Take a dict/object of name-value pairs instead of just names.
Inturn specify the actual value wrt default, rather than the
string representing that value.
Trap the needed change event rather than click wrt select.
Also update the config params dump to indicate that now one needs
to use document to get hold of gMe global object, this is bcas of
moving to module type js.
Also add ui.mjs to importmap
Some models like llama3 found to try to be over intelligent by
repeating garbage still, but by tweaking the garbage a bit so that
it is not exactly same. So avoid setting these penalties and let
the model's default behaviour work out, as is.
Also the simple minded histogram based garbage trimming from end,
works to an extent, when the garbage is more predictable and
repeatative.
Allow for more uniq chars, but then ensure that a given type of
char ie numerals or alphabets or other types dont cross the
specified maxType limit. This allows intermixed text garbage
to be identified and trimmed.
Instead of blindly building histogram for specified substring
length, and then checking if any new char within specified min
garbage length limit, NOW exit learn state when specified maxUniq
chars are found. Inturn there should be no new chars with in
the specified min garbage length required limit.
TODO: Need to track char classes like alphabets, numerals and
special/other chars.
Use it to bring in a simple trim garbage at end logic, which is
used to trim received response.
Also given that importmap assumes esm / standard js modules, so
also global variables arent implicitly available outside the
modules. So add it has a member of document for now
* ggml : fix loongson compile warnings
ggml-ci
* Fix loongarch quantize test fail.
Fix unexpected error introduced during rebase code.
* tests : disable json test due to lack of python on the CI node
ggml-ci
---------
Co-authored-by: junchao-loongson <zhaojunchao@loongson.cn>
* llama : cache llama_token_to_piece
ggml-ci
* llama : use vectors and avoid has_cache
ggml-ci
* llama : throw on unknown tokenizer types
ggml-ci
* llama : print a log of the total cache size
* Update random test: add_bos_token.
* Update random test: add WPM models for testing.
* Build vocab.special_tokens_cache using vocab token types.
* Fix and improve WPM preprocessing.
- Fix unicode edge case combinations.
- Split by whitspace in the same pass.
* Discard all tokens when no matching found.
