metadata
library_name: transformers
tags:
- trl
- sft
license: apache-2.0
datasets:
- Mike0307/alpaca-en-zhtw
language:
- zh
pipeline_tag: text-generation
Download the model
The base-model microsoft/Phi-3-mini-4k-instruct currently relies on
the latest dev-version transformers and torch.
Also, it needs trust_remote_code=True as an argument of the from_pretrained() function.
pip install git+https://github.com/huggingface/transformers
pip install --pre torch torchvision torchaudio --index-url https://download.pytorch.org/whl/nightly/cpu
Additionally, LoRA model requires the peft package.
pip install peft
Now, let's start to download the model.
import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
model_id = "Mike0307/Phi-3-mini-4k-instruct-chinese-lora"
model = AutoModelForCausalLM.from_pretrained(
model_id,
device_map="mps", # FIX mps if not MacOS
torch_dtype=torch.float32,
trust_remote_code=True,
)
tokenizer = AutoTokenizer.from_pretrained(model_id)
Example of inference
input_text = "<|user|>將這五種動物分成兩組。\n老虎、鯊魚、大象、鯨魚、袋鼠 <|end|>\n<|assistant|>"
inputs = tokenizer(input_text, return_tensors="pt").to(torch.device("mps")) # FIX mps if not MacOS
outputs = model.generate(
**inputs,
temperature = 0.0,
max_length = 500,
do_sample = False
)
generated_text = tokenizer.decode(outputs[0], skip_special_tokens=True)
print(generated_text)