metadata

library_name: peft
base_model: meta-llama/Llama-2-7b-chat-hf
license: mit
language:
  - en

Chadgpt Llama2 7b

Colab Example

https://colab.research.google.com/drive/1L_Bcg7YtgO5cmaR5aTxmY3zlblCCDUi-?usp=sharing

Install Prerequisite

!pip install -q git+https://github.com/huggingface/peft.git
!pip install transformers
!pip install -U accelerate
!pip install accelerate
!pip install bitsandbytes # Instal bits and bytes for inference of the model

Login Using Huggingface Token

# You need a huggingface token that can access llama2
!huggingface-cli login

Download Model

import torch
from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer

peft_model_id = "danjie/Chadgpt-Llama2-7b"
config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(config.base_model_name_or_path, return_dict=True, load_in_8bit=True, device_map='auto')
tokenizer = AutoTokenizer.from_pretrained(config.base_model_name_or_path)

# Load the Lora model
model = PeftModel.from_pretrained(model, peft_model_id)

Inference

def talk_with_llm(tweet: str) -> str:
    # Encode and move tensor into cuda if applicable.
    encoded_input = tokenizer(tweet, return_tensors='pt')
    encoded_input = {k: v.to("cuda") for k, v in encoded_input.items()}

    output = model.generate(**encoded_input, max_new_tokens=64)
    response = tokenizer.decode(output[0], skip_special_tokens=True)
    return response

talk_with_llm("<User> Your sentence \n<Assistant>")