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license: mit |
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--- |
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Experiment with distilabel: |
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```python |
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dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train", token=hf_token) |
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dataset = dataset.filter(lambda r: r["status"]!="tie" and r["chosen_score"]>5) |
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def chatml_format(example): |
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# Format system |
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if len(example['system']) > 0: |
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message = {"role": "system", "content": example['system']} |
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system = tokenizer.apply_chat_template([message], tokenize=False) |
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else: |
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system = "" |
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# Format instruction |
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message = {"role": "user", "content": example['input']} |
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prompt = tokenizer.apply_chat_template([message], tokenize=False, add_generation_prompt=True) |
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# Format chosen answer |
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chosen = example['chosen'] + "<|im_end|>\n" |
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# Format rejected answer |
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rejected = example['rejected'] + "<|im_end|>\n" |
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return { |
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"prompt": system + prompt, |
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"chosen": chosen, |
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"rejected": rejected, |
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} |
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# Load dataset |
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#dataset = load_dataset("Intel/orca_dpo_pairs")['train'] |
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# Save columns |
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original_columns = dataset.column_names |
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# Tokenizer |
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tokenizer = AutoTokenizer.from_pretrained(model_name) |
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tokenizer.pad_token = tokenizer.eos_token |
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tokenizer.padding_side = "left" |
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# Format dataset |
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dataset = dataset.map( |
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chatml_format, |
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remove_columns=original_columns |
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) |
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# Print sample |
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dataset[1] |
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``` |
