Stefano Fiorucci PRO

anakin87

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Contributing to Haystack LLM framework ๐Ÿ—๏ธ. Language Models: orchestration, post-training, synthetic data...

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New activity in google/gemma-2-9b 8 days ago

Fine-tuning Hyperparameters

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#27 opened 7 months ago by
tanliboy
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Fine-tune ModernBERT for RAG with Synthetic Data

By sdiazlor โ€ข
โ€ข 28
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๐๐ž๐ฐ ๐ˆ๐ญ๐š๐ฅ๐ข๐š๐ง ๐’๐ฆ๐š๐ฅ๐ฅ ๐‹๐š๐ง๐ ๐ฎ๐š๐ ๐ž ๐Œ๐จ๐๐ž๐ฅ๐ฌ: ๐†๐ž๐ฆ๐ฆ๐š ๐๐ž๐จ๐ ๐ž๐ง๐ž๐ฌ๐ข๐ฌ ๐œ๐จ๐ฅ๐ฅ๐ž๐œ๐ญ๐ข๐จ๐ง ๐Ÿ’Ž๐ŸŒ๐Ÿ‡ฎ๐Ÿ‡น

I am happy to release two new language models for the Italian Language!

๐Ÿ’ช Gemma 2 9B Neogenesis ITA
anakin87/gemma-2-9b-neogenesis-ita
Building on the impressive work by VAGO Solutions, I applied Direct Preference Optimization with a mix of Italian and English data.
Using Spectrum, I trained 20% of model layers.

๐Ÿ“Š Evaluated on the Open ITA LLM leaderboard ( mii-llm/open_ita_llm_leaderboard), this model achieves strong performance.
To beat it on this benchmark, you'd need a 27B model ๐Ÿ˜Ž


๐Ÿค Gemma 2 2B Neogenesis ITA
anakin87/gemma-2-2b-neogenesis-ita
This smaller variant is fine-tuned from the original Gemma 2 2B it by Google.
Through a combination of Supervised Fine-Tuning and Direct Preference Optimization, I trained 25% of the layers using Spectrum.

๐Ÿ“ˆ Compared to the original model, it shows improved Italian proficiency, good for its small size.


Both models were developed during the recent #gemma competition on Kaggle.
๐Ÿ““ Training code: https://www.kaggle.com/code/anakin87/post-training-gemma-for-italian-and-beyond


๐Ÿ™ Thanks @FinancialSupport and mii-llm for the help during evaluation.
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