---
language:
- en
license: apache-2.0
library_name: transformers
tags:
- axolotl
pipeline_tag: summarization
---


---
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

```python
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](https://huggingface.co/datasets/thepowefuldeez/Qwen-summarize-dataset-train)

### 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](https://github.com/Tiiiger/bert_score/tree/master) 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"/>](https://github.com/OpenAccess-AI-Collective/axolotl)