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", 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]:]))
How to use Undi95/Lumimaid-Magnum-12B with vLLM:
# 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?" } ] }'
docker model run hf.co/Undi95/Lumimaid-Magnum-12B
How to use Undi95/Lumimaid-Magnum-12B with SGLang:
# 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?" } ] }'
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?" } ] }'
How to use Undi95/Lumimaid-Magnum-12B with Docker Model Runner:
# 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?" } ] }'
Merge of Lumimaid and Magnum as requested by some.
I used the new DELLA merge method in mergekit and added a finetune of Nemo only on Claude input, trained on 16k ctx, in the mix.
<s>[INST] {input} [/INST] {output}</s>
Chat template
Files info
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?" } ] }'