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README.md
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We release under the Apache 2.0 license 2 checkpoints:
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- [idefics2-8b-base](https://huggingface.co/HuggingFaceM4/idefics2-8b-base): the base model
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- [idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b): the base model fine-tuned on a mixture of supervised and instruction datasets (text-only and multimodal datasets)
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# Model Summary
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For optimal results, we recommend fine-tuning `idefics2-8b` on one's specific use-case and data. In fact, the instruction-fine-tuned model (`idefics2-8b`) is significantly better at following instructions from users and thus should be preferred when using the models out-of-the-box or as a starting point for fine-tuning.
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As a starting point, we provide fine-tuning codes that can be adapted for one's particular scenario:
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- With the [TRL library](https://github.com/huggingface/trl): [Script](https://gist.github.com/edbeeching/228652fc6c2b29a1641be5a5778223cb)
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- With the [Hugging Face Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#api-reference%20][%20transformers.Trainer): [Tutorial notebook](https://colab.research.google.com/drive/1NtcTgRbSBKN7pYD3Vdx1j9m8pt3fhFDB?usp=sharing)
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We release under the Apache 2.0 license 2 checkpoints:
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- [idefics2-8b-base](https://huggingface.co/HuggingFaceM4/idefics2-8b-base): the base model
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- [idefics2-8b](https://huggingface.co/HuggingFaceM4/idefics2-8b): the base model fine-tuned on a mixture of supervised and instruction datasets (text-only and multimodal datasets)
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- idefics2-8b-chatty (coming soon): `idefics2-8b` further fine-tuned on long conservations
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# Model Summary
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For optimal results, we recommend fine-tuning `idefics2-8b` on one's specific use-case and data. In fact, the instruction-fine-tuned model (`idefics2-8b`) is significantly better at following instructions from users and thus should be preferred when using the models out-of-the-box or as a starting point for fine-tuning.
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`idefics2-8b` usually generates very short answers. For long generations, use `idefics2-8b-chatty`, which was further fine-tuned on long conversations.
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As a starting point, we provide fine-tuning codes that can be adapted for one's particular scenario:
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- With the [TRL library](https://github.com/huggingface/trl): [Script](https://gist.github.com/edbeeching/228652fc6c2b29a1641be5a5778223cb)
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- With the [Hugging Face Trainer](https://huggingface.co/docs/transformers/main/en/main_classes/trainer#api-reference%20][%20transformers.Trainer): [Tutorial notebook](https://colab.research.google.com/drive/1NtcTgRbSBKN7pYD3Vdx1j9m8pt3fhFDB?usp=sharing)
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