Dorami-Instruct

Dorami-Instruct is a Supervised Fine-tuning(SFT) model based on the pretrained model lucky2me/Dorami

Model description

Training data

• wangrui6/Zhihu-KOL

Training code

• dorami

How to use

1. Download model from Hugging Face Hub to local

git lfs install
git clone https://huggingface.co/lucky2me/Dorami-Instruct

2. Use the model downloaded above

from transformers import AutoTokenizer, AutoModelForCausalLM, GenerationConfig
model_path = "The path of the model downloaded above"
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = AutoModelForCausalLM.from_pretrained(model_path)
prompt="fill in any prompt you like."
inputs = tokenizer(prompt, return_tensors="pt")
generation_config = GenerationConfig(max_new_tokens=64, do_sample=True, top_k=2, eos_token_id=model.config.eos_token_id)
outputs = model.generate(**inputs, generation_config=generation_config)
decoded_text = tokenizer.batch_decode(outputs, skip_special_tokens=True)
print(decoded_text)