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💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian
💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian
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Lately, I've spent some time fine-tuning language models.
Lately, I've spent some time fine-tuning language models.
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Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language
Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language
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🔹 Small (3.82 B parameters) but capable model
🔹 Small (3.82 B parameters) but capable model
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🔹 128k context length
🔹 128k context length
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Chat with it on 🤗 Spaces: 
Chat with it on 🤗 Spaces: 
resource
https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
space
anakin87/Phi-3.5-mini-ITA
https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
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Model card: 
Model card: 
resource
https://huggingface.co/anakin87/Phi-3.5-mini-ITA
model
anakin87/Phi-3.5-mini-ITA
https://huggingface.co/anakin87/Phi-3.5-mini-ITA
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🗃️ Data
🗃️ Data
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Supervised fine-tuning using a good mix of English and Italian data:
Supervised fine-tuning using a good mix of English and Italian data:
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- 
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https://huggingface.co/datasets/mlabonne/FineTome-100k
dataset
mlabonne/FineTome-100k
https://huggingface.co/datasets/mlabonne/FineTome-100k
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@mlabonne
mlabonne
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https://huggingface.co/datasets/efederici/capybara-claude-15k-ita
dataset
efederici/capybara-claude-15k-ita
https://huggingface.co/datasets/efederici/capybara-claude-15k-ita
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 by 
 by 
mention
@efederici
efederici
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🙏 Thanks to the authors for the datasets.
🙏 Thanks to the authors for the datasets.
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🎯 Targeted training with Spectrum
🎯 Targeted training with Spectrum
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I used Spectrum, a relatively new technique for parameter-efficient learning.
I used Spectrum, a relatively new technique for parameter-efficient learning.
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The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest.
The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest.
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I trained the top 30% of model layers.
I trained the top 30% of model layers.
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📝 Spectrum paper: 
📝 Spectrum paper: 
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https://arxiv.org/abs/2406.06623
https://arxiv.org/abs/2406.06623
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📊 Vibe check and performance on Italian benchmarks seem encouraging
📊 Vibe check and performance on Italian benchmarks seem encouraging
💬 🇮🇹 Phi 3.5 mini ITA: a Small Language Model for Italian

Lately, I've spent some time fine-tuning language models.

Now I am happy to release Phi 3.5 mini ITA: a fine-tuned version of Phi-3.5-mini-instruct to improve performance on the Italian language

🔹 Small (3.82 B parameters) but capable model
🔹 128k context length

Chat with it on 🤗 Spaces: https://huggingface.co/spaces/anakin87/Phi-3.5-mini-ITA
Model card: https://huggingface.co/anakin87/Phi-3.5-mini-ITA

🗃️ Data
Supervised fine-tuning using a good mix of English and Italian data:
- https://huggingface.co/datasets/mlabonne/FineTome-100k by @mlabonne 
- https://huggingface.co/datasets/efederici/capybara-claude-15k-ita by @efederici 
🙏 Thanks to the authors for the datasets.


🎯 Targeted training with Spectrum
I used Spectrum, a relatively new technique for parameter-efficient learning.
The idea is to train only the layers of the model with high Signal-to-Noise Ratio (SNR) and ❄️ freeze the rest.
I trained the top 30% of model layers.

📝 Spectrum paper: https://arxiv.org/abs/2406.06623


📊 Vibe check and performance on Italian benchmarks seem encouraging
👍
mlabonne John6666 osanseviero victor merve kristaller486
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lukecage 4rtemi5
https://cdn-avatars.huggingface.co/v1/production/uploads/626505d493e0b04d75710566/9rfJc9ORXU9J5a42Ev3v6.png
Stefano Fiorucci
anakin87
user
https://cdn-avatars.huggingface.co/v1/production/uploads/61b8e2ba285851687028d395/JtUGAwVh_4cDEsjNcfpye.png
Maxime Labonne
mlabonne
user