How to use StarRing2022/MiLu-GPT with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-generation", model="StarRing2022/MiLu-GPT")
# Load model directly from transformers import AutoTokenizer, AutoModelForCausalLM tokenizer = AutoTokenizer.from_pretrained("StarRing2022/MiLu-GPT") model = AutoModelForCausalLM.from_pretrained("StarRing2022/MiLu-GPT", device_map="auto")
How to use StarRing2022/MiLu-GPT with vLLM:
# Install vLLM from pip: pip install vllm # Start the vLLM server: vllm serve "StarRing2022/MiLu-GPT" # Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
docker model run hf.co/StarRing2022/MiLu-GPT
How to use StarRing2022/MiLu-GPT with SGLang:
# Install SGLang from pip: pip install sglang # Start the SGLang server: python3 -m sglang.launch_server \ --model-path "StarRing2022/MiLu-GPT" \ --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": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
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 "StarRing2022/MiLu-GPT" \ --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": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
How to use StarRing2022/MiLu-GPT with Docker Model Runner:
# Install vLLM from pip: pip install vllm
# Start the vLLM server: vllm serve "StarRing2022/MiLu-GPT"
# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'
基于GPT2+BERT的语言模型，以少量的纯中文语料从头训练，验证小模型在ChatGPT类似友好能力
GPT2+BERTokenizer从头训练模型（50W闲聊等语料）
环境： WIN10+Torch1.31+Cuda11.6 transformer4.29
GIT开源地址：https://github.com/StarRing2022/MiLu-GPT/
Install from pip and serve model
# Install vLLM from pip: pip install vllm# Start the vLLM server: vllm serve "StarRing2022/MiLu-GPT"# Call the server using curl (OpenAI-compatible API): curl -X POST "http://localhost:8000/v1/completions" \ -H "Content-Type: application/json" \ --data '{ "model": "StarRing2022/MiLu-GPT", "prompt": "Once upon a time,", "max_tokens": 512, "temperature": 0.5 }'