thepowerfuldeez/Qwen2-1.5B-Summarize

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Qwen2-1.5B-Instruct finetuned on my own synthetic data for summarization task for 2 epochs

More info on the project at my github: https://github.com/thepowerfuldeez/qwen2_1_5b_summarize

Usage

import torch
from transformers import AutoTokenizer, AutoModelForCausalLM

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct")
model = AutoModelForCausalLM.from_pretrained("thepowerfuldeez/Qwen2-1.5B-Summarize", 
                                             bnb_4bit_compute_dtype=torch.bfloat16,
                                             load_in_4bit=True, attn_implementation="flash_attention_2")

text = <YOUR_TEXT>
messages = [
    {"role": "system", "content": "You are helpful AI assistant."},
    {"role": "user", "content": f"Summarize following text: \n{text}"},
]
input_ids = tokenizer.apply_chat_template(messages, return_tensors='pt')
new_tokens = model.generate(input_ids, max_new_tokens=1024)[0][len(input_ids[0]):]
summary = tokenizer.decode(new_tokens, skip_special_tokens=True)

Dataset

Train split is here

Metrics

BERTScore

Model name	Dataset size	Result
Qwen2-1.5B-Instruct	-	0.07
Qwen2-1.5B-Summarize	8000	0.14
Qwen2-1.5B-Summarize	20500	In progress

I have used BERTScore from official implementation with microsoft/deberta-xlarge-mnli model. Then I sampled 32 inputs from test set (longer sentences to summarize) and generated summaries. I have reference summaries generated from stronger, Qwen2-72B-Instruct model, which I used as targets for metric.