Text Generation
Transformers
Safetensors
English
asterisk
aspp
pi-flow
hybrid-architecture
graph-reasoning
probability-flow
sft
trl
conversational
custom_code
Instructions to use NoesisLab/Asterisk-Pi-135M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use NoesisLab/Asterisk-Pi-135M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="NoesisLab/Asterisk-Pi-135M", trust_remote_code=True) messages = [ {"role": "user", "content": "Who are you?"}, ] pipe(messages)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("NoesisLab/Asterisk-Pi-135M", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use NoesisLab/Asterisk-Pi-135M with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "NoesisLab/Asterisk-Pi-135M" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/chat/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "NoesisLab/Asterisk-Pi-135M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }'Use Docker
docker model run hf.co/NoesisLab/Asterisk-Pi-135M
- SGLang
How to use NoesisLab/Asterisk-Pi-135M 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 "NoesisLab/Asterisk-Pi-135M" \ --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": "NoesisLab/Asterisk-Pi-135M", "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 "NoesisLab/Asterisk-Pi-135M" \ --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": "NoesisLab/Asterisk-Pi-135M", "messages": [ { "role": "user", "content": "What is the capital of France?" } ] }' - Docker Model Runner
How to use NoesisLab/Asterisk-Pi-135M with Docker Model Runner:
docker model run hf.co/NoesisLab/Asterisk-Pi-135M
| {% for message in messages %}{% if loop.first and messages[0]['role'] != 'system' %}{{ '<|im_start|>system | |
| You are a helpful AI assistant named Asterisk, trained by NoesisLab<|im_end|> | |
| ' }}{% endif %}{{'<|im_start|>' + message['role'] + ' | |
| ' + message['content'] + '<|im_end|>' + ' | |
| '}}{% endfor %}{% if add_generation_prompt %}{{ '<|im_start|>assistant | |
| ' }}{% endif %} |