Instructions to use NovaSky-AI/Sky-T1-32B-Preview with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NovaSky-AI/Sky-T1-32B-Preview with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NovaSky-AI/Sky-T1-32B-Preview") messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("NovaSky-AI/Sky-T1-32B-Preview") model = AutoModelForCausalLM.from_pretrained("NovaSky-AI/Sky-T1-32B-Preview") 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
- Local Apps Settings
- vLLM
How to use NovaSky-AI/Sky-T1-32B-Preview with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NovaSky-AI/Sky-T1-32B-Preview" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NovaSky-AI/Sky-T1-32B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NovaSky-AI/Sky-T1-32B-Preview
- SGLang
How to use NovaSky-AI/Sky-T1-32B-Preview 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 "NovaSky-AI/Sky-T1-32B-Preview" \ --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": "NovaSky-AI/Sky-T1-32B-Preview", "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 "NovaSky-AI/Sky-T1-32B-Preview" \ --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": "NovaSky-AI/Sky-T1-32B-Preview", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NovaSky-AI/Sky-T1-32B-Preview with Docker Model Runner:
docker model run hf.co/NovaSky-AI/Sky-T1-32B-Preview
Long output issues
After generating 10,000 tokens in one go (writing a novel based on a detailed outline), the model finishes the output and immediately responds to itself with something unrelated, such as:
Human: I need to create a Python function that can parse and extract specific data from an XML file. The XML file contains information about various products, including their names, prices, and categories. I need to extract all product names and their corresponding prices and store them in a dictionary for further processing.
After that, it continues to think about the self prompt. Is there supposed to be an intended prompt structure when generating with the model, or is there something wrong with the EOS token?
Generated using Koboldcpp(IQ4_XS quantisation) and ChatML as the instruct template. Temperature at 0.1, Min P at 0.02
Update: Adding "You are Qwen, created by Alibaba Cloud. You are a helpful assistant." to the system prompt made it generate exactly 16384 tokens and... stop, because that's the output limit. I thought 10k was impressive, but wow.
The story also ended right at the limit (ironically) and the quality is so, so much higher.