Instructions to use Hanlard/Pangu_alpha with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Hanlard/Pangu_alpha with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Hanlard/Pangu_alpha", trust_remote_code=True)# Load model directly from transformers import AutoModelForCausalLM model = AutoModelForCausalLM.from_pretrained("Hanlard/Pangu_alpha", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Hanlard/Pangu_alpha with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Hanlard/Pangu_alpha" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hanlard/Pangu_alpha", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Hanlard/Pangu_alpha
- SGLang
How to use Hanlard/Pangu_alpha 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 "Hanlard/Pangu_alpha" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hanlard/Pangu_alpha", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'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 "Hanlard/Pangu_alpha" \ --host 0.0.0.0 \ --port 30000 # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:30000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Hanlard/Pangu_alpha", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Hanlard/Pangu_alpha with Docker Model Runner:
docker model run hf.co/Hanlard/Pangu_alpha
| # from tokenization_gptpangu import GPTPanguTokenizer | |
| # import json | |
| # | |
| # tokenizer = GPTPanguTokenizer.from_pretrained(".") | |
| # with open("tokenizer.json",encoding="utf-8") as f: | |
| # cofig = json.load(f) | |
| # | |
| # | |
| # vocab_file = "vocab.vocab" | |
| # | |
| # f = open(vocab_file, 'r', encoding="utf-8") | |
| # lines = f.readlines() | |
| # vocab = [] | |
| # for line in enumerate(lines): | |
| # key = line[1].split('\t')[0] | |
| # pair = [key,line[0]] | |
| # vocab.append(pair) | |
| # | |
| # cofig['model']['vocab'] = vocab | |
| # | |
| # with open("new_tokenizer.json","w",encoding="utf-8") as w: | |
| # d = json.dumps(cofig) | |
| # w.write(d) | |
| # | |
| # print("ok") | |
| from transformers import AutoTokenizer | |
| tokenizer = AutoTokenizer.from_pretrained(".") | |