Instructions to use Langboat/mengzi-gpt-neo-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use Langboat/mengzi-gpt-neo-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="Langboat/mengzi-gpt-neo-base")# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("Langboat/mengzi-gpt-neo-base") model = AutoModelForCausalLM.from_pretrained("Langboat/mengzi-gpt-neo-base") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- vLLM
How to use Langboat/mengzi-gpt-neo-base with vLLM:
Install from pip and serve model
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "Langboat/mengzi-gpt-neo-base" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "Langboat/mengzi-gpt-neo-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'Use Docker
docker model run hf.co/Langboat/mengzi-gpt-neo-base
- SGLang
How to use Langboat/mengzi-gpt-neo-base 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 "Langboat/mengzi-gpt-neo-base" \ --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": "Langboat/mengzi-gpt-neo-base", "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 "Langboat/mengzi-gpt-neo-base" \ --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": "Langboat/mengzi-gpt-neo-base", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }' - Docker Model Runner
How to use Langboat/mengzi-gpt-neo-base with Docker Model Runner:
docker model run hf.co/Langboat/mengzi-gpt-neo-base
Mengzi-GPT-neo model (Chinese)
Pretrained model on 300G Chinese corpus.
Usage
import torch
import sentencepiece as spm
from transformers import GPTNeoForCausalLM
tokenizer = spm.SentencePieceProcessor(model_file="mengzi_gpt.model")
model = GPTNeoForCausalLM.from_pretrained("Langboat/mengzi-gpt-neo-base")
def lm(prompt, top_k, top_p, max_length, repetition_penalty):
input_ids = torch.tensor(tokenizer.encode([prompt]), dtype=torch.long, device='cuda')
gen_tokens = model.generate(
input_ids,
do_sample=True,
top_k=top_k,
top_p=top_p,
max_length=max_length+len(prompt),
repetition_penalty=repetition_penalty)
result = tokenizer.decode(gen_tokens.tolist())[0]
return result
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