🔥 Less is more for DPO, high quality matters!
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🔥 Less is more for DPO, high quality matters!
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📢 Dropping our first open dataset and LLM of the year:
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📢 Dropping our first open dataset and LLM of the year:
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💾Meet distilabel Orca Pairs DPO, an improved version of the now famous dataset from Intel:
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💾Meet distilabel Orca Pairs DPO, an improved version of the now famous dataset from Intel:
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https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
argilla/distilabel-intel-orca-dpo-pairs
dataset
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https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
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🏛️ And a new OpenHermes fine-tune outperforming baselines with 54% less DPO pairs:
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🏛️ And a new OpenHermes fine-tune outperforming baselines with 54% less DPO pairs:
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https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B
https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B
link
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You can use this new dataset for your DPO tuning, just like this:
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You can use this new dataset for your DPO tuning, just like this:
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from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# use this:
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r: 
        r["status"] != "tie" and 
        r["chosen_score"] >= 8 and 
        not r["in_gsm8k_train"]
)
```
from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# use this:
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r: 
        r["status"] != "tie" and 
        r["chosen_score"] >= 8 and 
        not r["in_gsm8k_train"]
)
```
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This will reduce the size of the original by 54% while giving you better quality preferences!
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This will reduce the size of the original by 54% while giving you better quality preferences!
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What should we build next?
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What should we build next?
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🔥 Less is more for DPO, high quality matters!

📢 Dropping our first open dataset and LLM of the year:

💾Meet distilabel Orca Pairs DPO, an improved version of the now famous dataset from Intel:

https://huggingface.co/datasets/argilla/distilabel-intel-orca-dpo-pairs
 

🏛️ And a new OpenHermes fine-tune outperforming baselines with 54% less DPO pairs:

https://huggingface.co/argilla/distilabeled-Hermes-2.5-Mistral-7B

You can use this new dataset for your DPO tuning, just like this:


```
from datasets import load_dataset

# Instead of this:
# dataset = load_dataset("Intel/orca_dpo_pairs", split="train")

# use this:
dataset = load_dataset("argilla/distilabel-intel-orca-dpo-pairs", split="train")

dataset = dataset.filter(
    lambda r: 
        r["status"] != "tie" and 
        r["chosen_score"] >= 8 and 
        not r["in_gsm8k_train"]
)
```
This will reduce the size of the original by 54% while giving you better quality preferences!

What should we build next?




❤️
davanstrien osanseviero merve radames tomaarsen mlabonne alielfilali01 clem linoyts KnutJaegersberg Forbu14 lysandre d0rj mammour VictorSanh gblazex sethuiyer eliebak sbrandeis
🤯
davanstrien osanseviero merve radames tomaarsen BramVanroy clem KnutJaegersberg lixin67
👍
hushell
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Blaze
gblazex
user
60420dccc15e823a685f2b03
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Daniel Vila
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