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--- |
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language: |
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- en |
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license: apache-2.0 |
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library_name: transformers |
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tags: |
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- axolotl |
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pipeline_tag: summarization |
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--- |
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--- |
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Qwen2-1.5B-Instruct finetuned on my own synthetic data for summarization task for 2 epochs |
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More info on the project at my github: https://github.com/thepowerfuldeez/qwen2_1_5b_summarize |
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### Usage |
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```python |
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import torch |
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from transformers import AutoTokenizer, AutoModelForCausalLM |
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tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-1.5B-Instruct") |
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model = AutoModelForCausalLM.from_pretrained("thepowerfuldeez/Qwen2-1.5B-Summarize", |
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bnb_4bit_compute_dtype=torch.bfloat16, |
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load_in_4bit=True, attn_implementation="flash_attention_2") |
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text = <YOUR_TEXT> |
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messages = [ |
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{"role": "system", "content": "You are helpful AI assistant."}, |
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{"role": "user", "content": f"Summarize following text: \n{text}"}, |
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] |
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input_ids = tokenizer.apply_chat_template(messages, return_tensors='pt') |
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new_tokens = model.generate(input_ids, max_new_tokens=1024)[0][len(input_ids[0]):] |
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summary = tokenizer.decode(new_tokens, skip_special_tokens=True) |
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``` |
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### Dataset |
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Train split is [here](https://huggingface.co/datasets/thepowefuldeez/Qwen-summarize-dataset-train) |
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### Metrics |
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#### BERTScore |
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|Model name | Dataset size | Result | |
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| ------------------ | ------------ | ---------- | |
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|Qwen2-1.5B-Instruct | - | 0.07 | |
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|Qwen2-1.5B-Summarize| 8000 | **0.14** | |
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|Qwen2-1.5B-Summarize| 20500 | In progress| |
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I have used BERTScore from [official](https://github.com/Tiiiger/bert_score/tree/master) implementation with `microsoft/deberta-xlarge-mnli` model. |
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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. |
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[<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) |
