* Don't force immediate interactive without -i
Sometimes we might want to use a reverse prompt but we want to let the
model generate tokens right after the initial prompt. So we don't force
user input mode if the -i flag wasn't specified and instead let it run
until we encounter the reverse prompt.
This gives use some more flexibility, since it doesn't force the user to
enter a newline if they want to let the model generate text right after
the initial prompt and only be asked for input if the reverse prompt is
encountered.
The `--interactive-first` flag is reintroduced to force the old
behavior. `-r` behaves like `-i` plus introduces a reverse prompt (it
can be specified more than once).
* Update help output.
---------
Co-authored-by: Johnman <tjohnman@github>
* 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
* Improve performance by changing std::map to std::unordered_map and std::map<id, token> id_to_token; to std::vector<token> id_to_token;
* fix last commit on gpt_vocab_init add vocab.id_to_token.resize(vocab.token_to_id.size());
* Removed include <map>
* Nest struct token score inside gpt_vocab
* renamed token to tok
* Compute perplexity over prompt
* More accurate perplexity calculation - over all logits in the context window (so 512x more tokens!)
* Output all perplexitiies
* Add timing/ETA
* 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
* 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>
* Use F16 for memory_k and memory_v
* add command line switch to use f16 instead of f32 for memory k+v
---------
Co-authored-by: Ty Everett <ty@tyweb.us>
* Implement non-greedy tokenizer that tries to maximize token lengths
* Insert single space in front of the prompt
- this is to match original llama tokenizer behavior
---------
Co-authored-by: Jakub Horak <jakub.horak@ibawizard.net>
The readme tells people to use the command line option "-t 8", causing 8
threads to be started. On systems with fewer than 8 cores, this causes a
significant slowdown. Remove the option from the example command lines
and use /proc/cpuinfo on Linux to determine a sensible default.
* Add AVX2 version of ggml_vec_dot_q4_1
* Small optimisations to q4_1 dot product (@Const-me)
* Rearrange Q4_1 quantization to work for multipart models. (Fix#152)
* Fix ggml_vec_mad_q4_1 too
* Fix non-vectorised q4_1 vec mul
* added ctx_size parameter
* added it in more places
* Apply suggestions from code review
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
* Initial work on interactive mode.
* Improve interactive mode. Make rev. prompt optional.
* Update README to explain interactive mode.
* Fix OS X build
* Apply fixes suggested to build on windows
Issue: https://github.com/ggerganov/llama.cpp/issues/22
* Remove unsupported VLAs
* MSVC: Remove features that are only available on MSVC C++20.
* Fix zero initialization of the other fields.
* Change the use of vector for stack allocations.
* Adding repeat penalization
* Update utils.h
* Update utils.cpp
* Numeric fix
Should probably still scale by temp even if penalized
* Update comments, more proper application
I see that numbers can go negative so a fix from a referenced commit
* Minor formatting
---------
Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
