Qwen2-1.5B-Summarize

---

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

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.

<img src="https://raw.githubusercontent.com/OpenAccess-AI-Collective/axolotl/main/image/axolotl-badge-web.png" alt="Built with Axolotl" width="200" height="32"/>