* llamamodel: only print device used in verbose mode
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
* python: expose backend and device via GPT4All properties
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
* backend: const correctness fixes
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
* python: bump version
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
* python: typing fixups
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
* python: fix segfault with closed GPT4All
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
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Signed-off-by: Jared Van Bortel <jared@nomic.ai>
Also dynamically limit the GPU layers and context length fields to the maximum supported by the model.
Signed-off-by: Jared Van Bortel <jared@nomic.ai>
most of these can just shortcut out of the model loading logic llama is a bit worse to deal with because we submodule it so I have to at least parse the hparams, and then I just use the size on disk as an estimate for the mem size (which seems reasonable since we mmap() the llama files anyway)
fixes a definite use-after-free and likely avoids some other
potential ones - std::string will convert to a std::string_view
automatically but as soon as the std::string in question goes out of
scope it is already freed and the string_view is pointing at freed
memory - this is *mostly* fine if its returning a reference to the
tokenizer's internal vocab table but it's, imo, too easy to return a
reference to a dynamically constructed string with this as replit is
doing (and unfortunately needs to do to convert the internal whitespace
replacement symbol back to a space)
Major change to the backend that allows for pluggable versions of llama.cpp/ggml. This was squashed merged from dlopen_backend_5 where the history is preserved.
