NorGPT-3B-continue-conversation-peft

Model Card

NorGPT-3B-continue-conversation-peft is trained on top of NorGPT-3B-continue model on NO-ConvAI2 dataset.

Prompt format:

Human: {prompt} Robot: |||\n {answer}

Inference prompt:

Human: {prompt} Robot: |||\n

Run the Model

from peft import PeftModel, PeftConfig
from transformers import AutoModelForCausalLM, AutoTokenizer
import torch
from tqdm.auto import tqdm

source_model_id = "NorGLM/NorGPT-3B-continue"
peft_model_id = "NorGLM/NorGPT-3B-continue-conversation-peft"

config = PeftConfig.from_pretrained(peft_model_id)
model = AutoModelForCausalLM.from_pretrained(source_model_id, device_map='balanced')

tokenizer_max_len = 2048
tokenizer_config = {'pretrained_model_name_or_path': source_model_id,
                            'max_len': tokenizer_max_len}
tokenizer = tokenizer = AutoTokenizer.from_pretrained(**tokenizer_config)
tokenizer.pad_token = tokenizer.eos_token

model = PeftModel.from_pretrained(model, peft_model_id)

Inference Example

Load the model to evaluate on the test set of NO-CNN/DailyMail dataset:

def load_and_prepare_data_last_prompt(df):
    """ Load and spearates last prompt from prompt """
     # id, turn_id, prompt, answer
    last_prompt = ["Human: " + df['prompt']
                   [i].split("Human:")[-1] for i in range(len(df))]
    df['last_prompt'] = last_prompt
    return df

def generate_text(text, max_length=200):
    # generate with greedy search
    model_inputs = tokenizer(text, return_attention_mask=True, return_tensors="pt",
              padding=True, truncation=True,  max_length=tokenizer_max_len)

    with torch.no_grad():
        output_tokens = model.generate(
                  **model_inputs, max_new_tokens=50, pad_token_id=tokenizer.eos_token_id)

    text_outputs = [tokenizer.decode(
                x, skip_special_tokens=True) for x in output_tokens]

    return text_outputs

print("--LOADING EVAL DATAS---")
eval_data = load_dataset("NorGLM/NO-ConvAI2", data_files="test_PersonaChat_prompt.json")
prompts = eval_data['train']['prompt']
positive_samples = eval_data['train']['answer']

print("--MAKING PREDICTIONS---")
model.eval()

output_file = <output file name>
generated_text = []

for prompt in tqdm(prompts):
    generated_text.append(generate_text(prompt, max_length=tokenizer_max_len))

df = pd.DataFrame({'prompts':prompts, 'generated_text':generated_text, 'positive_sample':positive_samples})

print("Save results to csv file...")
df.to_csv(output_file)

Note

More training details will be released soon!