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@brandonwillard and I were looking into the LlamaCppTokenizer and noticed a number of issues:
LlamaCppTokenizer
__getstate__
TransformerTokenizer
Tokenizer
LlamaCppTokenizer.__init__
special_tokens
vLLM
transformers
adapt_tokenizer
llamacpp
__hash__
_stablehash(serialized)
A lot of minor changes here. Please let me know if I'm missing something or if I've accidentally excluded something.
outlines.caching
_stablehash
models
Some of the work to fix this can be resurrected from #676
On hold until ExLlamaV2 integration is complete (#807)
ExLlamaV2
The text was updated successfully, but these errors were encountered:
outlines.generate
CFGGuide
outlines.fsm.parsing
generate.cfg
No branches or pull requests
What behavior of the library made you think about the improvement?
@brandonwillard and I were looking into the
LlamaCppTokenizer
and noticed a number of issues:__getstate__
is used to serialize for hashing.LlamaCppTokenizer
andTransformerTokenizer
are subclasses of outlinesTokenizer
, but vLLM is notLlamaCppTokenizer.__init__
doesn't loadspecial_tokens
vLLM
andtransformers
tokenizers useadapt_tokenizer
, butllamacpp
doesn't.__hash__
and_stablehash(serialized)
are calculated once per call rather than caching their hash value.How would you like it to behave?
A lot of minor changes here. Please let me know if I'm missing something or if I've accidentally excluded something.
__getstate__
is a fallback foroutlines.caching
, and by default we implement_stablehash
vLLM
becomes an outlinesTokenizer
and uses the standard interfaces.Tokenizer
mutation is disabledadapt_tokenizer
is removed. Allmodels
pass themselves to their respectiveTokenizer
to be constructed._stablehash
and__hash__
are only calculated once.Some of the work to fix this can be resurrected from #676
Status
On hold until
ExLlamaV2
integration is complete (#807)The text was updated successfully, but these errors were encountered: