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- use f-strings where possible
- drop first param of encode/decode functions since "utf-8" is the default
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If you deleted your old Meta LLaMA .pth files, then the
migrate-ggml-2023-03-30-pr613.py script will allow you to convert your
old ggml files into the new mmap()'able format.
See #613
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This is a breaking change that's going to give you three benefits:
1. Your inference commands should load 100x faster
2. You may be able to safely load models 2x larger
3. You can run many concurrent inference processes
This was accomplished by changing the file format so we can mmap()
weights directly into memory without having to read() or copy them
thereby ensuring the kernel can make its file cache pages directly
accessible to our inference processes; and secondly, that the file
cache pages are much less likely to get evicted (which would force
loads to hit disk) because they're no longer competing with memory
pages that were needlessly created by gigabytes of standard i/o.
The new file format supports single-file models like LLaMA 7b, and
it also supports multi-file models like LLaMA 13B. Our Python tool
now merges the foo.1, foo.2, etc. files back into a single file so
that the C++ code which maps it doesn't need to reshape data every
time. That's made llama.cpp so much simpler. Much of its load code
has now been deleted.
Furthermore, this change ensures that tensors are aligned properly
on a 32-byte boundary. That opens the door to seeing if we can get
additional performance gains on some microprocessors, by using ops
that require memory alignment.
Lastly note that both POSIX and the Windows platform are supported
Fixes #91
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correctly with `vocab_only` setting. Also confirmed that the code works as expected after running with reduced memory usage due to deletion of no-longer-needed variable. (#547)
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"Processing part 1 of 3" instead of "Processing part 0"
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* Major refactoring - introduce C-style API
* Clean up
* Add <cassert>
* Add <iterator>
* Add <algorithm> ....
* Fix timing reporting and accumulation
* Measure eval time only for single-token calls
* Change llama_tokenize return meaning
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* Add test-tokenizer-0 to do a few tokenizations - feel free to expand
* Added option to convert-pth-to-ggml.py script to dump just the vocabulary
* Added ./models/ggml-vocab.bin containing just LLaMA vocab data (used for tests)
* Added utility to load vocabulary file from previous point (temporary implementation)
* Avoid using std::string_view and drop back to C++11 (hope I didn't break something)
* Rename gpt_vocab -> llama_vocab
* All CMake binaries go into ./bin/ now
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* potential out of bounds read
* fix quantize
* style
* Update convert-pth-to-ggml.py
* mild cleanup
* don't need the space-prefixing here rn since main.cpp already does it
* new file magic + version header field
* readme notice
* missing newlines
Co-authored-by: slaren <2141330+slaren@users.noreply.github.com>
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* Refactor get_n_parts function to simplify code and improve readability
* Use f-strings instead of concatenation
* Refactoring: more concise and readable
* modularize
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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* feat: dockerize llamacpp
* feat: split build & runtime stages
* split dockerfile into main & tools
* add quantize into tool docker image
* Update .devops/tools.sh
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* add docker action pipeline
* change CI to publish at github docker registry
* fix name runs-on macOS-latest is macos-latest (lowercase)
* include docker versioned images
* fix github action docker
* fix docker.yml
* feat: include all-in-one command tool & update readme.md
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Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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convert-pth-to-ggml.py (#142)
There are ways that special tokens or other new tokens could be added to the tokenizer; therefore it's probably best not to assume the vocabulary is only 32000 tokens.
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this restricts malicious weights from executing arbitrary code by restricting the unpickler to only loading tensors, primitive types, and dictionaries
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