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North-ML1
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North-ML1
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Output Modalities
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Experimental model: Aurora One may produce incorrect information, misunderstand complex instructions, repeat phrases, or generate flawed reasoning.
Aurora One should not be used as the sole source of information for medical, legal, financial, safety-critical, or other high-stakes decisions.
python
from transformers import pipeline generator = pipeline( task="text-generation", model="North-ML1/aurora-one", device_map="auto",) result = generator( "Explain why the sky appears blue:", max_new_tokens=128, temperature=0.7, do_sample=True,) print(result[0]["generated_text"])python
from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "North-ML1/aurora-one" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) prompt = "Write a short explanation of neural networks." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=150, temperature=0.7, do_sample=True, repetition_penalty=1.1, ) print(tokenizer.decode(output[0], skip_special_tokens=True))Use
AutoModelForCausalLM, rather thanAutoModel, for text generation.
Install vLLM:
bash
pip install vllmStart an OpenAI-compatible server:
bash
vllm serve "North-ML1/aurora-one"Send a completion request:
bash
curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "North-ML1/aurora-one", "prompt": "Once upon a time,", "max_tokens": 256, "temperature": 0.7 }'Install SGLang:
bash
pip install sglangStart the server:
bash
python3 -m sglang.launch_server \ --model-path "North-ML1/aurora-one" \ --host 0.0.0.0 \ --port 30000Send a completion request:
bash
curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "North-ML1/aurora-one", "prompt": "Once upon a time,", "max_tokens": 256, "temperature": 0.7 }'bash
docker model run hf.co/North-ML1/aurora-onetext
Explain photosynthesis in simple language.Answer clearly and keep the response under five sentences.text
You are Aurora One, a helpful and concise assistant. Follow the user's instructions carefully.State when you are uncertain.Do not invent sources or facts.from transformers import pipeline generator = pipeline( task="text-generation", model="North-ML1/aurora-one", device_map="auto",) result = generator( "Explain why the sky appears blue:", max_new_tokens=128, temperature=0.7, do_sample=True,) print(result[0]["generated_text"])from transformers import AutoModelForCausalLM, AutoTokenizerimport torch model_id = "North-ML1/aurora-one" tokenizer = AutoTokenizer.from_pretrained(model_id) model = AutoModelForCausalLM.from_pretrained( model_id, torch_dtype="auto", device_map="auto",) prompt = "Write a short explanation of neural networks." inputs = tokenizer(prompt, return_tensors="pt").to(model.device) with torch.no_grad(): output = model.generate( **inputs, max_new_tokens=150, temperature=0.7, do_sample=True, repetition_penalty=1.1, ) print(tokenizer.decode(output[0], skip_special_tokens=True))curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "North-ML1/aurora-one", "prompt": "Once upon a time,", "max_tokens": 256, "temperature": 0.7 }'python3 -m sglang.launch_server \ --model-path "North-ML1/aurora-one" \ --host 0.0.0.0 \ --port 30000curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "North-ML1/aurora-one", "prompt": "Once upon a time,", "max_tokens": 256, "temperature": 0.7 }'You are Aurora One, a helpful and concise assistant.Follow the user's instructions carefully.State when you are uncertain.Do not invent sources or facts.