model-conversion : add embedding prompt file support #15871
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This commit adds support for passing a prompt file to the model conversion targets/scripts. It also updates the logits.cpp to print out embedding information in the same format as when running the original embedding model. The motivation for this is that it allows us to pass files of different sizes when running the converted models and validating the logits. This can be particularly important when testing the sliding window functionality of models where the sequence length needs to exceed a certain number of tokens to trigger the sliding window logic.
ggerganov
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Sep 25, 2025
pwilkin
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Sep 25, 2025
This commit adds support for passing a prompt file to the model conversion targets/scripts. It also updates the logits.cpp to print out embedding information in the same format as when running the original embedding model. The motivation for this is that it allows us to pass files of different sizes when running the converted models and validating the logits. This can be particularly important when testing the sliding window functionality of models where the sequence length needs to exceed a certain number of tokens to trigger the sliding window logic.
struct
pushed a commit
to struct/llama.cpp
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Sep 26, 2025
This commit adds support for passing a prompt file to the model conversion targets/scripts. It also updates the logits.cpp to print out embedding information in the same format as when running the original embedding model. The motivation for this is that it allows us to pass files of different sizes when running the converted models and validating the logits. This can be particularly important when testing the sliding window functionality of models where the sequence length needs to exceed a certain number of tokens to trigger the sliding window logic.
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This commit adds support for passing a prompt file to the model conversion targets/scripts. It also updates the logits.cpp to print out embedding information in the same format as when running the original embedding model.
The motivation for this is that it allows us to pass files of different sizes when running the converted models and validating the logits.
This can be particularly important when testing the sliding window functionality of models where the sequence length needs to exceed a certain number of tokens to trigger the sliding window logic.