Docker Model Runner:
docker model run hf.co/Undi95/Lumimaid-Magnum-12B
Magnum v4
Lumimaid-Magnum has been a favorite of mine for a long time.
Would you consider a new version using the new Magnum v4, since it shares the same Mistral prompt with Lumi? magnum v2 used ChatML.
Not the author, but I believe this was made with Mini-Magnum 12b v1.1, which does use Mistral formatting.
Not the author, but I believe this was made with Mini-Magnum 12b v1.1, which does use Mistral formatting.
Mini-magnum 12b v1.1 has been out for 5 months and Magnum v4 is 2 months old.
Sure, I'm not saying Undi shouldn't do an alternate version. It would be interesting to compare.
I will do that if that interest some people, sure!
It's just a merge.
Hello, I'm currently doing the exact same merge, replacing the mini v1.1 with v4.
I will upload the model when done and make some basic GGUF.
Also, I invite you to try out my "Sushi" model, if you have enough power to run it, I would be happy if you do and post feedback.
Have a nice day!
Thank you!